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Basic Physics Lecture 15
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https://av.tib.eu/media/12926
| null |
https://tib.flowcenter.de/mfc/medialink/3/deb989ea1f591281ebe5b3e2eb64d580f8ec721c1bd5396efd618839606cc1f7d8/Basic_Physics_3A._Lecture_15_flash9.mp4
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2013
|
Physics
|
Lecture
| null |
English
|
10.5446/12926 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Dennin, Michael
| null |
Basic Physics Lecture 14
|
https://av.tib.eu/media/12925
| null |
https://tib.flowcenter.de/mfc/medialink/3/de868cf82594e5078fcbd5f6ca1af4a2795a8b507ca5c0b4bd3db145d756e16587/Basic_Physics_3A._Lecture_14_flash9.mp4
|
2013
|
Physics
|
Lecture
| null |
English
|
10.5446/12925 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Dennin, Michael
| null |
Basic Physics Lecture 13
|
https://av.tib.eu/media/12924
| null |
https://tib.flowcenter.de/mfc/medialink/3/de4c30ad0630a0c95356859ba32bfd3e132db63142dc5d0e69fdcd0d7a7abf2a17/Basic_Physics_3A._Lecture_13_flash9.mp4
|
2013
|
Physics
|
Lecture
| null |
English
|
10.5446/12924 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Dennin, Michael
| null |
Basic Physics Lecture 12
|
https://av.tib.eu/media/12923
| null |
https://tib.flowcenter.de/mfc/medialink/3/de15cee113826742ad58488c86452adc59a0f27b403fe2fbc83261996956e7f11b/Basic_Physics_3A._Lecture_12_flash9.mp4
|
2013
|
Physics
|
Lecture
| null |
English
|
10.5446/12923 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Dennin, Michael
| null |
Basic Physics Lecture 11
|
https://av.tib.eu/media/12922
| null |
https://tib.flowcenter.de/mfc/medialink/3/de7c53ff32711211a5fd64ad981ddcfaee38a3bd16e1faf9bd51467f116bb2d570/Basic_Physics_3A._Lecture_11_flash9.mp4
|
2013
|
Physics
|
Lecture
| null |
English
|
10.5446/12922 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Dennin, Michael
| null |
Basic Physics Lecture 10
|
https://av.tib.eu/media/12921
| null |
https://tib.flowcenter.de/mfc/medialink/3/de1fd03247c4c75b369f725d5fab4c8c04126e54fc89f8d417ef2937abf2dfa282/Basic_Physics_3A._Lecture_10_flash9.mp4
|
2013
|
Physics
|
Lecture
| null |
English
|
10.5446/12921 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Dennin, Michael
| null |
Basic Physics Lecture 9
|
https://av.tib.eu/media/12920
| null |
https://tib.flowcenter.de/mfc/medialink/3/de1b619ed736fe0e1d1fcae9eb536a194453ba0465647b68e6f710c44b1105a1f1/Basic_Physics_3A._Lecture_09_flash9.mp4
|
2013
|
Physics
|
Lecture
| null |
English
|
10.5446/12920 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Dennin, Michael
| null |
Basic Physics Lecture 7
|
https://av.tib.eu/media/12919
| null |
https://tib.flowcenter.de/mfc/medialink/3/de973f2a1410d30931cd9ebd78cefd14f7b8159b29bf8cbbf3f318ae68a9514051/Basic_Physics_3A._Lecture_07._Constant_Acceleration_in_2D_Motion_flash9.mp4
|
2013
|
Physics
|
Lecture
| null |
English
|
10.5446/12919 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Dennin, Michael
| null |
Basic Physics Lecture 8
|
https://av.tib.eu/media/12918
| null |
https://tib.flowcenter.de/mfc/medialink/3/de368d0e6cfe8fc4f7775375977cd3948db8d44b20397ac8ffa21aff35abad0d25/Basic_Physics_3A._Lecture_08_flash9.mp4
|
2013
|
Physics
|
Lecture
| null |
English
|
10.5446/12918 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Dennin, Michael
| null |
Math for Economists - Lecture 5
|
https://av.tib.eu/media/12914
| null |
https://tib.flowcenter.de/mfc/medialink/3/ded9ecb23305403a01da61fbc5d9aeddbd379376cb4fee4a18c36dd8919b636cbc/Math_4_1._Math_for_Economists._Lecture_05_flash9.mp4
|
2013
|
Mathematics
|
Lecture
| null |
English
|
10.5446/12914 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Kronewetter, Jason
| null |
Math for Economists - Lecture 9
|
https://av.tib.eu/media/12913
| null |
https://tib.flowcenter.de/mfc/medialink/3/de065f79dc2cbb6146f5f02b3d8124fff4bc1486e39dc04daa54725a08977d4f33/Math_4_1._Math_for_Economists._Lecture_09_flash9.mp4
|
2013
|
Mathematics
|
Lecture
| null |
English
|
10.5446/12913 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Kronewetter, Jason
| null |
Conditional Probability
|
https://av.tib.eu/media/12911
| null |
https://tib.flowcenter.de/mfc/medialink/3/de086f2b9e12392fe598f15ab792557f42ba6e77f1e01eb497438fd68dfff0c849/Introduction_to_Probability_and_Statistics_131A._Lecture_9._Conditional_Probability_flash9.mp4
|
2013
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Mathematics
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Lecture
| null |
English
|
10.5446/12911 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
New Algorithms in Information Science
|
https://av.tib.eu/media/12910
| null |
https://tib.flowcenter.de/mfc/medialink/3/de3ec6a4afa676e76ec03f6cf7945fbebe8f7f0d0a838cbbde5907d7e83e536a0f/New_Algorithms_in_Information_Science_flash9.mp4
|
2010
|
Mathematics
|
Lecture
| null |
English
|
10.5446/12910 (DOI)
|
CC Attribution - ShareAlike 3.0 USA:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Osher, Stanley
| null |
Math for Economists - Lecture 15
|
https://av.tib.eu/media/12909
| null |
https://tib.flowcenter.de/mfc/medialink/3/de73a409f9e3107092235c30014c5e1689f2c6716f5dc7458d0a78d0064bf5fc4eb4/Math_4._Math_for_Economists._Lecture_15_Final_Review_flash9.mp4
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2013
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Mathematics
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Lecture
| null |
English
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10.5446/12909 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
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|
Kronewetter, Jason
| null |
Math for Economists - Lecture 14
|
https://av.tib.eu/media/12908
| null |
https://tib.flowcenter.de/mfc/medialink/3/de7b44e34138dee03c9f193ed02cb1904bd4efd5c5f5396d7646aa5a3552fc3c2f/Math_4._Math_for_Economists._Lecture_14_flash9.mp4
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2013
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Mathematics
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Lecture
| null |
English
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10.5446/12908 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
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|
Kronewetter, Jason
| null |
Math for Economists - Lecture 13
|
https://av.tib.eu/media/12907
| null |
https://tib.flowcenter.de/mfc/medialink/3/dec8e76ef0245b628bc9f28b3769dbcd523eba148e356521b6bd9b5aa5d2736d80/Math_4._Math_for_Economists._Lecture_13_flash9.mp4
|
2013
|
Mathematics
|
Lecture
| null |
English
|
10.5446/12907 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Kronewetter, Jason
| null |
Math for Economists - Lecture 12
|
https://av.tib.eu/media/12906
| null |
https://tib.flowcenter.de/mfc/medialink/3/de0fb3989ce7c1b4588f94097e9ca37e315ded453a3323ef7089899ea61d534cd7/Math_4._Math_for_Economists._Lecture_12_flash9.mp4
|
2013
|
Mathematics
|
Lecture
| null |
English
|
10.5446/12906 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Kronewetter, Jason
| null |
Math for Economists - Lecture 11
|
https://av.tib.eu/media/12905
| null |
https://tib.flowcenter.de/mfc/medialink/3/de82bc1d5d1d46a5a276e1d97113678cd975eed6d1065ea019b050ad0398df2126/Math_4._Math_for_Economists._Lecture_11_flash9.mp4
|
2013
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Mathematics
|
Lecture
| null |
English
|
10.5446/12905 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Kronewetter, Jason
| null |
Math for Economists - Lecture 10
|
https://av.tib.eu/media/12904
| null |
https://tib.flowcenter.de/mfc/medialink/3/de82558c2ef37d4f17f9473dee7662ff670dd767eea0ec1d7904b00aacbb3885e6/Math_4._Math_for_Economists._Lecture_10_flash9.mp4
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2013
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Mathematics
|
Lecture
| null |
English
|
10.5446/12904 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Kronewetter, Jason
| null |
Math for Economists - Lecture 8
|
https://av.tib.eu/media/12902
| null |
https://tib.flowcenter.de/mfc/medialink/3/dedc3485b13467a3acb6e323b8fc93fff06568c40fb9a6451a29febdee3a6b351e/Math_4._Math_for_Economists._Lecture_08_flash9.mp4
|
2013
|
Mathematics
|
Lecture
| null |
English
|
10.5446/12902 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
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|
Kronewetter, Jason
| null |
Math for Economists - Lecture 7
|
https://av.tib.eu/media/12901
| null |
https://tib.flowcenter.de/mfc/medialink/3/de13c2ed23974a4cc4ee420ac562289e757abf7ae6cbb1093e4db91daa8c665fe9/Math_4._Math_for_Economists._Lecture_07_flash9.mp4
|
2013
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Mathematics
|
Lecture
| null |
English
|
10.5446/12901 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Kronewetter, Jason
| null |
Math for Economists - Lecture 6
|
https://av.tib.eu/media/12900
| null |
https://tib.flowcenter.de/mfc/medialink/3/def6090c92a1185e4a7220ba28d2e14d7345858660bd6f88bda40e96bd7112480b/Math_4_1._Math_for_Economists._Lecture_06_flash9.mp4
|
2013
|
Mathematics
|
Lecture
| null |
English
|
10.5446/12900 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Kronewetter, Jason
| null |
Math for Economists - Lecture 4
|
https://av.tib.eu/media/12898
| null |
https://tib.flowcenter.de/mfc/medialink/3/de48814298786b6d8e3cb7997a9c177745b791a8578c81f8663bf4988f0962370e/Math_4._Math_for_Economists._Lecture_04_flash9.mp4
|
2013
|
Mathematics
|
Lecture
| null |
English
|
10.5446/12898 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Kronewetter, Jason
| null |
Math for Economists - Lecture 3
|
https://av.tib.eu/media/12897
| null |
https://tib.flowcenter.de/mfc/medialink/3/dee3a65092536dce6574e0113419d03585f08542bd342d815e6f2b3d7e03bf2e3a/Math_4._Math_for_Economists._Lecture_03_flash9.mp4
|
2013
|
Mathematics
|
Lecture
| null |
English
|
10.5446/12897 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Kronewetter, Jason
| null |
Math for Economists - Lecture 2
|
https://av.tib.eu/media/12896
| null |
https://tib.flowcenter.de/mfc/medialink/3/de2d1a0e3e840a8f55b7be24ac179f027a669f91659e6909c1469ca109ac693e50/Math_4._Math_for_Economists._Lecture_02_flash9.mp4
|
2013
|
Mathematics
|
Lecture
| null |
English
|
10.5446/12896 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Kronewetter, Jason
| null |
Math for Economists - Lecture 1
|
https://av.tib.eu/media/12895
| null |
https://tib.flowcenter.de/mfc/medialink/3/defcd1d4d3d01886a9a166de5f651b49aa763f8452297cdfa5f70d0a2974e3fb41/Math_4._Math_for_Economists._Lecture_01._Introduction_to_the_Course_flash9.mp4
|
2013
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Mathematics
|
Lecture
| null |
English
|
10.5446/12895 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Kronewetter, Jason
| null |
Distributions from normal Distribution
|
https://av.tib.eu/media/12893
| null |
https://tib.flowcenter.de/mfc/medialink/3/de6f0febf68d22f0b326c9459b3c98f7020ccc46f6548a60ab0686b62b36afa526/Introduction_to_Probability_and_Statistics_131A._Lecture_8_Distributions_from_normal_Distribution_fl.mp4
|
2013
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Mathematics
|
Lecture
| null |
English
|
10.5446/12893 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Limit Theorems
|
https://av.tib.eu/media/12892
| null |
https://tib.flowcenter.de/mfc/medialink/3/de2be05c5c24cc8bd31ac6a648511ee21fc78e15bc2f9f53b92cdf7998c590e24f76/Introduction_to_Probability_and_Statistics_131A._Lecture_7._Limit_Theorems_flash9.mp4
|
2013
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Mathematics
|
Lecture
| null |
English
|
10.5446/12892 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Joint Distribution (2)
|
https://av.tib.eu/media/12891
| null |
https://tib.flowcenter.de/mfc/medialink/3/deb4ae5f5671a1b33b886d5abb681a3b6ece23c7938fe73530900e627595fbbd09/Introduction_to_Probability_and_Statistics_131A._Lecture_6._Joint_Distribution_flash9.mp4
|
2013
|
Mathematics
|
Lecture
| null |
English
|
10.5446/12891 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Expected Values
|
https://av.tib.eu/media/12890
| null |
https://tib.flowcenter.de/mfc/medialink/3/de0e68de9485c1a1eb6e4c01541e443cc8507dcb7ea6e59e6678fade359c943d73/Introduction_to_Probability_and_Statistics_131A._Lecture_5._Expected_Values_flash9.mp4
|
2013
|
Mathematics
|
Lecture
| null |
English
|
10.5446/12890 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Joint Distribution (1)
|
https://av.tib.eu/media/12889
| null |
https://tib.flowcenter.de/mfc/medialink/3/dee746d0a231d30cafc49c7b850694abc78d41acba77af8f495c5c2b1d73f8ac77e0/Introduction_to_Probability_and_Statistics_131A._Lecture_4._Joint_Distribution_flash9.mp4
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2013
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Mathematics
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Lecture
| null |
English
|
10.5446/12889 (DOI)
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CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Random Variables
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https://av.tib.eu/media/12888
| null |
https://tib.flowcenter.de/mfc/medialink/3/de5a123c34059fc35c5c9f7ec9dda8bf2477ec8cfe9f721d6056ac3740f3be26ce73/Introduction_to_Probability_and_Statistics_131A._Lecture_3._Random_Variables_flash9.mp4
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2013
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Mathematics
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Lecture
| null |
English
|
10.5446/12888 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Probability (2)
|
https://av.tib.eu/media/12887
| null |
https://tib.flowcenter.de/mfc/medialink/3/ded05ee9747eb0bcf9f6f7c5d08f80274effc643ee2dceef24703e4ea73d4067dd/Introduction_to_Probability_and_Statistics_131A._Lecture_2._Probability_flash9.mp4
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2013
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Mathematics
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Lecture
| null |
English
|
10.5446/12887 (DOI)
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CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Final Review
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https://av.tib.eu/media/12886
| null |
https://tib.flowcenter.de/mfc/medialink/3/de68e74fae9802d1f1c2e0a1217fb4c8d2f069b011fa68635e23d1a039f7ad6bdf/Introduction_to_Probability_and_Statistics_131A._Lecture_16._Final_Review_flash9.mp4
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2013
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Mathematics
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Lecture
| null |
English
|
10.5446/12886 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Simple Random Sampling
|
https://av.tib.eu/media/12885
| null |
https://tib.flowcenter.de/mfc/medialink/3/def9b5ba635a647e69274bb6272520080955f3e6a6c342f3a47d0c91bd5336d1a4/Introduction_to_Probability_and_Statistics_131A._Lecture_15._Simple_Random_Sampling_flash9.mp4
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2013
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Mathematics
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Lecture
| null |
English
|
10.5446/12885 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Random Sampling
|
https://av.tib.eu/media/12884
| null |
https://tib.flowcenter.de/mfc/medialink/3/de7e4141edb393b5700d27733c310624e38697c34f3017e53c157164e61402e00b/Introduction_to_Probability_and_Statistics_131A._Lecture_14._Random_Sampling_flash9.mp4
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2013
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Mathematics
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Lecture
| null |
English
|
10.5446/12884 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Hypothesis Testing
|
https://av.tib.eu/media/12883
| null |
https://tib.flowcenter.de/mfc/medialink/3/de861ee2e2ad9d353fa52ab5649ca5dcb0ebae4baaf31ae8036a148ee4be80a10d/Introduction_to_Probability_and_Statistics_131A_1._Lecture_13._Hypothesis_Testing_flash9.mp4
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2013
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Mathematics
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Lecture
| null |
English
|
10.5446/12883 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Fitting of Probability Distributions
|
https://av.tib.eu/media/12882
| null |
https://tib.flowcenter.de/mfc/medialink/3/def44798bf44cc7c3cc6a424e362ae6adf68d60b2da3a66518c636bb0cb86f13f1/Introduction_to_Probability_and_Statistics_131A._Lecture_12._Fitting_of_Probability_Distributions_fl.mp4
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2013
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Mathematics
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Lecture
| null |
English
|
10.5446/12882 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Estimation of Parameters
|
https://av.tib.eu/media/12881
| null |
https://tib.flowcenter.de/mfc/medialink/3/de92e8427e810e24e821dee13e398edaaaf973b0f2cb597fd2f95a99619b063fa8/Introduction_to_Probability_and_Statistics_131A._Lecture_11._Estimation_of_Parameters_flash9.mp4
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2013
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Mathematics
|
Lecture
| null |
English
|
10.5446/12881 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Probability (1)
|
https://av.tib.eu/media/12880
| null |
https://tib.flowcenter.de/mfc/medialink/3/ded3eeb45c5ce7fd9d3a3e978929896a5c30b470a5ddf7e39b2a25a435ad4798ccef93/Introduction_to_Probability_and_Statistics_131A._Lecture_1._Probability_flash9.mp4
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2013
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Mathematics
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Lecture
| null |
English
|
10.5446/12880 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Survey Sampling
|
https://av.tib.eu/media/12879
| null |
https://tib.flowcenter.de/mfc/medialink/3/de167fd01a7eb6a4cca08918b97d9dc3c857cb0568a2e2ade6f79c22e63d6b9ca3/Introduction_to_Probability_and_Statistics_131A._Lecture_10._Survey_Sampling_flash9.mp4
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2013
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Mathematics
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Lecture
| null |
English
|
10.5446/12879 (DOI)
|
CC Attribution - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
|
Cranston, Michael C.
| null |
Eefke Smit, STM Association at the DataCite summer meeting 2012
|
https://av.tib.eu/media/10501
| null |
https://tib.flowcenter.de/mfc/medialink/3/de84991453a648ded6c944246f6fb5d55ea5b5c0fb8ca8d85b6cbc1061278a3d36/Eefke_20Smit_2C_20STM_20Association_1__flash9.mp4
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2012
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Computer Science
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Conference/Talk
| null |
English
|
10.5446/10501 (DOI)
|
CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Smit, Eefke
| null |
Matthew Woollard, UK Data Archive at the DataCite summer meeting 2012
|
https://av.tib.eu/media/8392
| null |
https://tib.flowcenter.de/mfc/medialink/3/deaa026c90c4cdc06ce39c1bbd020f522b8a1584bc79d501f52b174c022df42750/Matthew_Woolard_UK_Data_Archive_at_the_DataCite_summer_meeting_2012_flash9.mp4
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2012
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Computer Science
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Conference/Talk
| null |
English
|
10.5446/8392 (DOI)
|
CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Woollard, Matthew
| null |
Jonathan Grant, RAND Europe at the DataCite summer meeting 2012
|
https://av.tib.eu/media/8391
| null |
https://tib.flowcenter.de/mfc/medialink/3/decd3fb33aa770e9ad52319501e2e1416ce4af1308aa66dbd368e63bcf17c5d65f/Jonathan_Grant_RAND_Europe_at_the_DataCite_summer_meeting_2012_flash9.mp4
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2012
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Computer Science
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Conference/Talk
| null |
English
|
10.5446/8391 (DOI)
|
CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Grant, Jonathan
| null |
Andrew Treloar, ANDS at DataCite summer meeting 2012
|
https://av.tib.eu/media/6571
| null |
https://tib.flowcenter.de/mfc/medialink/3/de8788d0e8d190a79b1aa5d2a174b7283d63803086459fe615f79e91cb3e615f8a/Andrew_20Treloar_2C_20ANDS_20at_20DataCite_20summer_20meeting_202012_1_flash9.mp4
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2012
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Computer Science
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Conference/Talk
| null |
English
|
10.5446/6571 (DOI)
|
CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Treloar, Andrew
| null |
Adam Farquhar, President of DataCite, British Library, DataCite summer meeting 2012
|
https://av.tib.eu/media/6569
| null |
https://tib.flowcenter.de/mfc/medialink/3/de63c977e5e642e3a20b1026a65c30980f604250aef8881da57568fc4d0c3ff92e50/Adam_20Farquhar_2C_20President_20of_20DataCite_2C_20British_20Library_2C_20DataCite_20summer_20meeti.mp4
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2012
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Computer Science
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Conference/Talk
| null |
English
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10.5446/6569 (DOI)
|
CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Farquhar, Adam
| null |
Jean-François Perrin, Institute Laue Langevin at DataCite summer meeting 2012
|
https://av.tib.eu/media/6567
| null |
https://tib.flowcenter.de/mfc/medialink/3/decea5c0ef296828ad8ae092ff7d75ff2a35db12df068dd649cbefde842f0bdbc7/Jean-Francois_20Perrin_2C_20Institute_20Laue_20Langevin_20at_20DataCite_20summer_20meeting_202012_fl.mp4
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2012
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Computer Science
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Conference/Talk
| null |
English
|
10.5446/6567 (DOI)
|
CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Perrin, Jean-François
| null |
Michael Wilson, STFC at the DataCite summer meeting 2012
|
https://av.tib.eu/media/6566
| null |
https://tib.flowcenter.de/mfc/medialink/3/dedb7dedb844f36a7e4575a4bb41c1a1c07a5488648f41cea5c5e368698b5d012e/Michael_20Wilson_2C_20STFC_20at_20the_20DataCite_20summer_20meeting_202012_flash9.mp4
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2012
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Computer Science
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Conference/Talk
| null |
English
|
10.5446/6566 (DOI)
|
CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Wilson, Michael
| null |
Scott Edmonds, Giga Science, BGI Shenzhen at DataCite summer meeting 2012
|
https://av.tib.eu/media/6565
| null |
https://tib.flowcenter.de/mfc/medialink/3/de93650f24ff4184d6beb4cd64852b048469219faaf05e3bace0f42159cf2a6358/Scott_20Edmonds_2C_20Giga_20Science_2C_20BGI_20Shenzhen_20at_20DataCite_20summer_20meeting_202012_fl.mp4
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2012
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Computer Science
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Conference/Talk
| null |
English
|
10.5446/6565 (DOI)
| null |
Edmonds, Scott
| null |
Susanna Sansone, University of Oxford, ISA at the DataCite summer meeting 2012
|
https://av.tib.eu/media/6564
| null |
https://tib.flowcenter.de/mfc/medialink/3/de22883fb581af3081df5d1bf7c2aac6d4a6f44a3820c8a7b1652853040a9c7002/Susanna_20Sansone_2C_20University_20of_20Oxford_2C_20ISA_20at_20the_20DataCite_20summer_20meeting_20.mp4
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2012
|
Computer Science
|
Conference/Talk
| null |
English
|
10.5446/6564 (DOI)
|
CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Sansone, Susanne
| null |
Vishwas Chavan, GBIF at DataCite summer meeting 2012
|
https://av.tib.eu/media/6563
| null |
https://tib.flowcenter.de/mfc/medialink/3/de9c8bc5d04b12f47dc1e561088a927390397b8e7dfca6f2f487be60cb4dcfee68/Vishwas_20Chavan_2C_20GBIF_20at_20DataCite_20summer_20meeting_202012_flash9.mp4
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2012
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Computer Science
|
Conference/Talk
| null |
English
|
10.5446/6563 (DOI)
|
CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Chavan, Vishwas
| null |
Indexing (27.4.2011)
|
https://av.tib.eu/media/362
| null |
https://tib.flowcenter.de/mfc/medialink/3/dea6dc17038ec3b24053c4daed0c45518e9da9dec594a23d9b7ddb74d5665e88b8/irws-ss11-v4_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
|
English
|
10.5446/362 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Selke, Joachim
|
Miscellaneous (13.7.2011)
|
https://av.tib.eu/media/361
| null |
https://tib.flowcenter.de/mfc/medialink/3/deacc41d294ac4b4102d1bf9f7b23fd574108c14c9eaae34a6c904998f84517b4d/irws-ss11-v13_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
|
English
|
10.5446/361 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Selke, Joachim
|
Document clustering (25.5.2011)
|
https://av.tib.eu/media/360
| null |
https://tib.flowcenter.de/mfc/medialink/3/decc776c363c655a27fd4f3d5dc3b23c2fb6180973fcc85c30519766d22f8aa55a1b/irws-ss11-v7_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
|
English
|
10.5446/360 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Selke, Joachim
|
Language models, Retrieval evaluation (18.5.2011)
|
https://av.tib.eu/media/359
| null |
https://tib.flowcenter.de/mfc/medialink/3/de4fe9b126bed687e52677353123de53bd9404fc719033f15f75aa83a8dc0eb303/irws-ss11-v6_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
|
English
|
10.5446/359 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Selke, Joachim
|
Introduction to Web retrieval (22.6.2011)
|
https://av.tib.eu/media/358
| null |
https://tib.flowcenter.de/mfc/medialink/3/de1793128914c29737453d8a235320a3b0a7185fda90774ebcc16ca1af2ab433d4/irws-ss11-v10_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
|
English
|
10.5446/358 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Selke, Joachim
|
Support vector machines (8.6.2011)
|
https://av.tib.eu/media/357
| null |
https://tib.flowcenter.de/mfc/medialink/3/de1e4e623241cceace5353d0fa315ed8789020014665799b186e53cf171a963492/irws-ss11-v9_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
|
English
|
10.5446/357 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Selke, Joachim
|
Introduction and fundamental notions (6.4.2011)
|
https://av.tib.eu/media/356
| null |
https://tib.flowcenter.de/mfc/medialink/3/de46e249fe67517674c0601fa5114faa4c0ee6806274c71b693f19806cd64c13f5/irws-ss11-v1_flash9_1.mp4
|
2011
|
Computer Science
|
Lecture
|
This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
|
English
|
10.5446/356 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Selke, Joachim
|
Web crawling (29.6.2011)
|
https://av.tib.eu/media/355
| null |
https://tib.flowcenter.de/mfc/medialink/3/de765511d273de7a4fe2a6c06055e894095a5df0034313030a7e09618b337eac72/irws-ss11-v11_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
|
English
|
10.5446/355 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Selke, Joachim
|
Link analysis (6.7.2011)
|
https://av.tib.eu/media/354
| null |
https://tib.flowcenter.de/mfc/medialink/3/deb794c508c834331acc085c5c3e97169066db0a83e0c4e3b08ab99dd1534133ed7d/irws-ss11-v12_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
|
English
|
10.5446/354 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Selke, Joachim
|
Fuzzy retrieval model, Coordination level matching, Vector space retrieval model (13.4.2011)
|
https://av.tib.eu/media/353
| null |
https://tib.flowcenter.de/mfc/medialink/3/de7f5c30b27c40b1d124b64e0901dd323a720f78aed59b9bf44332a10f5ee09dbc2e/irws-ss11-v2_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
|
English
|
10.5446/353 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Selke, Joachim
|
Latent Semantic Indexing (11.5.2011)
|
https://av.tib.eu/media/352
| null |
https://tib.flowcenter.de/mfc/medialink/3/de2e708d907d6a43d34cd17a6bcb16822c83beea790a59da4876f8973e2fd9ba0e/irws-ss11-v5_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
|
English
|
10.5446/352 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Selke, Joachim
|
Relevance feedback, Classification (1.6.2011)
|
https://av.tib.eu/media/351
| null |
https://tib.flowcenter.de/mfc/medialink/3/def6cef8d01d57e9728ef893f25717df86ac1a01042f86a1485c304ce5f1c730f9f7/irws-ss11-v8_flash9_1.mp4
|
2011
|
Computer Science
|
Lecture
|
This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
|
English
|
10.5446/351 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Selke, Joachim
|
Probabilistic retrieval models (20.4.2011)
|
https://av.tib.eu/media/350
| null |
https://tib.flowcenter.de/mfc/medialink/3/de1df6919ebe30f0af9d9890c5be3c453e728fb90ca69a7014e4720ed6a1651e23/irws-ss11-v3_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
|
English
|
10.5446/350 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Selke, Joachim
|
Shot Detection (23.06.2011)
|
https://av.tib.eu/media/349
| null |
https://tib.flowcenter.de/mfc/medialink/3/dec739d11c781aa7e544277d7ea24701059f8feec69606c91d306932f63ddc47e7/mmdb-ws1011-v10_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/349 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Introduction in Audio Retrieval 2 (12.05.2011)
|
https://av.tib.eu/media/348
| null |
https://tib.flowcenter.de/mfc/medialink/3/defe379d037dbe43f81ebfaea075f9da7a40e712e87678d49c753575be385d6dd2/mmdb-ws1011-v6b_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/348 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Basic concepts, Evaluation procedures (07.04.11)
|
https://av.tib.eu/media/347
| null |
https://tib.flowcenter.de/mfc/medialink/3/decd5c30e9dbe7b2895d20036db770363c2b2513431fb7b53b05061df996fec1eda2/mmdb-ws1011-v1_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/347 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Query by Humming, Melody Representation, Hidden Markov Model (26.05.11)
|
https://av.tib.eu/media/346
| null |
https://tib.flowcenter.de/mfc/medialink/3/de9e7de1bb23e709bdf8c87a079185d5a85635f907f383e6e79e798cf31c43acfb/mmdb-ws1011-v8_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/346 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Video Similarity (30.06.2011)
|
https://av.tib.eu/media/345
| null |
https://tib.flowcenter.de/mfc/medialink/3/dee621a56230e3310d2fd9517c02c92cbe7e7b37951a8c6dd05c5c3691f3693b0ddb/mmdb-ws1011-v11_flash9_1.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/345 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Indexes (14.07.2011)
|
https://av.tib.eu/media/344
| null |
https://tib.flowcenter.de/mfc/medialink/3/de9ea189d5df41743bc76439996e3c1baec7d6a63f8fc1f25242ce1e076baac1b0/mmdb-ws1011-v13_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/344 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Multiresolution Analysis, Form based Features, Thresholding, Edge Detection, Morphological Operators (28.04.2011)
|
https://av.tib.eu/media/343
| null |
https://tib.flowcenter.de/mfc/medialink/3/de2f8819e14c602cfced4ac97dc9c5b8b5606d760cd784b7e751ce8a3b99a514e9/mmdb-ws1011-v4_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/343 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Audio Low level Features, Difference Limen, Pitch Recognition (19.05.2011)
|
https://av.tib.eu/media/342
| null |
https://tib.flowcenter.de/mfc/medialink/3/decf8a4627037ddb1c6e7889a2799f330366573c3ec395314e9f4143e2babb7d04/mmdb-ws1011-v7_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/342 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Features introduction, Color features and color histograms, Matching of color histograms (14.04.2011)
|
https://av.tib.eu/media/341
| null |
https://tib.flowcenter.de/mfc/medialink/3/de0c6087d96ff4bfd94d1ab36a084894fc72fef733dc1bbc85b2577de71e609069/mmdb-ws1011-v2_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/341 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Hidden Markov Model, Video Retrieval (09.06.11)
|
https://av.tib.eu/media/340
| null |
https://tib.flowcenter.de/mfc/medialink/3/ded296081723a775726545d745ae94492588c272953689631044ff607fff251444/mmdb-ws1011-v9_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/340 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Texture Features, Low-Level Texture Features, Tamura Measure, Random Field Models, Transform Domain Features (21.04.2011)
|
https://av.tib.eu/media/339
| null |
https://tib.flowcenter.de/mfc/medialink/3/de9fda9302e3141f3d65872e79beb993fd2b05b88f277c6ec8c3e2013abb2d6455/mmdb-ws1011-v3_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/339 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Video Abstraction (07.07.2011)
|
https://av.tib.eu/media/338
| null |
https://tib.flowcenter.de/mfc/medialink/3/debe77986b5f3826786e5a9695b45bf3a5acd70663a395d340de827e97f7dd44cd/mmdb-ws1011-v12_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/338 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Introduction in Audio Retrieval 1 (12.05.2011)
|
https://av.tib.eu/media/337
| null |
https://tib.flowcenter.de/mfc/medialink/3/de748516e16ec79183d94b42f6a5d574d33bda0475550851612bb25c30b3465c3012c2/mmdb-ws1011-v6a_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/337 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Chain Codes, Area based Retrieva, Moment Invariants, Query by Visual example (05.05.2011)
|
https://av.tib.eu/media/336
| null |
https://tib.flowcenter.de/mfc/medialink/3/decb5ede13dc45392e0e9c827173f5a04594fd44846b6a1034586daace185d385c30/mmdb-ws1011-v5_flash9.mp4
|
2011
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
|
English
|
10.5446/336 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Clustering (20.01.2011)
|
https://av.tib.eu/media/335
| null |
https://tib.flowcenter.de/mfc/medialink/3/de42e6ec8307303e18577fd9771160665215167be6517bd8781176863107a55f1e/dwh-ws1011-v11_flash9.mp4
|
2010
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
|
English
|
10.5446/335 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
DWs in Praxis (03.02.2011)
|
https://av.tib.eu/media/334
| null |
https://tib.flowcenter.de/mfc/medialink/3/dee421f444867d54ace989f8e3efa6c156f47e5a155a01e3239d62fc4f42eeefd2/dwh-ws1011-vc13_flash9.mp4
|
2010
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
|
English
|
10.5446/334 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Data Mining Overview, Association Rule Mining (16.12.10)
|
https://av.tib.eu/media/333
| null |
https://tib.flowcenter.de/mfc/medialink/3/deeba99ad35c30269d6cf8da686cc4210278ad532e8254b0ebd7abb0ee6444d04fcd/dwh-ws1011-v8_flash9.mp4
|
2010
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
|
English
|
10.5446/333 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Optimization (25.11.2010)
|
https://av.tib.eu/media/332
| null |
https://tib.flowcenter.de/mfc/medialink/3/de9bb307fce7058aaf58d8b61da3b26926309f0b67945676e780d21f98cd0b4498/dwh-ws1011-v5_flash9.mp4
|
2010
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
|
English
|
10.5446/332 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Decision Support Systems (27.01.2011)
|
https://av.tib.eu/media/331
| null |
https://tib.flowcenter.de/mfc/medialink/3/deb5286867e47ef27ce7569855cb8b8869450fde5881885033226623cf7c15c577/dwh-ws1011-v12_flash9.mp4
|
2010
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
|
English
|
10.5446/331 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Data Modeling (Logical & Physical Models) (11.11.2010)
|
https://av.tib.eu/media/330
| null |
https://tib.flowcenter.de/mfc/medialink/3/dec5c7604752d3d833b7e5bc1164ca423c4116917313f240a5781d20c5aa219ba9/dwh-ws1011-v3_flash9.mp4
|
2010
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
|
English
|
10.5446/330 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Sequence Pattern Mining & Time Series (06.01.2011)
|
https://av.tib.eu/media/329
| null |
https://tib.flowcenter.de/mfc/medialink/3/de41e388118020d652e57be39fe0d615c814b94959fd53790eb20f2e8666cb8bbe/dwh-ws1011-v9_flash9_1.mp4
|
2010
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
|
English
|
10.5446/329 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Architecture, Data Modeling (Conceptual Model) (04.11.2010)
|
https://av.tib.eu/media/328
| null |
https://tib.flowcenter.de/mfc/medialink/3/de90366fcdc53181eefc7a9ddb049437a88be83c1257b75d35cbd274ff8d8f74ee/dwh-ws1011-v2_flash9_1.mp4
|
2010
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
|
English
|
10.5446/328 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Classification (13.01.2011)
|
https://av.tib.eu/media/327
| null |
https://tib.flowcenter.de/mfc/medialink/3/defa5fc66f079e0a7c01c7c5f5b2360a0cdb58249609895735038bad854c6d2997/dwh-ws1011-v10_flash9.mp4
|
2010
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
|
English
|
10.5446/327 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Indexes (18.11.2010)
|
https://av.tib.eu/media/326
| null |
https://tib.flowcenter.de/mfc/medialink/3/de7a50a40638e4e008ae2c01a49b8ac4b77bf5430428e2ea22578daddfe3a91aad/dwh-ws1011-v4_flash9.mp4
|
2010
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
|
English
|
10.5446/326 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Data Warehousing and Data Mining Techniques - Introduction (28.10.2010)
|
https://av.tib.eu/media/325
| null |
https://tib.flowcenter.de/mfc/medialink/3/deb839dcd45e94508015fa27a0ee9a71d90aecda678af00415e18d0e3ba202a994/dwh-ws1011-v1_flash9.mp4
|
2010
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
|
English
|
10.5446/325 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
Build the DW, ETL (09.12.2010)
|
https://av.tib.eu/media/319
| null |
https://tib.flowcenter.de/mfc/medialink/3/de69f29e82e6c692bdd0726c5c302c25174d7a7e5274be12386a25e90b9a2754cc93/dwh-ws1011-v7_flash9.mp4
|
2010
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
|
English
|
10.5446/319 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
OLAP Operations & Queries (02.12.2010)
|
https://av.tib.eu/media/316
| null |
https://tib.flowcenter.de/mfc/medialink/3/de88d5d571482184abf08ef828cde754388aaef14425238eceb89f19b4f78af5ec/dwh-ws1011-v6_flash9.mp4
|
2010
|
Computer Science
|
Lecture
|
In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
|
English
|
10.5446/316 (DOI)
|
CC Attribution - NonCommercial 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
|
Balke, Wolf-Tilo
|
Homoceanu, Silviu
|
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