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TSum: Fast, Principled Table Summarization Jieying Chen
Jia-Yu Pan
Google, Inc.
Google, Inc.
Spiros Papadimitriou
[email protected]
[email protected]
Christos Faloutsos
Rutgers University
[email protected]
Carnegie Mellon University
[email protected]
ABSTRACT Given a table where rows correspond to records and columns correspond to attributes, we want to find a small number of patterns that succinctly summarize the dataset. For example, given a set of patient records with several attributes each, how can we find (a) that the "most representative" pattern is, say, (male, adult, ∗), followed by (∗, child, lowcholesterol ), etc? We propose TSum, a method that provides a sequence of patterns ordered by their "representativeness." It can decide both which these patterns are, as well as how many are necessary to properly summarize the data. Our main contribution is formulating a general framework, TSum, using compression principles. TSum can easily accommodate different optimization strategies for selecting and refining patterns. The discovered patterns can be used to both represent the data efficiently, as well as interpret it quickly. Extensive experiments demonstrate the effectiveness and intuitiveness of our discovered patterns.
Categories and Subject Descriptors H.2.8 [Database Applications]: Data Mining
General Terms Algorithms
In this paper, we focus on the problem of effectively and succinctly summarizing a table of categorical data. In particular, we are interested in the following questions: How can we find a few patterns, that summarize the majority of the rows? How can we effectively characterize the significance of each pattern, as well as how many patterns are sufficient to summarize the structure of the dataset? A few examples of applications include: (a) summarizing patient records including symptoms, to find that, e.g., "most patients have in common that they are male, middle-aged, and have high cholesterol, then most patients are children
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with chickenpox, and then female with osteoporosis," and these patterns describe the majority of patients; (b) summarizing a set of click fraud clicks to grasp what these spam clicks have in common (e.g., same IP address or query words, etc.). The patterns cover a big portion of spam clicks and provide useful information to determine the cause/property of the spam attacks. The informal problem definition is Given a table with N rows and D categorical attributes, Find a few good patterns, that properly summarize the majority of those rows. To achieve the above, the fundamental question is how to measure the goodness of a pattern? We propose to formulate this as a compression problem. Intuitively, a pattern is "good" if it leads to high compression. The discovered table summary can be used to represent the data more efficiently and to interpret them quickly, by revealing the combinations of features (i.e., patterns) that properly summarize the data. Our main contributions in this paper are the following: Compression-based formalization: We propose to approach the problem as outlined above. This permits us to discover the patterns and to suggest the appropriate number of patterns without human intervention. Generality: Our proposed TSum framework can accommodate different strategies for searching in the pattern space (e.g., strategies that may incorporate domain knowledge). Effectiveness: We demonstrate that the discovered patterns are informative and intuitive, by conducting an extensive experimental evaluation. Scalability: We propose a strategy to discover the patterns, based on our formalization, that is linear on the number of rows N .
Summarizing Click Fraud Attacks
A click fraud filtering system examines ad clicks and filters clicks that are considered invalid. From time to time, there may be sudden increases of clicks filtered by the system. These sudden increases of invalid clicks may due to (1) new click fraud attacks caught by the system, (2) system misconfigurations at the ad network partner or publisher sites, and so on. These increases of invalid clicks create "spikes" in the ad click traffic. Our proposed method can automatically generate a summary for the "spikes" in click-fraud invalid clicks, so that an engineer can easily grasp what these spam clicks have in common (e.g., same IP address or query words, etc.). The summary generated by the proposed algorithms consists of the major patterns among the invalid clicks. These patterns
provide useful information for determining the cause of an alert and whether there are leakages in the filters. In the remainder of the paper we first introduce the proposed method, then present experiments, followed by discussion, related work and conclusions.
Our goal is to design a data mining process for finding and ranking patterns that can properly summarize a data table. Starting with the informal problem definition given in the introduction, we proceed by formalizing the key concepts of our proposed TSum framework. Our formalization is based on the Minimum Description Length (MDL) principle [11, 7]. Furthermore, our proposed framework is modular, allowing users to plug any pattern generation method into the framework. Our framework will subsequently choose those patterns that summarize the table well. Users have the flexibility to incorporate domain knowledge in the process by using a domain-specific pattern generation method. First, we need to define precisely what we mean by "pattern" and then we define the objective function that determines which are good patterns. Finally, to illustrate the flexibility of TSum, we describe two strategies for pattern generation and show that the TSum framework can compare and incorporate both.
Examples and Definitions
The typical application of the proposed TSum framework is to summarize a transactional data set with categorical attributes. More specifically, each record in the data set is a tuple (A1 =v1 , . . ., AD =vD ), where Ai are the attribute names and vi are the attribute values. In this work we consider categorical values, but it is possible to extend to other types. When the order of the attributes is known, we omit their names in a data record (v1 , . . ., vD ). In this work, a pattern is a "template" that is matched by a set of records. Definition 1. (Pattern, matching attributes, and pattern size) A pattern is a tuple (A1 =u1 , . . ., AD =uD ), where ui can either be a specific value or a "don't care" symbol (denoted as "∗"). Attribute Ai is a matching attribute iff ui 6= ∗. The number of matching attributes in a pattern P is the size of the pattern, denoted size(P ). Following established database terminology [4], the patterns used in this work are partial match patterns. A pattern is a conjunction of conditions. For example, consider the patient records in Figure 1, with attributes (gender, age, blood-pressure). Two example patterns are P1 =(gender =M, age=adult, blood-pressure= ∗) and P2 =(gender = ∗, age=child, blood-pressure=low ). To simplify the notation, we either omit the "dont' care" attributes from the pattern representation, i.e., P1 =(gender =M, age=adult) and P2 =(age=child, blood-pressure=low ), or omit the attribute names, i.e., P1 =(M, adult, ∗) and P2 =(∗, child, low ). Furthermore, we say that a data record (v1 , . . ., vD ) is covered by a pattern P = (u1 , . . ., uD ) if either vi =ui or ui =∗ ("don't care"). A data record is coverable by different patterns, and a pattern can cover more than one record in a data table. In Figure 1, the records in the top of the table are covered by the first pattern, the records in the middle are covered by the second pattern, and the records in the
gender M M M M M F M F M F
age adult adult adult adult adult child child child teen teen
blood-pressure normal low normal high low low low low high normal
Figure 1: Example table with patient records. Lines highlight the groupings a human might identify: P1 =(male, adult, ∗) and P2 =(∗, child, low ).
bottom are covered by neither pattern. Intuitively, a human would consider these two patterns as a good summary of the example table: most of these patients are either adult males, or children with low blood pressure. Definition 2 (Pattern set and list). A pattern list is an ordered sequence of patterns P = [P1 , P2 , . . .], while a pattern set is simply a set of patterns P = {P1 , P2 , . . .}, with no particular order. The reason for distinguishing between set and list will become clear later. A collection of patterns provides a way to summarize a data table. For example, in Table 1, the pattern P1 can be used to summarize the first five records. In particular, the data record ID01 can be summarized as "pattern P1 with (blood-pressure='normal')". Note that the more a pattern P covers a data record R, the better P can summarize R (i.e., the less additional information we need to give to fully describe the data record R). Similarly, the pattern P2 can be used to summarize data records ID06, ID07, and ID07. In general, a pattern can be used to summarize all of the data records that it covers. In the example, records ID09 and ID10 are not covered by any pattern, and therefore, cannot be summarized by any pattern. Our goal is to identify a collection of patterns that best summarize a data table. Problem 1. (Summarizing a table with categorical data) Given a data set T that consists of records with categorical attribute values, find patterns P that summarize the data set T . Next, we formalize how we choose P. We propose to use the concept of "encoding cost" to quantify the summarization quality of a given pattern collection. Specifically, we define a cost function that measures the effectiveness of a pattern list on compressing the given data table. Problem 2 (Compression Cost Estimation). Given a table T and a list of patterns P = [P1 , . . . , Pm ], estimate the resulting compression cost CC(T , P). Given several competing pattern collections, our proposed framework TSum will identify and refine a pattern list that best summarizes the given data set, where the output pattern list achieves the smallest encoding cost.
Problem 3 (Pattern selection). Given a data table T and several pattern collections P1 , . . ., Pm : Identify a pattern collection, refine the patterns, and output a pattern list that achieves the lowest compression cost for the data table T .
Symbol N D Wj Vj
P k Ni log∗ (i)
The proposed TSum framework uses the compressed size of a data table to choose the most successful patterns. In this section, we first describe how to use a pattern to compress data records, and we introduce our proposed compression encoding scheme. Then, we will describe our "pattern marshalling" procedure for ordering and refining an unordered set of patterns S. The output of "pattern marshalling" is an ordered list of patterns PMA(P). We propose a scheme to compute the overall compression cost of a table T using PMA(P). At the end, TSum selects and outputs the pattern list PMA(P ∗ ) which achieves the best compression cost on the given data table T .
Pattern Encoding and Compression Cost
We first define a few important concepts. As we mentioned earlier, the coverage of a pattern P is the count of records that satisfy P . The footprint of a pattern P on a data set T , denoted as FP (P, T ), is the number of bits that the pattern can represent and thus save: FP (P, T ) = Cov (P, T ) ∗ size(P ). Intuitively, the footprint of a pattern P on a data table T is correlated to the number of matched attributes covered by the pattern P on T . We propose an encoding scheme to compress the set of data records covered by a pattern P . The proposed encoding scheme can be illustrated using the example in Figure 1. In the figure, pattern P1 covers the records ID01, ID02, ID03, ID04, ID05. These five records can be compressed by specifying three components: the pattern P1 , the number of records covered by P1 , and the attribute values that are not covered by P1 . The matched attributes, matched (P ), of a pattern P =(u1 , . . ., uD ), are exactly the attributes that the pattern specifies; the rest we shall refer to as the don't care attributes. Definition 3 (Model cost of a pattern). The cost of encoding a pattern is: X CC(P ) = D + Wi (1) i;Ai ∈matched(P )
A pattern P =(u1 , . . ., uD ) can be encoded in two parts: (1) a D-bit bitmap specifying the matching attributes (bit value 1) and the "don't care" attributes (bit value 0), and (2) the values of the matching attributes. Since Wi is the number of bits for encoding the values of attribute Ai , this completes the definition.
Definition Number of records Number of attributes Average number of bits for j-th attribute Vocabulary = number of distinct values of j-th attribute pattern-list = (P1 , P2 , . . . , Pk ) Number of patterns in pattern-list Number of records matching the i-th pattern Number of bits to encode integer i
Table 1: Symbols and definitions
The compression requires encoding 3 pieces of information: (1) the pattern P , (2) the number of records covered by P , and (3) the attribute values in R that are not covered by P . Since N is the number of records in R (the coverage of P on T ), the second term follows. In practice, we substitute log∗ (N ) with log∗ (N ) = 2 ∗ dlog2 (N + 2)e. The third term is the cost for encoding the attribute values of R that are not covered by the pattern P . This completes the justification of Definition 4. Table 1 gives the definitions of symbols that we use.
Marshalling and Refining a Pattern Set
With our proposed encoding scheme introduced in Definition 4, given a pattern set P = {P1 , . . . , Pk }, we can now compress a data table T by compressing the records covered by each of the patterns Pi in P. However, in some cases, a data record may be covered by more than one pattern in the given P. How do we resolve such conflict and achieve the best compression? In this section, we introduce a "pattern marshalling" procedure to determine a precedence among the patterns in P and compress a data set using the patterns in their marshalled ordering. We propose to order the patterns according to the benefit that a pattern can provide in terms of the "compression savings." Definition 5 (Compression saving). The compression saving of a pattern P on a data table T , denoted as Saving(P, T ), is the amount of compression it can achieve. Specifically, it is the difference of bits between the uncompressed and compressed representations of the data records covered by pattern P . Let N be the number of records covered by pattern P and D be the number of attributes in T . Then, Saving(P, T ) = Benefit(P, T ) − Overhead (P, T ). (3) Intuitively, Saving(P, T ) consists of two terms: (1) the benefit of using the pattern to represent data records, X Benefit(P, T ) = (N − 1) ∗ Wi , (4)
i,Ai ∈matched(P ) Definition 4 (Compression cost of a pattern). Given a pattern P and a data table T , the set of records R covered and (2) the overhead of using the pattern in the representaby P can be compressed in CC(P, T ) bits, that is tion,
CC(P, T ) = CC(P ) + log∗ (N ) + N ∗
Overhead (P, T ) = D + log∗ (N ) Wi . (2)
i,Ai 6∈matched(P )
where log∗ (N ) is the cost in bits to store the self-delimiting version of integer N (see [11]).
To resolve the conflict when a data record is covered by more than one pattern, we propose to "marshall" the patterns according to their compression saving. The pattern with the highest compression saving will take precedence
over other patterns. Therefore, when there is a coverage conflict, the data record will be assigned to the pattern with the highest compression saving. More specifically, given a set of patterns S, the "pattern marshalling algorithm (PMA)" iteratively picks a pattern from S which has the highest compression saving. After each iteration, the data records that have been covered by the patterns chosen so far will be removed from consideration. The compression saving of the patterns at the next iteration is considered only on the remaining data records which have not been covered by any chosen pattern. In other words, at each iteration, the pattern chosen is that with the best "incremental compression saving." The PMA algorithm repeats the iterations until no pattern has positive incremental compression saving. Definition 6 (Residue data table). Given a data table T and a pattern collection P, we defined the "residue data table" of T , with respect to P, as the data records that are not covered by any of the patterns in P. We denote the residue data table as T \ P. Definition 7 (Incremental compression saving). Given a list of patterns P = [P1 , . . . , Pl ] and a data set T , the "incremental compression saving" of a new pattern P , with respect to P, is defined as: Saving(P, T \ P) = Benefit(P, T \ P) − Overhead (P, T \ P). (6) Intuitively, Saving(P, T \ P) is essentially the compression saving of P on the residue data table T \ P . Given a set of patterns, the PMA algorithm ranks the patterns by their (incremental) compression saving, and outputs a list of patterns that are useful in compressing the data table in question. Algorithm 1 shows the pseudocode of the PMA algorithm. To compress the entire data table T using a "marshalled" pattern list P = [P1 , . . . , Pk ], we propose a procedure similar to the PMA algorithm. The procedure starts from the first pattern P1 , compresses the records in T covered by P1 , and then continues to the next pattern and compresses the records in the residue data set. In practice, we always include the "don't care" pattern, i.e., (*, . . ., *) in the pattern list when compressing a data table. The "don't care" pattern can be used in representing the data records that are not covered by any other pattern in the given pattern list. We now can define the total compression cost of a data table T , using a given set of (marshalled) patterns P:
a pattern set P ∗ that best summarizes the table T in terms of compression saving. In the functional form, P ∗ = TSum(T , I = {P1 , . . . , Pk }),
P ∗ = arg min CC(T , P M A(P)).
where PinI
The final output of the TSum framework is the marshalled pattern list P M A(P ∗ ).
The TSum framework takes in one or more pattern sets and selects a pattern set (after "marshalling") that is best in summarizing a data table. The input pattern sets are therefore important regarding the quality of the summarization. In this work, the best summarization for a data table corresponds to the pattern set that best compresses the data table. Unfortunately, there are exponentially many possible pattern sets that can compress the data table, and it is difficult to find the one that achieves the best compression. In this section, we introduce two strategies for generating a pattern set that captures the major characteristics of a data table, namely, the "Spike-Hunting" strategy and the "Local-Expansion" strategy. The Spike-Hunting strategy identifies the major multi-dimensional "spikes" in the given data set, where the "spikes" usually correspond to (and, summarize) major groups of data records in the data set. On the other hand, the Local-Expansion strategy tries to "grow" patterns to increase the compression saving on the data records.
The Spike-Hunting Strategy
Real world data sets usually contain clusters among attribute values. These clusters sometimes capture key properties of the data set and provide a good summary of the data set. The most significant clusters of attribute values also reveal themselves as "spikes" in the multi-dimensional attribute space. In this work, each spike is represented as a set of conditions such as {A = a, B = b, . . .}, which is the same representation as that of a pattern (Definition 1). We propose a pattern generation strategy ("Spike-Hunting") to find these attribute-value spikes (patterns) in a data table. At the high level, the procedure of the "Spike-Hunting" strategy is as follows: First, pick an attribute A that is "spiky" with respect to the marginal distribution of A, and then find the attribute values of A that forms the spike (say, A = a). Then, recursively repeat the same steps of picking spiky attributes and finding spike values, from the attributes that have not been considered yet and conditioned on only Definition 8 (Compression cost of a data table). the data records that satisfy the spiky conditions found so Given a data table T and a "marshalled" pattern list P = far. Algorithm 2 shows the algorithm details. [P1 , . . . , Pk ], the total compression cost of T using P is: Intuitively, the Spike-Hunting algorithm progressively searches k X for a multi-dimensional spike starting from single attributes. CC(P, T ) = CC(Pi , T \ {P1 , . . . , Pi−1 }). (7) When a spike is found in the marginal distribution of one i=1 attribute, the algorithm continues to refine that spike by examining the marginal distributions on other attributes, conIn the equation, CC(Pi , T \ {P1 , . . . , Pi−1 }) is the compresditioned on those attributes (and their corresponding spike sion cost of Pi on the residue data table T \ {P1 , . . . , Pi−1 } values) that have already been included in the spike pattern. (see Definitions 4 and 6). In the Spike-Hunting algorithm, we use the entropy of an Definition 9 (The TSum framework). Given a data attribute's (conditional) marginal distribution to determine table T , and an input collection of pattern sets I = {P1 , . . . , Pk }, if there is a spike on that attribute. Then, we determine the the TSum framework selects and refines (through marshalling) values in the conditional marginal distribution that belong
Algorithm:(Pattern Marshalling) PMA (S, T) Input: T : Table, N rows, D attributes Input: S: Set of m patterns {P1 , . . . , Pm } Output: The best pattern-list P = [Pi1 , ...Pik ] P = ∅; R = S; while R 6= ∅ do //Select the pattern with the top "incremental" compression saving. Sort R by the Saving(P, T \ P), w.r.t. P ; Let the top pattern be Ptop = arg maxP ∈R Saving(P, T \ P); Let the incremental compression saving be btop = maxP ∈R Saving(P, T \ P) //btop could be non-positive, if so, ignore the pattern Ptop . if btop > 0 then //Append Ptop at the end of the output pattern list P. P ← [P, Ptop ] ; end //Update remaining patterns. R ← R \ Ptop ; end //P now is a sorted list of patterns. return P Algorithm 1: Pattern-marshalling algorithm (PMA): Picks patterns with the best incremental compression saving, and skips the ones with zero or negative saving.
to the spike (i.e., the values which are by far more probable than other values). We take the top few values of A that capture the majority of the energy (variance) of the (conditional) distribution as the spike values. We note that the high-level procedure of Spike-Hunting algorithm looks similar to the CLIQUE algorithm [3], if we viewed each distinct attribute value as an "interval" when using CLIQUE. One difference between the proposed SpikeHunting algorithm and CLIQUE is the objective function used in selecting the (subspace) dimensions. In particular, Spike-Hunting uses the entropy function, followed by the energy ratio check, whereas CLIQUE uses a user-specified density threshold, to identify dimensions having dense regions. In our experiments (Section 5), we will show that TSum can work with any pattern generation strategy, whether it be Spike-Hunting (a strategy with a procedure similar to that of the CLIQUE algorithm) or any other strategy, and compare and select the appropriate patterns from the ones generated by these different strategies.
The Local-Expansion Strategy
Based on the proposed compression cost function (Definition 4), we also design a pattern generation strategy that directly looks for patterns that could minimize the compression cost. Our approach for finding each such pattern is a "best-first" approach: we start from single attributes first and find a single-condition pattern P = {(A = a)} that has the best compression saving, and then expand the pattern by adding other conditions until the compression cost can not be improved. To find the next pattern, the same procedure is repeated, but only on the "residue" data table, the part of the data table not covered by the patterns found so far. Because this strategy generates a pattern by expanding in a "best-first" fashion, we called this strategy the "LocalExpansion" method. Algorithm 3 shows the details of this method. Figure 2 illustrates the process of pattern expansion (Algorithm 4). A pattern P1 will be expanded to P2 by adding an additional condition, if the compression saving (indicated
Figure 2: Illustration of pattern expansion: The blue area indicates the saving that a pattern P1 can achieve. The Local-Expansion algorithm will expand P1 to P2 (depicted by the red area) if P2 has bigger compression saving (that is, roughly speaking, if the red area is bigger than the blue area). by the colored areas in the figure) increases. According to Definition 5, the compression saving of a pattern is dominated by the term Benefit(P, T ), which is closely related to the colored area in the figure.
General Pattern Generation Strategy
The two strategies that we presented above, the SpikeHunting method and the Local-Expansion method, are just two of the many possible methods for generating pattern sets to summarize a data table. We would like to emphasize that the proposed TSum framework can work with any pattern set, regardless of whether it is generated by our proposed strategies or not. The two strategies we proposed are two special cases of a more general strategy for pattern generation. Conceptually, the Spike-Hunting and Local-Expansion algorithms are both
Algorithm:Spike-Hunting (T ) Input: A table T with N rows and D attributes Output: A list of patterns P P = ∅; foreach attribute A in T do //Check whether A's marginal distribution is spiky. if Entropy(A) ≤ threshold then Identify the values forming the spike SV = {a1 , a2 , . . .}; foreach value a in {a1 , . . .} do //Create a table conditioned on A = a. Let T 0 be the table which contains records with A = a, and does not include the attribute A; //Recusively find the spikes. SubPatterns = Spike-Hunting (T 0 ); foreach pattern P in SubPatterns do Pnew = {A = a} ∪ P ; P = P ∪ Pnew ; end end end end return P Algorithm 2: The Spike-Hunting algorithm. Algorithm:Local-Expansion (T ) Input: A table T with N rows and D attributes Output: A list of patterns P P = ∅; while T is not empty do //Expand from an empty pattern (Algorithm 4). P = Expand(T , ∅) //Stop, if we cannot achieve more compression saving. if P == ∅ then break; end //Found a new pattern. P = P ∪ {P }; //Update the table by removing records covered by P . T = T − { tuples covered by P }; end return P Algorithm 3: The Local-Expansion algorithm
doing a traversal on a lattice space of all possible patterns. The two algorithms have different criteria to decide which branch to traverse first and when to stop the traversal. Figure 3 illustrates the lattice structure of our space, using the "patient" toy dataset in Figure 1. We start from the empty pattern-list ((*,*,*)), and we proceed with two types of expansions: (a) condition-expansion, where we add one more condition to one of the existing patterns of the patternlist (see thin-lines in Figure 3), and (b) pattern-expansion, where we append one more pattern to the pattern list P (thick line in Figure 3) The Spike-Hunting and Local-Expansion methods use different criteria when doing condition-expansion. The SpikeHunting method does the condition-expansion step when it detects additional spiky attribute values, and the expansion to a new attribute can have multiple choices of values (if the spike consists of more than one value). On the other hand, the Local-Expansion method expands with a new condition if adding the new condition can increase the compression
saving of the pattern. Both methods do a series of condition-expansion operations until the expansion of a pattern stops, then a patternexpansion operation is performed to find the next pattern. Both methods do not revisit a pattern after it is finalized. Regarding the pattern-expansion operation, the Spike-Hunting method starts a new pattern by back-tracking from the pattern finalized before. On the other hand, the Local-Expansion method starts a new pattern from scratch, but considers only the data records on the residue data table, not on the entire table.
In this section, we consider the following data sets from UC-Irvine repository. • ADULT50-: Adults with income less than 50K (24720 records, 7 attributes). • ADULT50+: Adults with income greater than 50K (7841
Algorithm:Expand(T , P ) Input: T : A table with N rows and D attributes Input: P : A pattern from which to start the expansion Output: Pexpand : An expanded pattern with a best compression saving foreach attribute A not included in P do foreach value a of A do Let P 0 be the expanded pattern by appending (A = a) to P .; Compute the compression saving of P 0 on T . end end Let Pbest be the expanded pattern with the biggest compression saving Saving(T , Pbest ).; if Saving(T , Pbest ) > Saving(T , P ) then //Continue the expansion recursively. return Expand(T , Pbest ) end else return P end Algorithm 4: Expanding a pattern to improve compression saving. The best pattern may be the empty pattern, i.e., no pattern: (*, *, . . . , *). Dataset Best Bits Coverage
ADULT50Local Expansion 447,477 100%
ADULT50+ Local Expansion 124,252 100%
CENSUS Spike Hunting 2,037,398 43.81%
NURSERY Local Expansion 216,126 100%
Table 2: Winning heuristic, for each dataset
( (*,*,*) )
Figure 3: Lattice organization of our search space. Light line: "condition-expansion"; thick line:"pattern-expansion". records, 7 attributes). • NURSERY: Nursery data (12960 records, 9 attributes). We run TSum with the two proposed pattern generation strategies, Spike-Hunting and Local-Expansion. Table 2 reports the winning strategy and the corresponding bit cost. Notice that Local-Expansion usually wins, but not always. In general, Local-Expansion tends to choose patterns with much broader coverage, because these typically give larger compression saving. On the other hand, Spike-Hunting tends to produce patterns with much fewer "don't cares" (∗), which usually correspond to fairly focused spikes and, hence, have lower coverage. In other words, Local-Expansion tries to cover as many rows of the table as possible, whereas SpikeHunting will mostly cover "hot-spots" in the table. Shorter
patterns help reduce the bit cost by covering a larger number of rows, whereas longer patterns cover more attributes and therefore need fewer bits to describe each row they cover. Spike-Hunting will try to explore as many attributes as possible by considering all rows in the table, whereas Local-Expansion progressively excludes rows by considering only the remainder table. If "hot-spots" are fairly spread out, then Spike-Hunting may succeed in finding patterns that achieve a lower bit cost. In general, however, LocalExpansion will tend to produce patterns with lower bit cost. Finally, we should point out that, our pattern marshalling method will refine any pattern set and rank patterns with respect to how "informative" they are, whether they are produced by Local-Expansion, Spike-Hunting, or another strategy. Next we give more details for each dataset.
ADULT dataset. This dataset is from the UCI repository, and is already naturally subdivided into two classes: people above or below income of $50K (ADULT50+ and ADULT50-, respectively). For ADULT50-, the Local-Expansion algorithm wins, with a bit cost of 447, 477. The top 3 patterns are listed in Table 3. In the table, "footprint" indicates the number of bits used in encoding the attribute values matched by a pattern. For comparison, in Table 4 we also show the top patterns selected by Spike-Hunting, even though this is not the winning strategy on this data set (total bit cost 470,406). We clearly see that these patterns have much fewer don't care attributes. Both sets of patterns make sense. Those selected by Local-Expansion show the important broad properties in the dataset (as reflected by the lower total bit cost), whereas those selected by Spike-Hunting are more focused on hotspots in the data set that are important (i.e., hot spots that
age ∗ ∗ ∗
workclass private ∗ private
education ∗ ∗ ∗
marital ∗ ∗ ∗
occup. ∗ ∗ ∗
race white white black
sex ∗ ∗ ∗
Footprint 104104 17481 13657
Benefit 104061 17442 13614
Coverage 60.16% 23.57% 7.89%
Table 3: Top three patterns of the winning strategy (Local-Expansion) selected by TSum for ADULT50- data. age 20–30 20–30 30–40
workclass private private private
education some-college HS-grad HS-grad
marital-status never-married never-married married-civ-spouse
occup. ∗ ∗ ∗
race white white white
sex ∗ ∗ M
Footprint 22192 21071 12680
Benefit 22137 21016 12624
Coverage 4.72% 4.49% 2.56%
Table 4: Top three patterns (ranked by our pattern marshalling algorithm) using Spike-Hunting on ADULT50-.
still achieve notable reduction in bit cost). In summary, both algorithms produce reasonable summaries, with broad characteristics that reflect the design goals of each optimization heuristic. Furthermore, the patterns produced by the winning strategy (Local-Expansion), as chosen by TSum, make sense as a good summary of the broad characteristics of the dataset: people at this income bracket are usually younger, there is no dominating marital status, and no dominating occupation. This is naturally reflected by our proposed bit cost, and agrees with intuition. Before we continue, we note that, for each pattern, the footprint is a good approximation of the benefit, i.e., the actual number of compression bits. Thus, for the remaining datasets we just show footprint. For ADULT50+, TSum again chooses Local-Expansion as the winning strategy, with a total bit cost of 124, 252. Table 5 further shows the top three patterns selected by TSum. The observations when comparing against Spike-Hunting are very similar to those for ADULT50-. Finally, the patterns again make sense and form a good summary of the broad characteristics: people in this income bracket are usually older, and thus married. CENSUS dataset. Here Spike-Hunting is chosen as the winning strategy by TSum, with a bit cost of 2, 037, 398 (versus 2, 210, 319 bits for Local-Expansion). As noted earlier, we believe this is because the spikes in this dataset are more broad. In more detail, Local-Expansion progressively searches for patterns, favoring those with higher coverage, and subsequenty excluding the covered rows in its search. On the other hand, Spike-Hunting tends to give patterns with more attribute values (and fewer "don't cares") at the cost of typically lower coverage. However, each covered record can be described with fewer bits, since the SpikeHunting patterns tend to include more attributes. In summary, both algorithms produce reasonable summaries according to the design of their optimization heuristics (and the marshalling process of TSum can produce a good ranking of patterns from either strategy) and, depending on the structure of the dataset, one strategy may be overall better than others. Again, the total bit cost reflects the properties of good summaries of the broad characteristics.
Nursery dataset. The nursery dataset is essentially synthetic, with mostly independently uniform random distributions, and thus it is interesting to note that TSum finds no meaningful summary because none exists. The results
Figure 4: Linear scalability: run time vs number of records. themselves are not interesting, and thus omitted.
Figure 4 shows that the running time of our algorithm is linear on N (number of records). The dataset was ADULT50and we sampled records, with sample sizes from 4000 to 24720. We report the average running time over 5 runs, along with the error-bars (red) for one standard deviation.
Clustering and co-clustering There are numerous such algorithms including methods for numerical attributes (Birch [15], CURE [8], DBSCAN [12], and OPTICS [5]), categorical attributes high dimensional variations (subspace clustering [3] and projected clustering [2]). All of these methods finds cluster centers alone, or cluster centers together with subsets of dimensions. However, they generally do not produce patterns or rules. Furthermore, most of the above approaches need some user-specified parameter or threshold. Association Rules Association rules typically operate on sets (or, binary attributes), such as "market baskets." However, (a) they need user-defined parameters (e.g., min-support, etc), and (b) they generate too many rules, which defeats the purpose of summarization. Several publications try to address this issue, e.g., [1, 13, 14, 16]. Most of this work needs some user-defined parameter. More importantly, the pattern sets that any of these approaches produce can be
age ∗ ∗ ∗
workclass ∗ private ∗
education ∗ ∗ ∗
marital-status married-civ-spouse ∗ married-civ-spouse
occup. ∗ ∗ ∗
race white white ∗
sex male ∗ ∗
Footprint 38108 6360 2550
Coverage 69.43% 13.52% 10.84%
Table 5: Top three patterns of the winning strategy (Local-Expansion) selected by TSum for ADULT50+ data.
incorporated in our proposed framework, as an alternative optimization strategy, and TSum will rank those patterns and choose an appropriate number to form a good summary of the general characteristics of the data set. SVD, LSI and dimensionality reduction These methods operate on matrices, and have been successful in numerous applications, e.g., [6, 9]. However, in most cases (a) some user-defined parameters are required (e.g., how many singular values/vectors to keep), and (b) the resulting patterns are hard to interpret (vectors in high-dimensional space). Compression, MDL, and Kolmogorov complexity LempelZiv [10] and LZW are suitable for strings. However, since we would need to linearize the table, an ordering of both rows and columns would be necessary, which is not an easy problem. MDL [11, 7] is a vital concept. At the high level, TSum exactly tries to find a pattern language and the corresponding patterns, so that the table (using the patterns) together with the chosen patterns themselves can be compressed as well as possible. As far as TSum is concerned, any method that generates patterns from a given data table can work side-by-side within the TSum framework. The pattern generation methods (either a clustering algorithm or a pattern enumeration method) provide "pattern candidates" for TSum, and the TSum framework will pick the patterns that are suitable for describing the data table.
In this paper we present TSum, a method to efficiently discover patterns that properly summarize a data table. Our main contribution is the rigorous formalization, based on the insight that table summarization can be formalized as a compression problem. More specifically, (i) we propose a compression cost objective to effectively discover proper summaries that reveal the global structure of the data table, and (ii) show how different optimzation strategies can be accomodated by our framework, and (iii) propose two strategies, Spike-Hunting and Local-Expansion, each designed with different goals for their optimization heuristics.
We would like to thank Anita Krishnakumar, Razvan Surdulescu, and Michael Mcnally for comments and suggestions on the paper.
[1] F. N. Afrati, A. Gionis, and H. Mannila. Approximating a collection of frequent sets. In KDD, 2004. [2] C. C. Aggarwal and P. S. Yu. Finding generalized projected clusters in high dimensional spaces. In SIGMOD, 2000. [3] R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data. DMKD, 11(1), 2005.
[4] A. Aho and J. Ullman. Optimal partial match retrieval when fields are independently specified. ACM TODS, 4(2):168–179, June 1979. [5] M. Ankerst, M. M. Breunig, H.-P. Kriegel, and J. Sander. OPTICS: Ordering points to identify the clustering structure. In SIGMOD Conference, pages 49–60, 1999. [6] S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6):391–407, Sept. 1990. [7] P. D. Gr¨ unwald. Advances in Minimum Description Length: Theory and Applications, chapter A Tutorial Introduction to the Minimum Description Length Principle. MIT Press, 2005. http://homepages.cwi.nl/~pdg/ftp/mdlintro.pdf. [8] S. Guha, R. Rastogi, and K. Shim. CURE: An e fficient clustering algorithm for large databases. In SIGMOD Conference, pages 73–84, Seattle, Washington, 1998. [9] F. Korn, H. Jagadish, and C. Faloutsos. Efficiently supporting ad hoc queries in large datasets of time sequences. ACM SIGMOD, pages 289–300, May 13-15 1997. [10] A. Lempel and J. Ziv. Compression of two-dimensional images. In A. Apostolico and Z. Galil, editors, Combinatorial Algorithms on Words, pages 141–154. Springer-Verlag, Malatea, Italy, June 18-22 1984. Published as NATO ASI Series, volume F12. [11] J. Rissanen. Minimum description length principle. In S. Kotz and N. L. Johnson, editors, Encyclopedia of Statistical Sciences, volume V, pages 523–527. John Wiley and Sons, New York, 1985. [12] J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery, 2(2):169–194, 1998. [13] G. I. Webb. Discovering significant rules. In KDD, 2006. [14] X. Yan, H. Cheng, J. Han, and D. Xin. Summarizing itemset patterns: a profile-based approach. In KDD, 2005. [15] T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An efficient data clustering method for very large databases. ACM SIGMOD, pages 103–114, May 1996. [16] F. Zhu, X. Yan, J. Han, P. S. Yu, and H. Cheng. Mining colossal frequent patterns by core pattern fusion. In ICDE, 2007.
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text:
|Base Price (without GST):
|Rs. 1720608
|GST + Cess:
|Rs. 774293
|Exshowroom Delhi:
|Rs. 2494901
|On Road Price in Delhi:
|Rs. 2918592
XUV700 AX7 Luxury All Wheel Drive AT On Road Price includes Road Tax, MCD Parking, Dealer Logistics Handling and Insurance Premium
Mahindra XUV700 - Cheetah is right here in all new generation with major upliftment in Styling, Features, Performance at its best.
Powered by 2.2 Litre Crdi Diesel Engine with even more impressive Power Delivery delivering 182 BHP Power and 420 NM Torque - XUV700 Diesel is a Powerhouse in itself and even more powerful than immediate rivals as Tata Harrier, MG Hector, Jeep Compass too
Also now available in All Wheel Drive - Mahindra XUV700 offers Convenience of Drive yet to have a SUV with Off road dynamics.
Spacious Cabin with great Ingress and Egress with Adrenox Experience to transform a superior lifestyle for XUV700 Owner. In terms of Seating - XUV700 Ax7 Diesel is available in only 7 Seater Option
Available in 5 Colors: Everest White, Electric Blue, Midnight Black, Dazzling Silver and Red Rage
Below are Features list in all new Mahindra XUV700 Diesel Ax7 All Wheel Drive Automatic Transmission
Seating: 7 Seater
Safety: ABS EBD, 7 Airbags, ADAS Safety, Blind View Monitor, DVR DashCam, TPMS, LED DRL, Front Fog Light, Speed Sensitive Door Lock, Cornerng Lamp
Convenience: Dual Zone Automatic Climate Control with Clean Air with AC Vents on 2nd and 3rd Row, Power Steering with Tilt and Telescopic Steering, All 4 Power Window, Remote Central Lock, Push Button Ignition with Keyless Entry, 6 Way Electric Driver Seat with Memory Function, 360 Degree Camera, Wireless Charging, Automatic Headlight on in Low Visibility, Automatic Rain Sensing Wiper, One Touch Down on Driver Side Power Window, Center Seat Armrest, Electric ORVM Adjustable Headrest on Front and Rear, Steering Mount Control, Smart Door Handles, Dual HD Screen in Cabin
Styling: Panoramic Sunroof, LED Headlights, LED Tail Light, LED Turn Indicator, 10.25 Inch Digital Instrument Cluster in HD Setup, Electric Smart Door Handles, Leatherette Seats, Leather Wrapped Steering Wheel, Power Fold ORVM
Seating: 2nd Row with 60:40 One Touch Tumble, 2nd Row Seat with Rear Center Armrest
Wheels: 18 Inch Diamond Cut Alloy Wheel
Infotainment: 10.25 Inch Touchscreen Music System with Amazon Alxa Bult. Even has Wireless Android Auto and Apple Car Play with 3D Audio Sound System with 12 Speakers and 2 Year Adrenox Subscription
Mahindra XUV700 Discount Offers
Engine - 2198 cc
Power bhp - 182 bhp
Fuel Type - Diesel
ARAI Mileage - TBA
|Exshowroom Price Delhi:
|Rs. 2494901
|+ Road Tax & Registration:
|+ MCD Parking Fee in Delhi:
|Rs. 4000
|+ Comprehensive Car Insurance :
|Rs. 71266
|+ Dealer Logistics Charges **:
|Rs. 10000
|+ TCS (Tax at Source):
|Rs. 24949
|+ FASTag RFID Tag:
|Rs. 600
|Net On Road Price in Delhi:
|Rs. 2918592
|Optional Zero Dep Premium :
|Rs. 9331
|HP Endorsement Fees on RC:
|Rs. 1500
|HDFC Bank EMI for 3 year:
|Rs. 66250
|ICICI Bank EMI for 5 year:
|Rs. 42965
|SBI Finance EMI for 7 Year:
|Rs. 32637
Length - 4695 mm
Width - 1890 mm
Height - 1755 mm
Ground Clearance - 200 mm
Fuel Tank - 60 Litre
Boot Space - 600 Litre in 5 Seater, 150 Litre in 7 Seater Litre
XUV700 AX7 Luxury All Wheel Drive AT Features List
Advanced Features
Basic Standard Features
Road Tax & Registration Charges in Delhi / NCR
Delhi with MCD Fee - Rs.
Haryana - Faridabad, Gurgaon - Rs.
UP - Ghaziabad, Noida, Meerut - Rs.
Road Tax with Registration in Other Cities
Uttarakhand - Dehradun - Rs.
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Check Car On Road Prices in Other States
Check your Maximum Loan Eligibility basis Salary and EMI - Advanced Calculator
Avail Expert Support for any Queries related to Mahindra XUV700
Safety Features in Mahindra XUV700 AX7 Luxury All Wheel Drive AT
Here are Safety Features Analysis of Mahindra XUV700 AX7 Luxury All Wheel Drive AT car :-
» Tyre Pressure Monitoring System : TPMS is available
» Passive Safety : This Model equipped with Best Passive Safety Specs of 6 Airbags
» 360 Degree Camera : This Advanced Model is equipped with 360 Degree Camera
» Active Safety : Great, this Model is equipped with ABS, EBD and ESP too. Hill Hold Assist Safety is available
» ADAS : Excellent, ADAS Safety for Adaptive Cruise and Autonomous Features is available
Overall Specifications of Mahindra XUV700 AX7 Luxury All Wheel Drive AT
Mahindra XUV700 AX7 Luxury All Wheel Drive AT comes with 2198 cc Engine in Diesel delivering 182 bhp power .
Claimed ARAI mileage is TBA in Diesel.
Overall Unladen Ground Clearance is 200 mm . This car comes with Boot Space of 600 Litre in 5 Seater, 150 Litre in 7 Seater Litre and Fuel Tank Capacity is 60 Litre.

subset: commoncrawl


text:
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Albert LeGatt is the Roman Catholic Archbishop of St. Boniface in the Province of Manitoba, Canada. He was appointed Archbishop by Pope Benedict XVI on July 3, 2009, and received the Pallium on June 29, 2010.
Albert LeGatt
|July 3, 2009
|June 29, 2010
|Personal details
|Styles of
Albert LeGatt
|Reference style
|The Most Reverend
|Spoken style
|Your Grace
|Religious style
|Posthumous style
|not applicable
He was born on May 6, 1953, in Melfort, Saskatchewan, to Joseph and Emma LeGatt. Legatt studied at St. Boniface College (University of Manitoba), graduating in 1974 with his B.A. in Philosophy and French. Following graduation he served as a CUSO (Canadian University Services Overseas) volunteer in Ghana teaching French in a secondary school.
In 1977 he entered Le Grand Séminaire de Québec, and was ordained to the priesthood for the Diocese of Prince Albert on June 19, 1983, by Bishop Blaise Morand. For the next seventeen years (1983–2000) he served in a number of parishes in the diocese, as well as co-ordinator of the Diocesan Liturgical Commission, Director of Vocations and Consultor.
In 2000–2001 he pursued studies at the University of Notre Dame; and on October 5, 2001, he was consecrated as Bishop of Saskatoon in St. Patrick's Church by Bishop Morand, Archbishop James Hayes of Halifax and Archbishop Peter Mallon of Regina. He served the Diocese of Saskatoon for eight years until his appointment as Archbishop of St. Boniface in 2009.
LeGatt's Episcopal motto is Ut Unum Sint (Latin: That They May Be One) and is taken from Chapter 17, Verse 22 of The Gospel According to John.
He is Chairman of the Episcopal Commission for Liturgy and the Sacraments – English Sector for the Canadian Conference of Catholic Bishops and a Fourth Degree member of the Knights of Columbus.
External linksEdit
- Archbishop LeGatt's biography on the archdiocesan website.
- Archbishop LeGatt's listing at www.catholic-hierarchy.org.
- News release from the Diocese of Saskatoon on the appointment of Archbishop LeGatt as Archbishop of St. Boniface.
- Catholic Register article on the appointment of Archbishop LeGatt to the Archdiocese of St. Boniface.


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Jarvis AI writer is a machine-learning analysis engine that can write blog posts and emails. Not only can it write blog posts and emails, it can also write articles and emails for you. But how can you use it? Keep reading to discover how it can help you! These are some of the most popular features.
Jarvis AI writer is a machine learning analysis engine
Jarvis is a machine intelligence engine for content-marketing. Jarvis software is designed to produce high-quality content, passing Copyscape and other quality assurance testing. Jarvis creates content that is valuable to readers, not useless fluff. Jarvis also creates the most engaging landing pages and compelling Facebook and Google ads.
With its machine learning capabilities, Jarvis is able to write like a human, by analyzing a variety of sources and turning them into unique content. Jarvis can write a whole book in less than a week and sell it on Amazon. The software has quickly gained a large user base and offers a money-back guarantee. Jarvis also has a Facebook group where users can interact with the developers.
Jarvis is available for a free trial. Click here. The software uses a deep machine-learning algorithm to produce readable articles and webpages, with little to no human input. It can be used to create social media posts, personal blogs and YouTube video titles. Jarvis is highly recommended as a tool for copywriting. Jarvis is even available for larger teams and businesses.
Jarvis is an AI copywriting tool with 80% of words automatically generated. This software can create product descriptions, sales copy, email subject lines, as well as email subject lines. It integrates with popular SEO software Surfer. Its real-estate feature allows you to optimize content in search engines. It can also generate a unique headline. Jarvis offers three different plans. Each plan comes with a money back guarantee.
Jarvis' AI algorithm, Jarvis, is continually improving. Jarvis has a lot to offer. Jarvis can create long-form content including marketing copy and Facebook ads. Jarvis AI can even script YouTube videos! Jarvis AI is a powerful artificial intelligence that will eliminate the need for a human writer. Jarvis can now be used as a ghostwriter to help you run your business. The technology behind this new tool is quite impressive.
The Jasper AI editors is a powerful tool, similar to Google docs and Microsoft Word. The editor is compatible with a WYSIWYG editor that allows you to add headings, text formatting and keywords. Writing content is not difficult with the Jasper AI writer, but it does require more effort and creativity to create high-quality articles.
It can write blog posts
Jarvis is an AI service that allows you to hire a professional to create your blog posts. This AI-powered writer can create a variety of content including blog posts, product descriptions, and sales copy. It can also enhance your existing content. Jarvis can also be used to write blog posts. Learn more about Jarvis, and how it can boost your traffic.
Jarvis AI can also assist with content writing, digital marketing, and more. It can improve SEO and write social media copy. Jarvis AI is backed by a 7 day money back guarantee. Jarvis is easy to use. Logging into the Jarvis dashboard will allow you to access various templates and AI writers. The system is capable to write blogs, sales copies, and even SEO. The Jarvis software will even create an outline for your blog post. This can help to attract more readers and rank higher with Google.
In addition to writing blog posts, Jarvis also can write long-form content, such as articles and ebooks. In the Boss Mode, users can ask Jarvis to write an entire blog post. Jarvis can be used to help them get the best out of Jarvis. All they need is a topic, tone of voice and the AI will start to work. It can also produce fiction. Jarvis comes with an outline generator, which is highly recommended for blog posts that are listicles or "how-to" type.
The software comes with a free 10,000-credit trial and a list of templates to choose from. It can also help you with email marketing and generate creative subject lines. An AI tool can help you increase your traffic and sales by allowing your message to be the focus. In fact, Jasper can even generate ideas in over 26 languages. Jarvis AI is the perfect tool to help you find a new blog post.
The AI software promises to produce high-quality material. Jarvis comes with SEO tools as well as social media posting tools. Jarvis stands out among other AI copywriting tools due to its many features. Jarvis can help you write blog posts or create web content. Jarvis is an AI tool which can make blogging simple and productive. Imagine how much time you will save! Imagine being able to concentrate on your business while Jarvis creates content for you.
Jarvis is an artificial intelligence writer who can create blog posts. While you cannot hire a human blogger to do this, Jarvis can help you increase your traffic, SEO rankings, and overall website performance. This AI writer will write quality blog posts for you. The software uses an SEO-optimized keyword database to automatically optimize the content for search engine results. You can even hire it for your small business! Jarvis is an excellent solution for those who want great-looking blog posts without any hassle!
It can be used to help you write emails
AI tools use data to improve the quality of your writing. With the right input, you can use an AI writer to write a new subject line or email in minutes. These tools are not perfect. Before you send it to your target audience, make sure that you have checked the data generated by an AI tool. This will ensure that your emails are being written correctly. Here are some tips for using Jarvis AI writer to create emails.
o The software can work with different templates. You can choose a template that is tailored to your needs, depending on the type of document. For example, you can use a template for an email and let Jarvis write the body of the email. Jarvis AI also generates email subject line based on your content description. These results are more appealing and will increase click-throughs on Google search results. It even provides you with a subject line generator.
o Use templates or AI tools to make emails more engaging. Jarvis has templates for email subject lines, marketing angles, and persuasive bullet points. These templates can help market your startup. The templates can be used for email marketing that appeals to different audiences. The AI-based writing assistant will also assist you in creating long-form content like blog posts. The AI writer can help you create emails that can then be shared with your contacts.
o Use AI for personalizing emails AI will recognize the tone of your customers and craft a personalized reply. It can even personalize content to your customers' preferences. This will increase sales, as your clients will feel appreciated and valued. Jarvis AI writer is available to help you write emails and make sure your business stands out from the crowd. A well-written message can help build trust and brand loyalty with customers.
o Create a list containing all the emails you have sent. AI tools can help you save time and effort when writing emails. It will allow for you to create more emails in a shorter amount of time. It will also reduce repetitive tasks. An AI writer can be a great option if you don't have any ideas. You can give AI a prompt and provide text for it to use to create your email.
o Let Jarvis AI write for you. This tool works in just three steps. Select a template, then enter your data and let the writer get to work. The Starter Plan allows you to quickly generate copy. This plan works best for shorter-form content. All you have to do is enter the company name, description, and achievements. Jarvis AI writer creates an email with all your information.


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'America's Got Talent' is full of Golden Buzzer's this season! Perhaps it's like this every season, I just don't recall it happening this frequently.
Last night 9 year-old Celine Tam, sister to Dion, took the stage to sing a Michael Bolton classic. "How Am I Supposed To Live Without You" came out in 1989, when I was 2 years old. Nonetheless, Bolton would be impressed. Her performance impressed guest judge Laverne Cox so much, she got the Golden Buzzer!


text:
© Valery sharifulin/TASS
MOSCOW, April 6. /TASS/. More than 12 thousand people and more than 120 pieces of equipment, including the latest models of weapons, will be held may 9 on red square on parade in honor of the 73-th anniversary of Victory. This was reported by the "red Star" commander of the Land forces of Russia Oleg Salyukov.


text:
Remember the CSS Shenandoah
From: email@example.com
As the end of October approaches, we should remember Confederate Marine Corp Sergeant George Canning. He was the last active duty casualty of the War of Northern Perfidy. Sergeant Canning died on board CSS
Shenandoah October 30, 1865. Shenandoah was en route to Liverpool, England where it would surrender to the Royal Navy.
CSS Shenandoah wrote the final page of Confederate military history. On June 28, 1865, Shenandoah captured 10 Union whalers in the Bering Sea near the Arctic Circle. The crews of these ships were paroled, loaded onto two of the whalers, and told to sail for Hawaii. The other eight ships became the final addition to the Confederate Navy's artificial reef program.
CSS Shenandoah then sailed southward along the West Coast of North America contemplating an attack on San Francisco. Off the California Coast they hailed a British ship and were told the war was over. Captain Waddell ordered his crew to embark on one of the most grueling voyages in maritime history. They sailed around Cape Horn and headed for England where they arrived on November 6, 1865. The crew lived on salt pork and salt beef for months. Two crewmen died shortly before arriving, and Sgt. Canning was the last to go. Waddell was not going to let the Yankees have his ship.
On Oct. 30th please Google: YouTube, Celtic woman, Shenandoah. You will listen to a hauntingly beautiful violin solo of the song Shenandoah. Then close your eyes and try to imagine the CSS Shenandoah sailing through the Golden Gate with all of her sails out just looking for trouble. If only.
Sail on Shenandoah
Sail on ye brave hearts
Joe Jordan
Smyrna, GA


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KissRussianBeauty Review 2022: What Does This Site Have To Offer?
These days, there are countless dating sites where you can meet Slavic women for long-term relationships or marriage, but few of them actually manage to make it big. KissRussianBeauty is certainly one of the dating services whose popularity is not up for debate. But is it genuinely better than its competitors and what are its strongest features and drawbacks? Find out from our in-depth KissRussianBeauty review.
Signing up at KissRussianBeauty
In order to protect the safety of its female members, KissRussianBeauty requires all new male members to create an account before viewing any of the profiles or contacting the ladies. Becoming a member of Kiss Russian Beauty is very easy: you need to specify your name, birth date, who you are looking for on the site, your email address, and choose your password. You will also need to have your profile confirmed, and once that happens, you can begin browsing the site and reaching out to the ladies who caught your attention.
Design and interface
KissRussianBeauty.com is a well-designed site and you can see it from your first seconds as a member. The interface of the site is clear and uncluttered, so you can easily get anywhere you want. There are no distracting banners or flashy animations, which allows you to just enjoy the beautiful Slavic women and the communication. It's also worth noting that KissRussianBeauty can be used on any platform. If you like to communicate with the girls using your mobile phone, you can use the mobile version of the site or download an official app, which is available both for Android and iOS.
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There are several ways to discover the girls of KissRussianBeauty. The first one allows you to see all women who are currently online, and it's a great way to find a partner for a quick chat. The second one is the Smart Matches system, where the algorithm analyzes your own profile, your activity on the site, and the type of women you communicate with to suggest more female profiles you will like.
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The profiles of girls on KissRussianBeauty contains just enough information for you to decide whether you want to get to know them better. In each profile, you will find several profile photos, basic facts about the lady, such as her location, marital status, and interests, and a self-written intro where she explains what she likes and who she wants to meet. Many profiles also have the woman's answers to various dating questions where you can see whether you are a good match.
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There are dating sites that offer just a couple of communication options, which significantly limits your freedom of creativity when approaching the girls. This is not the case with Kiss Russian Beauty, where you can find a huge variety of contact features that can fit any dating style. The most popular features for contacting the women on KissRussianBeauty include:
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Like most reputable dating sites, KissRussianBeauty.com tries to make the site a safe space for both men and women. That is why the free features on Kiss Russian Beauty are limited. As a free member, you can browse the female profiles, view some of their profile pictures, add them to favorites, and say hi. For everything else, you will need credits in your account. Credits are available in several packages ranging from $15.99 for 2 credits to $399 for 100 credits. As a special one-time offer, you can purchase 2 credits for $3.99.
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Pros and cons of using KissRussianBeauty
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The verdict
If your goal is to meet a Russian or Ukrainian girl for long-term romance or marriage, Kiss Russian Beauty should be one of the first sites for you to try. The design and functionality of the service are constantly improving, but our number one reason for liking KissRussianBeauty is the huge variety of single Slavic women where you can effortlessly find a lady who just ticks every box for you.


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3 June 2018 1638 days ago
Summer School Cancelled
Dear all, we are very sorry to announce that the CLS Summer School 2018: Law and Technology, View from a Mountain, had to be cancelled because of organisational diffuculties. We tried our best to avoid this outcome but the trouble unfortunately prevailed in the end. Please accept our... more
23 May 2018 1649 days ago
2018 Lecturers: Jack Nea
Next lecturer is Jack Nea who is the Legal Research Manager for the Courts Service of Ireland. In this role Jack leads the legal research support function for the Irish Judiciary (comprising of approximately 40 researchers), and directs both long and short term legal research projects undertaken... more
5 May 2018 1667 days ago
2018 Lecturers: Zdeněk Kučera
Our lecturer Zdeněk Kučera, an expert on IT law, specializes in electronic contracting issues, negotiation of technology contracts, personal data protection, on-line marketing legal regulation, and also assessing of deployment of IT products on the market. Zdeněk Kučera is the guarantor... more
27 April 2018 1675 days ago
CEEMC success
Zuzana Vikarská and Jan Exner, our team members, celebrated great results of their own teams of young EU lawyers. more
22 April 2018 1680 days ago
2018 Lecturers: Ciarán Burke
Ciarán Burke is Professor of International Law and Director of International Legal Studies at the Friedrich Schiller Universität, Jena. He is also Director of Research at the Irish Law Reform Commission and a legal consultant for a number of international organisations. He has... more
18 April 2018 1684 days ago
Koen Lenaerts about CLS Summer School
Koen Lenaerts, the president of the ECJ, has paid a visit to Charles University. Before his lecture, the dean of the Law Faculty, Jan Kuklík, shared with students what professor Lanearts told him, which was nothing less than that he fondly remembers those great discussions he had at... more
12 April 2018 1690 days ago
2018 Lecturers: Radim Polčák
Let us introduce our next lecturer – Radim Polčák, a leading expert in IT law. Radim has JUDr. and Ph.D. degrees from Masaryk University in Brno. In 2013 he obtained a doc. (habilitation) for his research on "Internet and Metamorphoses of Law". He... more
28 March 2018 1705 days ago
2018 Lecturers: Jan M. Passer
Source: lidovky.cz Dear all, from this week on, we are starting to introduce our lecturers attending this year of CLS Summer School. We are very pleased that our first guest to be introduced is Jan M. Passer, judge of the General Court (EU). Jan holds degrees from Charles... more
23 December 2017 1800 days ago
9 December 2017 1814 days ago
Zuzana Vikarská and Barbara Budinská on Taricco II
Zuzana Vikarská, our academic director, and Barbora Budinská, our former participant, commented on the recent ECJ's judgment in Taricco II. more


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A Year of Bringing Shadows
We talk about a year of Shadowbringers, what we're filling our time with, and make some pretty baseless predictions about when 5.3 is finally coming out....
We've got good news and bad news for you in this episode. The good news is … that we have another fun episode for you to dive in to that gets oddly metaphysical for a couple of minutes and then fun again. The bad news is that we have to talk about the cancellation of the North American Fan Festival for 2020. Come process your grief with us, and once that's settled, settle in as we discuss the merits of the hunt trains. Are trains good? Bad? Inherently neutral, as are all machines used for locomotion? Give us a listen and then tell us what you think!
Gaming has been a core part throughout Poptarts life. Though her specialty is JRPGs, she tries to branch out and try new things. Most of her time gets put into the MMORPG, Final Fantasy XIV, but she still finds time to play new games.
Chip has a boring adult day job and then burns as much free time as possible playing the videos james. Fond of a variety of MMOs, JRPGs, and action games without active time button mashing.
Began the gaming life with a game boy to be kept entertained during a vacation in the early 90s and forgot to put it down after that. Currently, most gaming time goes into FFXIV. Doing her best to prove she's not a robot.
Playing all kinds of games since she was a kid, Deanna is a big fan of many different genres of games. But the main focus has been FFXIV since May 2018. She occasionally strays to FPS or RPGs whenever one catches her attention.
This week Exomatt talked to our guest Bullseyebenson about his gaming history, and the new Wraith Prestiege Skin.
Finally another Developer update for Project L, DNF Duel has breathed new life, and Street Fighter 6 has another beta.
SubtleFW and Fabulous join us this week to talk a lot about Protection Paladins.
Aloy heads to the ruins of Las Vegas to find POSEIDON.
Wondrous Tails is available on major podcast platforms. Search for it in your podcast platform of choice or use one of the links below.


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|Vintage Pulp
|Sep 19 2021
Into battle, me mateys! And tonight for those who survive—extra portions of organic Chai tea!
Today is International Talk Like a Pirate Day, not an official holiday, sadly. We asked the Pulp Intl. girlfriends what they'd do if they were pirates and the answers weren't pretty. Making all the men walk the plank was the most charitable of their thoughts, with swords and whips coming into play pretty quickly after that. Good thing we're only supposed to talk like pirates. Arrr... let's tone down the homicidal thoughts, girls.
Above and below is a collection of vintage paperbacks with women pirates. Well, maybe the woman on the cover of Rafael Sabatini's The Fortunes of Captain Blood isn't a pirate so much as someone defending herself. But anyone who can handle two pistols at once is an honorary pirate, at the least. We found eleven examples, and the cover art on display is by Harry Schaare, Rudolph Belarski, Barye Phillips, Paul Anna Soik, and others.
Frank ShayGardner F. FoxPhilip RushRobert E. HowardJeramie PriceDouglas BrownRaafael SabatiniInglis FletcherFrank G. SlaughterC.V. TerryAdam ShawHarry SchaareRudolph BelarskiBarye PhillipsPaul Anna Soikcover artcover collectionliterature
|Vintage Pulp
|Aug 20 2017
I hereby claim this land and everything in it for the British Emp—er, I mean for me!
Since we're on the subject of tropical islands (see below), here is a really beautiful cover for Adam Shaw's 1966 novel Pleasure Island. We first saw it at killercoversoftheweek, which informed us that it was painted by Ron Lesser, one of top illustrators of the mid-century era. Taking a close look at the art, it seems to us that the characters depicted are thinking two entirely different things at this moment.
Him: Wow, she's hot! I can't wait to have her. I think I'll call this place Pleasure Island.
Her: *sigh* It was nice while it lasted. Looks like we'll have to invent clothes and self defense classes now.
The locale in the story is one of the Marquesas Islands. Shaw's characters made a habit of stumbling upon natural wonders, because he followed Pleasure Island with Isle of Delight and Shipwrecked on Paradise. Safe to assume pleasures, delights, and paradise-like qualties were quickly ruined in each place. See more Lesser cover art here.


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(Image courtesy of Concept Laser.)
The aerospace industry has always been a difficult market for start-ups to break into, going all the way back to Glenn Curtiss and the surprisingly litigious Wright Brothers. Today, the challenge doesn't come from intellectual property or competing with giants like Boeing and Lockheed Martin—there's an entire supply chain feeding those behemoths, after all.
The real challenge for start-ups comes from the fact that the aerospace industry de…
Source URL: How Metal Additive Manufacturing is Changing the Aerospace Industry Landscape
Source Website: Advance Engineering – Engineering.com


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John Motson, or 'Motty' to me and you is now part of the talkSPORT family.
Having reversed a decision to retire from broadcasting, he will now be appearing on regularly when the new season begins.
"It didn't take long to come out of retirement because talkSPORT is a great place to talk football," he said.
"I'm looking forward to joining the team, commentating on this great game for sometime longer and speaking to a host of footballing legends for a series of shows for fans to re-live the best of football past and present."
Starting next month, his dulcet tones will be heard across the station, including live commentary of a selection talkSPORT's exclusive Premier League fixtures as well as further analysis and opinion.
Each Monday and Friday with Hawksbee and Jacobs (weekdays from 1pm) he will be reviewing and previewing all the football action plus later this year he will interview some of his favourite players in 'Motty's Magnificent XI' nostalgia shows.
First, though…
Why does he wear a sheepskin coat?
"I didn't set out to make the sheepskin coat anything special, but it just happened when I was at Wycombe in 1990 and the snow came down. I looked forlorn and it just stuck from then on," he said ahead of his 'retirement' at the end of last season, explaining he thinks he has worn 'nine or 10' over the years.
"I have always had them made to measure. Even when it is warm people ask where the coat is.
"I've always paid for my own. People think the BBC pay for them but they don't."
What team does he support?
Barnet, despite being born in Salford. "I have a long connection with the club. I was a junior reporter at the local paper (the Barnet Press) that covered their matches.
"I've always lived around Hertfordshire and have looked for their results ever since.
"I go and watch them whenever I can."
When was his first ever radio commentary?
Everton v Derby in December 1969. An Alan Ball goal helped the Toffees to the points. Talking about the game, he said: "When I came back to Euston on the train that night I was walking behind two guys who were blind and I heard one of them saying to the other: 'Wasn't it great to be able to listen to the game?'"


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I am an amateur photographer, with a passion for nature and and our amazing canals and wildlife.
All photographs are the work of myself and therefore if you wish you use any of my photographs would you please ask first. © Jo Lodge
Tuesday, 17 October 2017
Monday, 2 October 2017
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