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Chapter 13: Query Processing. Basic Steps in Query Processing
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1 Chapter 13: Query Processing! Overview! Measures of Query Cost! Selection Operation! Sorting! Join Operation! Other Operations! Evaluation of Expressions 13.1 Basic Steps in Query Processing 1. Parsing and translation 2. Optimization 3. Evaluation
2 Basic Steps in Query Processing (Cont.)! Parsing and translation! translate the query into its internal form. This is then translated into relational algebra.! Parser checks syntax, verifies relations! Evaluation! The query-execution engine takes a query-evaluation plan, executes that plan, and returns the answers to the query Basic Steps in Query Processing : Optimization! A relational algebra expression may have many equivalent expressions! E.g., σ balance<2500 ( balance (account)) is equivalent to balance (σ balance<2500 (account))! Each relational algebra operation can be evaluated using one of several different algorithms! Correspondingly, a relational-algebra expression can be evaluated in many ways.! Annotated expression specifying detailed evaluation strategy is called an evaluation-plan.! E.g., can use an index on balance to find accounts with balance < 2500,! or can perform complete relation scan and discard accounts with balance
3 Basic Steps: Optimization (Cont.)! Query Optimization: Amongst all equivalent evaluation plans choose the one with lowest cost.! Cost is estimated using statistical information from the database catalog "e.g. number of tuples in each relation, size of tuples, etc.! In this chapter we study! How to measure query costs! Algorithms for evaluating relational algebra operations! How to combine algorithms for individual operations in order to evaluate a complete expression! In Chapter 14! We study how to optimize queries, that is, how to find an evaluation plan with lowest estimated cost 13.5 Measures of Query Cost! Cost is generally measured as total elapsed time for answering query! Many factors contribute to time cost "disk accesses, CPU, or even network communication! Typically disk access is the predominant cost, and is also relatively easy to estimate. Measured by taking into account! Number of seeks * average-seek-cost! Number of blocks read * average-block-read-cost! Number of blocks written * average-block-write-cost "Cost to write a block is greater than cost to read a block data is read back after being written to ensure that the write was successful
4 Measures of Query Cost (Cont.)! For simplicity we just use number of block transfers from disk as the cost measure! We ignore the difference in cost between sequential and random I/O for simplicity! We also ignore CPU costs for simplicity! Costs depends on the size of the buffer in main memory! Having more memory reduces need for disk access! Amount of real memory available to buffer depends on other concurrent OS processes, and hard to determine ahead of actual execution! We often use worst case estimates, assuming only the minimum amount of memory needed for the operation is available! Real systems take CPU cost into account, differentiate between sequential and random I/O, and take buffer size into account! We do not include cost to writing output to disk in our cost formulae 13.7 Selection Operation! File scan search algorithms that locate and retrieve records that fulfill a selection condition.! Algorithm A1 (linear search). Scan each file block and test all records to see whether they satisfy the selection condition.! Cost estimate (number of disk blocks scanned) = b r "b r denotes number of blocks containing records from relation r! If selection is on a key attribute, cost = (b r /2) "stop on finding record! Linear search can be applied regardless of "selection condition or "ordering of records in the file, or "availability of indices
5 Selection Operation (Cont.)! A2 (binary search). Applicable if selection is an equality comparison on the attribute on which file is ordered.! Assume that the blocks of a relation are stored contiguously! Cost estimate (number of disk blocks to be scanned): " log 2 (b r ) cost of locating the first tuple by a binary search on the blocks "Plus number of blocks containing records that satisfy selection condition Will see how to estimate this cost in Chapter Selections Using Indices! Index scan search algorithms that use an index! selection condition must be on search-key of index.! A3 (primary index on candidate key, equality). Retrieve a single record that satisfies the corresponding equality condition! Cost = HT i + 1! A4 (primary index on nonkey, equality) Retrieve multiple records.! Records will be on consecutive blocks! Cost = HT i + number of blocks containing retrieved records! A5 (equality on search-key of secondary index).! Retrieve a single record if the search-key is a candidate key " Cost = HT i + 1! Retrieve multiple records if search-key is not a candidate key " Cost = HT i + number of records retrieved Can be very expensive! " each record may be on a different block one block access for each retrieved record
6 Selections Involving Comparisons! Can implement selections of the form σ A V (r) or σ A V (r) by using! a linear file scan or binary search,! or by using indices in the following ways:! A6 (primary index, comparison). (Relation is sorted on A) "For σ A V (r) use index to find first tuple v and scan relation sequentially from there "For σ A V (r) just scan relation sequentially till first tuple > v; do not use index! A7 (secondary index, comparison). "For σ A V (r) use index to find first index entry v and scan index sequentially from there, to find pointers to records. "For σ A V (r) just scan leaf pages of index finding pointers to records, till first entry > v "In either case, retrieve records that are pointed to requires an I/O for each record Linear file scan may be cheaper if many records are to be fetched! Implementation of Complex Selections! Conjunction: σ θ1 θ2... θn (r)! A8 (conjunctive selection using one index).! Select a combination of θ i and algorithms A1 through A7 that results in the least cost forσ θi (r).! Test other conditions on tuple after fetching it into memory buffer.! A9 (conjunctive selection using multiple-key index).! Use appropriate composite (multiple-key) index if available.! A10 (conjunctive selection by intersection of identifiers).! Requires indices with record pointers.! Use corresponding index for each condition, and take intersection of all the obtained sets of record pointers.! Then fetch records from file! If some conditions do not have appropriate indices, apply test in memory
7 Algorithms for Complex Selections! Disjunction:σ θ1 θ2... θn (r).! A11 (disjunctive selection by union of identifiers).! Applicable if all conditions have available indices. "Otherwise use linear scan.! Use corresponding index for each condition, and take union of all the obtained sets of record pointers.! Then fetch records from file! Negation: σ θ (r)! Use linear scan on file! If very few records satisfy θ, and an index is applicable to θ " Find satisfying records using index and fetch from file Sorting! We may build an index on the relation, and then use the index to read the relation in sorted order. May lead to one disk block access for each tuple.! For relations that fit in memory, techniques like quicksort can be used. For relations that don t fit in memory, external sort-merge is a good choice
8 External Sort-Merge Let M denote memory size (in pages). 1. Create sorted runs. Let i be 0 initially. Repeatedly do the following till the end of the relation: (a) Read M blocks of relation into memory (b) Sort the in-memory blocks (c) Write sorted data to run R i ; increment i. Let the final value of I be N 2. Merge the runs (N-way merge). We assume (for now) that N < M. 1. Use N blocks of memory to buffer input runs, and 1 block to buffer output. Read the first block of each run into its buffer page 2. repeat 1. Select the first record (in sort order) among all buffer pages 2. Write the record to the output buffer. If the output buffer is full write it to disk. 3. Delete the record from its input buffer page. If the buffer page becomes empty then read the next block (if any) of the run into the buffer. 3. until all input buffer pages are empty: External Sort-Merge (Cont.)! If i M, several merge passes are required.! In each pass, contiguous groups of M - 1 runs are merged.! A pass reduces the number of runs by a factor of M -1, and creates runs longer by the same factor. "E.g. If M=11, and there are 90 runs, one pass reduces the number of runs to 9, each 10 times the size of the initial runs! Repeated passes are performed till all runs have been merged into one
9 Example: External Sorting Using Sort-Merge External Merge Sort (Cont.)! Cost analysis:! Total number of merge passes required: log M 1 (b r /M).! Disk accesses for initial run creation as well as in each pass is 2b r "for final pass, we don t count write cost we ignore final write cost for all operations since the output of an operation may be sent to the parent operation without being written to disk Thus total number of disk accesses for external sorting: b r ( 2 log M 1 (b r / M) + 1)
10 Join Operation! Several different algorithms to implement joins! Nested-loop join! Block nested-loop join! Indexed nested-loop join! Merge-join! Hash-join! Choice based on cost estimate! Examples use the following information! Number of records of customer: 10,000 depositor: 5000! Number of blocks of customer: 400 depositor: Nested-Loop Join! To compute the theta join r θ s for each tuple t r in r do begin for each tuple t s in s do begin test pair (t r,t s ) to see if they satisfy the join condition θ if they do, add t r t s to the result. end end! r is called the outer relation and s the inner relation of the join.! Requires no indices and can be used with any kind of join condition.! Expensive since it examines every pair of tuples in the two relations
11 Nested-Loop Join (Cont.)! In the worst case, if there is enough memory only to hold one block of each relation, the estimated cost is n r b s + b r disk accesses.! If the smaller relation fits entirely in memory, use that as the inner relation. Reduces cost to b r + b s disk accesses.! Assuming worst case memory availability cost estimate is! = 2,000,100 disk accesses with depositor as outer relation, and! = 1,000,400 disk accesses with customer as the outer relation.! If smaller relation (depositor) fits entirely in memory, the cost estimate will be 500 disk accesses.! Block nested-loops algorithm (next slide) is preferable Block Nested-Loop Join! Variant of nested-loop join in which every block of inner relation is paired with every block of outer relation. for each block B r of r do begin for each block B s of s do begin for each tuple t r in B r do begin for each tuple t s in B s do begin Check if (t r,t s ) satisfy the join condition if they do, add t r t s to the result. end end end end
12 Block Nested-Loop Join (Cont.)! Worst case estimate: b r b s + b r block accesses.! Each block in the inner relation s is read once for each block in the outer relation (instead of once for each tuple in the outer relation! Best case: b r + b s block accesses.! Improvements to nested loop and block nested loop algorithms:! In block nested-loop, use M 2 disk blocks as blocking unit for outer relations, where M = memory size in blocks; use remaining two blocks to buffer inner relation and output " Cost = b r / (M-2) b s +b r! If equi-join attribute forms a key or inner relation, stop inner loop on first match! Scan inner loop forward and backward alternately, to make use of the blocks remaining in buffer (with LRU replacement)! Use index on inner relation if available (next slide) Indexed Nested-Loop Join! Index lookups can replace file scans if! join is an equi-join or natural join and! an index is available on the inner relation s join attribute "Can construct an index just to compute a join.! For each tuple t r in the outer relation r, use the index to look up tuples in s that satisfy the join condition with tuple t r.! Worst case: buffer has space for only one page of r, and, for each tuple in r, we perform an index lookup on s.! Cost of the join: b r + n r c! Where c is the cost of traversing index and fetching all matching s tuples for one tuple or r! c can be estimated as cost of a single selection on s using the join condition.! If indices are available on join attributes of both r and s, use the relation with fewer tuples as the outer relation
13 Example of Nested-Loop Join Costs! Compute depositor customer, with depositor as the outer relation.! Let customer have a primary B + -tree index on the join attribute customer-name, which contains 20 entries in each index node.! Since customer has 10,000 tuples, the height of the tree is 4, and one more access is needed to find the actual data! depositor has 5000 tuples! Cost of block nested loops join! 400* = 40,100 disk accesses assuming worst case memory (may be significantly less with more memory)! Cost of indexed nested loops join! * 5 = 25,100 disk accesses.! CPU cost likely to be less than that for block nested loops join Merge-Join 1. Sort both relations on their join attribute (if not already sorted on the join attributes). 2. Merge the sorted relations to join them 1. Join step is similar to the merge stage of the sort-merge algorithm. 2. Main difference is handling of duplicate values in join attribute every pair with same value on join attribute must be matched 3. Detailed algorithm in book
14 Merge-Join (Cont.)! Can be used only for equi-joins and natural joins! Each block needs to be read only once (assuming all tuples for any given value of the join attributes fit in memory! Thus number of block accesses for merge-join is b r + b s + the cost of sorting if relations are unsorted.! hybrid merge-join: If one relation is sorted, and the other has a secondary B + -tree index on the join attribute! Merge the sorted relation with the leaf entries of the B + -tree.! Sort the result on the addresses of the unsorted relation s tuples! Scan the unsorted relation in physical address order and merge with previous result, to replace addresses by the actual tuples "Sequential scan more efficient than random lookup Hash-Join! Applicable for equi-joins and natural joins.! A hash function h is used to partition tuples of both relations! h maps JoinAttrs values to {0, 1,..., n}, where JoinAttrs denotes the common attributes of r and s used in the natural join.! r 0, r 1,..., r n denote partitions of r tuples "Each tuple t r r is put in partition r i where i = h(t r [JoinAttrs]).! r 0,, r 1..., r n denotes partitions of s tuples "Each tuple t s s is put in partition s i, where i = h(t s [JoinAttrs]).! Note: In book, r i is denoted as H ri, s i is denoted as H si and n is denoted as n h
15 Hash-Join (Cont.) Hash-Join (Cont.)! r tuples in r i need only to be compared with s tuples in s i Need not be compared with s tuples in any other partition, since:! an r tuple and an s tuple that satisfy the join condition will have the same value for the join attributes.! If that value is hashed to some value i, the r tuple has to be in r i and the s tuple in s i
16 Hash-Join Algorithm The hash-join of r and s is computed as follows. 1. Partition the relation s using hashing function h. When partitioning a relation, one block of memory is reserved as the output buffer for each partition. 2. Partition r similarly. 3. For each i: (a) Load s i into memory and build an in-memory hash index on it using the join attribute. This hash index uses a different hash function than the earlier one h. (b) Read the tuples in r i from the disk one by one. For each tuple t r locate each matching tuple t s in s i using the in-memory hash index. Output the concatenation of their attributes. Relation s is called the build input and r is called the probe input Hash-Join algorithm (Cont.)! The value n and the hash function h is chosen such that each s i should fit in memory.! Typically n is chosen as b s /M * f where f is a fudge factor, typically around 1.2! The probe relation partitions s i need not fit in memory! Recursive partitioning required if number of partitions n is greater than number of pages M of memory.! instead of partitioning n ways, use M 1 partitions for s! Further partition the M 1 partitions using a different hash function! Use same partitioning method on r! Rarely required: e.g., recursive partitioning not needed for relations of 1GB or less with memory size of 2MB, with block size of 4KB
17 Handling of Overflows! Hash-table overflow occurs in partition s i if s i does not fit in memory. Reasons could be! Many tuples in s with same value for join attributes! Bad hash function! Partitioning is said to be skewed if some partitions have significantly more tuples than some others! Overflow resolution can be done in build phase! Partition s i is further partitioned using different hash function.! Partition r i must be similarly partitioned.! Overflow avoidance performs partitioning carefully to avoid overflows during build phase! E.g. partition build relation into many partitions, then combine them! Both approaches fail with large numbers of duplicates! Fallback option: use block nested loops join on overflowed partitions Cost of Hash-Join! If recursive partitioning is not required: cost of hash join is 3(b r + b s ) +2 n h! If recursive partitioning required, number of passes required for partitioning s is log M 1 (b s ) 1. This is because each final partition of s should fit in memory.! The number of partitions of probe relation r is the same as that for build relation s; the number of passes for partitioning of r is also the same as for s.! Therefore it is best to choose the smaller relation as the build relation.! Total cost estimate is: 2(b r + b s log M 1 (b s ) 1 + b r + b s! If the entire build input can be kept in main memory, n can be set to 0 and the algorithm does not partition the relations into temporary files. Cost estimate goes down to b r + b s
18 Example of Cost of Hash-Join customer depositor! Assume that memory size is 20 blocks! b depositor = 100 and b customer = 400.! depositor is to be used as build input. Partition it into five partitions, each of size 20 blocks. This partitioning can be done in one pass.! Similarly, partition customer into five partitions,each of size 80. This is also done in one pass.! Therefore total cost: 3( ) = 1500 block transfers! ignores cost of writing partially filled blocks Hybrid Hash Join! Useful when memory sized are relatively large, and the build input is bigger than memory.! Main feature of hybrid hash join: Keep the first partition of the build relation in memory.! E.g. With memory size of 25 blocks, depositor can be partitioned into five partitions, each of size 20 blocks.! Division of memory:! The first partition occupies 20 blocks of memory! 1 block is used for input, and 1 block each for buffering the other 4 partitions.! customer is similarly partitioned into five partitions each of size 80; the first is used right away for probing, instead of being written out and read back.! Cost of 3( ) = 1300 block transfers for hybrid hash join, instead of 1500 with plain hash-join.! Hybrid hash-join most useful if M >> bs
19 Complex Joins! Join with a conjunctive condition: r s θ1 θ2... θn! Either use nested loops/block nested loops, or! Compute the result of one of the simpler joins r θi s "final result comprises those tuples in the intermediate result that satisfy the remaining conditions θ 1... θ i 1 θ i θ n! Join with a disjunctive condition r θ1 θ2... θn s! Either use nested loops/block nested loops, or! Compute as the union of the records in individual joins r θ i s: (r θ1 s) (r θ2 s)... (r θn s) Other Operations! Duplicate elimination can be implemented via hashing or sorting.! On sorting duplicates will come adjacent to each other, and all but one set of duplicates can be deleted. Optimization: duplicates can be deleted during run generation as well as at intermediate merge steps in external sort-merge.! Hashing is similar duplicates will come into the same bucket.! Projection is implemented by performing projection on each tuple followed by duplicate elimination
20 Other Operations : Aggregation! Aggregation can be implemented in a manner similar to duplicate elimination.! Sorting or hashing can be used to bring tuples in the same group together, and then the aggregate functions can be applied on each group.! Optimization: combine tuples in the same group during run generation and intermediate merges, by computing partial aggregate values "For count, min, max, sum: keep aggregate values on tuples found so far in the group. When combining partial aggregate for count, add up the aggregates "For avg, keep sum and count, and divide sum by count at the end Other Operations : Set Operations! Set operations (, and ): can either use variant of merge-join after sorting, or variant of hash-join.! E.g., Set operations using hashing: 1. Partition both relations using the same hash function, thereby creating, r 1,.., r n r 0, and s 1, s 2.., s n 2. Process each partition i as follows. Using a different hashing function, build an in-memory hash index on r i after it is brought into memory. 3. r s: Add tuples in s i to the hash index if they are not already in it. At end of s i add the tuples in the hash index to the result. r s: output tuples in s i to the result if they are already there in the hash index. r s: for each tuple in s i, if it is there in the hash index, delete it from the index. At end of s i add remaining tuples in the hash index to the result
21 Other Operations : Outer Join! Outer join can be computed either as! A join followed by addition of null-padded non-participating tuples.! by modifying the join algorithms.! Modifying merge join to compute r s! In r s, non participating tuples are those in r Π R (r s)! Modify merge-join to compute r s: During merging, for every tuple t r from r that do not match any tuple in s, output t r padded with nulls.! Right outer-join and full outer-join can be computed similarly.! Modifying hash join to compute r s! If r is probe relation, output non-matching r tuples padded with nulls! If r is build relation, when probing keep track of which r tuples matched s tuples. At end of s i output non-matched r tuples padded with nulls Evaluation of Expressions! So far: we have seen algorithms for individual operations! Alternatives for evaluating an entire expression tree! Materialization: generate results of an expression whose inputs are relations or are already computed, materialize (store) it on disk. Repeat.! Pipelining: pass on tuples to parent operations even as an operation is being executed! We study above alternatives in more detail
22 Materialization! Materialized evaluation: evaluate one operation at a time, starting at the lowest-level. Use intermediate results materialized into temporary relations to evaluate next-level operations.! E.g., in figure below, compute and store σ ( account balance<2500 ) then compute the store its join with customer, and finally compute the projections on customer-name Materialization (Cont.)! Materialized evaluation is always applicable! Cost of writing results to disk and reading them back can be quite high! Our cost formulas for operations ignore cost of writing results to disk, so "Overall cost = Sum of costs of individual operations + cost of writing intermediate results to disk! Double buffering: use two output buffers for each operation, when one is full write it to disk while the other is getting filled! Allows overlap of disk writes with computation and reduces execution time
23 Pipelining! Pipelined evaluation : evaluate several operations simultaneously, passing the results of one operation on to the next.! E.g., in previous expression tree, don t store result of σ balance<2500 ( account )! instead, pass tuples directly to the join.. Similarly, don t store result of join, pass tuples directly to projection.! Much cheaper than materialization: no need to store a temporary relation to disk.! Pipelining may not always be possible e.g., sort, hash-join.! For pipelining to be effective, use evaluation algorithms that generate output tuples even as tuples are received for inputs to the operation.! Pipelines can be executed in two ways: demand driven and producer driven Pipelining (Cont.)! In demand driven or lazy evaluation! system repeatedly requests next tuple from top level operation! Each operation requests next tuple from children operations as required, in order to output its next tuple! In between calls, operation has to maintain state so it knows what to return next! Each operation is implemented as an iterator implementing the following operations " open() E.g. file scan: initialize file scan, store pointer to beginning of file as state E.g.merge join: sort relations and store pointers to beginning of sorted relations as state " next() E.g. for file scan: Output next tuple, and advance and store file pointer E.g. for merge join: continue with merge from earlier state till next output tuple is found. Save pointers as iterator state. " close()
24 Pipelining (Cont.)! In produce-driven or eager pipelining! Operators produce tuples eagerly and pass them up to their parents "Buffer maintained between operators, child puts tuples in buffer, parent removes tuples from buffer "if buffer is full, child waits till there is space in the buffer, and then generates more tuples! System schedules operations that have space in output buffer and can process more input tuples Evaluation Algorithms for Pipelining! Some algorithms are not able to output results even as they get input tuples! E.g. merge join, or hash join! These result in intermediate results being written to disk and then read back always! Algorithm variants are possible to generate (at least some) results on the fly, as input tuples are read in! E.g. hybrid hash join generates output tuples even as probe relation tuples in the in-memory partition (partition 0) are read in! Pipelined join technique: Hybrid hash join, modified to buffer partition 0 tuples of both relations in-memory, reading them as they become available, and output results of any matches between partition 0 tuples "When a new r 0 tuple is found, match it with existing s 0 tuples, output matches, and save it in r 0 "Symmetrically for s 0 tuples
25 Complex Joins! Join involving three relations: loan depositor customer! Strategy 1. Compute depositor customer; use result to compute loan (depositor customer)! Strategy 2. Computer loan depositor first, and then join the result with customer.! Strategy 3. Perform the pair of joins at once. Build and index on loan for loan-number, and on customer for customer-name.! For each tuple t in depositor, look up the corresponding tuples in customer and the corresponding tuples in loan.! Each tuple of deposit is examined exactly once.! Strategy 3 combines two operations into one specialpurpose operation that is more efficient than implementing two joins of two relations
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text:
"For you, I'll die!"
Beautiful, I listen to this all the time.
The only reason you didn't get a 10 from me is simple, your voice isn't perfect, but it is good enough for me to jam out to and really enjoy.
5/5 9/10
"For you, I'll die!"
Beautiful, I listen to this all the time.
The only reason you didn't get a 10 from me is simple, your voice isn't perfect, but it is good enough for me to jam out to and really enjoy.
5/5 9/10
Your music Isnt all the great anyways so dont bitch, and I highly doubt being a band they have time to rip off songs and not practice.
Heavens Fall, are you coming out with a CD? I want one =P you sound like a retail metal band.
Thanks man, yeah we are probably going to rerecord the tracks and add harmonies and such soon, then we will release it.
your intro is EXACTLY like my song "Fallen Hero"........ what the fuck??..... My song was produced in 2006 so...... look ill give you 1 star, but If your next song sounds like one of mine I'm going to come down on you like a sack of bricks do you understand me? Just so you know my shit is copyrighted so I would hate to do anything drastic if you tried to profit from this bro >.< btw i like your vox ;) goodluck
Yes because we have nothing better to do than surf newgrounds to rip off a song, that myself included, no one has ever heard of. Get a life dude.
I am Mike Meloni singer of heavens fall , and i just want to thank everyone for the very nice comments and support of our band , hope your on our myspace page for updates and new songs. myspace.com/heavensfallmusic . thx MM
lol- sounds like soil........
yep soil i hit the nail on the head.... i dont like soil or really this kinda pop like metal... but as far as the song my bias aside it is a pretty solid peice, i just it would have been more varied other than the little breakdown at 2:49 when your useing the broken chords-Picking...... but its nothing new unfortantly but all in all you have a pretty solid (cookie cutter) but solid non the less...... any ways thats just my input, maby go out on a limb alittle bit in stead of haveing a boreing drum pattern why not have some tom grooves during the breakdown instead of just hi-hats be creative with it, cus a key part of writeing a song is haveing every instrument be involving and not just have 1 stand out! thats my input! :) i hope its constructive.
Please contact me if you would like to use this in a project. We can discuss the details.

subset: commoncrawl


text:
Bad data can kill your sales and marketing efforts!
And, we can help clean up your existing data to be sure that when you are ready to market to clients, it has integrity and is up to date. Further, we can help with obtaining pertinent lists for your needs. We have trained staff qualified to assist you in this effort.
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Marketing Automation Systems, Email databases, donor management software, and CRMs are complicated tools. We can provide an analysis of your database in addition to training you and your staff how to run reports and make the most of your current systems.


text:
SEOUL, April 6 (Reuters) - South Korea's LG Electronics Inc said on Friday first-quarter operating profit likely rose 20 percent from a year earlier, beating market expectations.
LG estimated January-March profit at 1.1 trillion won ($1.03 billion) versus a Thomson Reuters StarMine SmartEstimate of 935 billion won from a poll of 16 analysts.
Revenue likely climbed 3 percent to 15.1 trillion won, compared with a 15.4 trillion won projected by SmartEstimate, which gives greater weight to recent estimates by analysts who are more consistently accurate.
The firm will disclose full results at the end of April. ($1 = 1,068.9000 won) (Reporting by Joyce Lee; Editing by Gopakumar Warrier)


text:
LEBANON - Security forces opened fire in the northeastern Lebanese city of Tripoli, killing one person after a crowd angry over the film set fire to a KFC and a Hardee's restaurant. About 25 people were wounded in the melee, including 18 policemen who were hit with stones and glass.
SUDAN - Several hundred protesters stormed the German Embassy in the capital, Khartoum, burning a car parked behind its gates and trash cans. Police fired tear gas, pushing the protesters outside the embassy's gates. There appeared to be no injuries to embassy staff and no apparent damage to the building. Most protesters dispersed, but a group marched to protest at the nearby British Embassy.
YEMEN - Security forces shot live rounds in the air and fired tear gas at a crowd of around 2,000 protesters trying to march to the U.S. Embassy in the capital, Sanaa. Police kept the crowd about a block away from the embassy. Friday's demonstration came a day after hundreds stormed the embassy compound and burned the American flag.
EGYPT - Riot police clashed with hundreds of protesters blocks away from the U.S. Embassy in Cairo, as the president broadcast an appeal to Muslims to protect embassies and tried to patch up strained relations with Washington. After weekly prayers, a crowd in Cairo's Tahrir Square tore up an American flag, and waved a black, Islamist flag. When protesters tried to move toward the embassy, ranks of police confronted them, firing tear gas.
IRAN - Thousands shouted 'Death to America' and 'Death to Israel' in Tehran in a demonstration after Friday prayers. Some burned the American and Israeli flags. State TV says similar protests were held in other Iranian cities.
BAHRAIN - More than 2,000 protesters chanted against the film and burned American and Israeli flags after Friday prayers in a Shiite mosque in Diraz, outside the capital, Manama. Security forces were absent, even though the area is a hotbed of opposition in Bahrain's 19-month Shiite-led uprising against the Sunni ruling system. Separately, Bahrain's Interior Ministry ordered media regulators to attempt to block access to the film clip in the Gulf kingdom.
IRAQ - Hundreds demonstrated in Baghdad's northern Sunni neighborhood of Azamaiyah, some shouting: 'No, no America! No, no to Israel,' and, 'We are ready to sacrifice ourselves for our Prophet.' Dozens also marched in Baghdad's Sadr City, a poor Shiite area in the capital's northeast. In the southern city of Basra, about 1,000 took to the streets and burned the American and Israeli flags. One banner said: 'Freedom doesn't mean offending two billion Muslims.'
TUNISIA - A crowd of several thousand demonstrators protested outside the US embassy in Tunis. Police respond to stone-throwing with tear gas. An AP reporter on the scene witnessed several people overcome by intense clouds of gas. An army helicopter flew overhead while armored vehicles protected the embassy.
ISRAEL - The Israeli police say about 400 people marched toward the U.S. consulate in east Jerusalem in protest over the prophet film. Demonstrators threw bottles and stones at police, who responded by firing stun grenades. Four protesters were arrested and the crowd was prevented from reaching the U.S. consulate.
WEST BANK - In the city of Nablus, about 200 people demonstrated against the film as Muslim clerics throughout the territory preached against it in Friday sermons.
SYRIA - About 200 protesters waved the Syrian flag and shouted anti-American slogans outside the long-closed U.S. Embassy in Damascus. The crowd held banners saying: 'He who curses the Prophet doesn't seek democracy' and 'a nation whose Prophet is Mohammad, would never kneel down.' The U.S. embassy has been closed since February because of the country's bloody conflict that has killed about 23,000 people.
AFGHANISTAN - About 1,500 protested in the eastern city of Jalalabad, shouting 'Death to America' and urged President Hamid Karzai to cut relations with the U.S.
PAKISTAN - Hundreds of hardline Muslims held peaceful protests against the film throughout Pakistan, shouting slogans and carrying banners criticizing the U.S. and those involved in the film. Police in Islamabad set up barricades and razor wire to prevent protesters from getting to the diplomatic enclave, where the U.S. Embassy and many other foreign missions are located. Protests were also held in Karachi, Peshawar and Lahore, where protesters shouted 'Down with America' and some burned the U.S. flag. About 200 policemen and barbed wire ringed the U.S. Consulate in Lahore.
GREAT BRITAIN - In London, around 250 protesters marched noisily but peacefully through Britain's capital to the U.S. embassy. The group, which called itself the 'Defenders of The Prophet,' held placards denouncing the U.S. and perceived Western imperialism.


text:
I'm a blur of ache after an hour with A in the gym. It seems I have a stone to lose and he is demanding to see what I eat. I don't eat badly (it seems to me) but I'm strongly aware of giving in to fish and chips once in a while so I am stalling him.
Just like the last time several phrases of 'A's are haunting me.
"I am very worried about this (indicating the roll of fat around my belly); this is actually dangerous."
"The next ten minutes will seem like an hour" (adjusting the gradient and speed of the running treadmill).
"So you go swimming. Do you actually take it seriously?"
"If you don't stretch after you go running your muscles will contract and you'll end up walking around like an old man."
It seems I have a mountain to climb, but I'm serious about this (after the 'dangerous' remark I'm actually a bit frightened) so I have booked ten sessions. In the meantime I take great heart from an old friend who has conquered her own mountain in fine style, and writes bravely about it here.


text:
GoRaleigh Bus Driver Retires After 47 Years
John Giles, known as Pops to his fellow drivers, has been a bus driver for the City of Raleigh area for 47 total years. Now, at the age of 70, he says it's time for him to retire. Originally from Raleigh, he started driving for the transit system on November 14, 1969, at 23 years of age. At that time Raleigh City Coach Lines ran the public bus service; now GoRaleigh is the City's transit provider.
When asked what made him want to start driving a bus Mr. Giles said his uncle used to drive an 18-wheeler. When he started his job in 1969 segregation was still prevalent in Raleigh. He took the job with the understanding that he may face harsh racism from the people he was providing transit service to, his passengers. While Downtown on lunch breaks Mr. Giles had to go to the basement of Hudson Belk to eat, not in the main restaurant with white patrons, and bathrooms were also segregated. In 1969 he was one of only 10 African-American drivers out of 52 total drivers operating a 40 bus fleet.
When Mr. Giles started driving, the buses had an ivory and green color scheme and no air conditioning. Bus fare was 25 cents and he had to manually count the money while using a changer. Over the last several years he has had first pick of bus runs because of his seniority. When asked what were his favorites he didn't want to pick just one, "I really love all of the routes, I enjoy people, but if I had to choose, route 4 (Rex) and route 22 (State Street)." His shift starts at 4 o'clock in the morning and ends around noon. So what does he like to do on his time off? "I love to hunt and fish," he said.
Mr. Giles has seen a lot of changes over his four decades of service. Population growth, traffic increases, shopping centers coming into existence. He was driving a bus before Crabtree or North Hills even existed. He's seen the name of transit change from the 1970s branded Capital Area Transit (CAT) to GoRaleigh in 2015. He's had two children, giving him five grandkids and one great grandkid. He's been married for 36 years to his wife, Debbie, who he met on the bus. He also obtained an HVAC certificate from Wake Technical Community College back in 1985.
One more thing, Mr. Giles is a three-time million mile driver, which means he's covered more than three million miles without ever having a non-preventable accident.
Mr. Giles' officially retires on Oct. 3. His career with the City of Raleigh will be celebrated on Thursday and Friday:
- Thursday, Sept. 29, at noon --- This is Mr. Giles' last run of the day. Friends and family will greet him at the Martin Street bus shelter area on the south side of Moore Square by the Acorn;
- Friday, Sept. 30, at 11 a.m. --- A celebration of Mr. Giles' career will be held in Room 123 at the GoRaleigh operations facility, 4104 Poole Rd.
Twitter & Instagram @GoRaleighNC; and on Facebook.com/RaleighRides.
For more information, contact
- Kelly Wright, Transit Marketing Specialist, 919-996-4042


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The info hub for the hub of global health
Farsi, fútbol, and football
Sanjay Chheda travels frequently, for work and for fun (we're not suggesting work isn't fun). Field Notes tip: We recommend you snag an invitation to dinner when he is in Seattle because preparing Indian and Persian cuisine is Sanjay's favorite version of relaxation.
Retired Col. David McCune grew up in tiny Prairie Grove, Arkansas, but after 20 years working at Madigan Army Medical Center, Washington is home. David's a solid Seahawks, Sounders, and Mariners fan, and when he's not cheering, he's riding. You may have seen him in the pack of Seattle to Portland bikers.
We're pleased to welcome Sanjay and David to the WGHA board in 2018!
Painless PrEP
Thanks to a new grant from USAID, PATH is working with people in Kenya, South Africa, and Uganda to advance a microarray patch that delivers HIV pre-exposure prophylaxis without the need for needles.
2 out of 3 ain't bad
Do you believe health is a human right? Are you ready to work on the front lines? Are you between 21 and 30? Okay, fine … know anyone who can answer yes to all three of those questions? If so, make sure they check out applications for the Global Health Corps fellowship. Cofounded by WGHA girl crush Barbara Bush, this group is leading a health equity movement.
O Captain! My Captain!
VillageReach's right-hand woman just stepped to the helm of the ship. Emily Bancroft will lead a global workforce of 170 people dedicated to taking health care to the last mile. Emily also joins a growing group of Global Health Seattleites commuting on the Bainbridge ferry—happy hour anyone?!
Crystal ball
Powerhouses at Seattle Children's, Fred Hutch, and the University of Washington launched the $50 million Institute for Precision Medicine. They're dedicated to making the crystal ball a reality—one that tells us what we'll get sick from years before we do or what specific drugs our bodies will best respond to.
Common sense
WGHA's founder and ten-year executive director, Lisa Cohen, joins Uncommon as a senior advisor. Lisa's 25-year journalism career, paired with her unparalleled passion for connecting people who really should know each other, bring anything but common star power to Uncommon's team. Get her on speed dial, now.
Water works
You may know that MSR Global Health helps bring clean water to communities with the SE200 Community Chlorine Maker. But did you know they're moving downstream? Thanks to the Humanitarian Innovation Fund grant, MSR is working to bring clean water to individual households in the developing world, too. Bravo.
The Power of Communication
Those involved with global health know the power of reliable data and effective communication. Fox Rothschild is a law firm made up of knowledgeable attorneys who embrace that philosophy. With 800 attorneys in 22 offices coast to coast, we have the resources it takes to stay ahead of developments in the rapidly changing international legal landscape. We're thought leaders, hosting more than 30 blogs, with contributors based in Seattle and across the country, designed to provide reliable guidance on pressing issues.
Find out what our attorneys are saying about topics that include Privacy Compliance & Data Security, HIPAA & Health Information Technology, Immigration, and more by clicking here.
Oops, we did it again
WGHA just launched another cohort of recent global health hires. And we hope you share an elevator ride with one of these 35 launchers, because they've got the perfect pitch. Here's just a few from our second cohort: Philip Breesch with the Bill & Melinda Gates Foundation, Sierra Petrosky with Bloodworks Northwest, and a big crew of 11 new Institute for Health Metrics and Evaluation staff, including researcher Benjamin Mayala.
Party people
We know global health work isn't always glamorous. Maybe you're rocking scrubs and safety goggles all day, trekking across rugged terrain to reach the most remote populations, or spending weekends in front of a computer screen to make sense of the data that drives us all.
WGHA's Pioneers of Global Health Awards Dinner and Auction gives you a few hours off and celebrates the critical work you do every day. Whether you were with us or are suffering from a bad case of FOMO, you can see pictures from the party and the video on YouTube.
"CDC is the common defense of the country against health threats. If we're going to be effective, then we have to be in the global health space."
— Dr. Brenda Fitzgerald, CDC Director, speaking with WGHA
during her quick Seattle visit this week.
Editor's note: And with our longest edition yet, that's a wrap! Field Notes is taking a break for the rest of 2017. Happy holidays to you, and we look forward to seeing you in the new year! Thank you for reading. We hope you'll share with your friends, too.
A new name for a law firm with a long history serving
the Seattle global health community.
Do you have a tip for Field Notes? Send it to Tiffany Cain, email@example.com
Copyright © 2017 Washington Global Health Alliance, All rights reserved.


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In May, Duane Mombourquette, IPAC Saskatchewan Board Member and Executive Director of the Saskatchewan Access & Privacy Branch, had the opportunity to correspond with Graham Smith about his role as the Deputy Commissioner and Director of Freedom Information on a wide range of issues relating to Freedom of Information legislation.
It's a mixed picture. The UK FOI Act covers a vast range of public bodies, from large government departments, all levels of local government, National Health Service, police and the state education sector. Ensuring that public authorities are aware of their FOI obligations is itself a challenge. Performance is generally good, with an increasing willingness to disclose information which can be released without any harm. Delay in responding is the biggest single issue and then if the request is refused it can take a while to pursue a complaint through the system. But even here, it's an improving picture.
The key success is the extensive use of the FOI to obtain information which previously was kept secret. There are numerous examples. Consumers have benefitted from the release of information about restaurant hygiene reports, vehicle test results and hospital infection rates. In addition there are many cases where local communities have got hold of information about key development and land use proposals affecting their area.
Great Britain is often portrayed as a leader in Open Government. Can you describe for our readers the state of Open Government in the United Kingdom? What has been accomplished and where is it headed?
The key here is proactive publication of information, usually through websites. The FOI Act provides for this through publication schemes. The ICO has approved a model scheme, backed by definition documents for specific kinds of public authorities, setting out what must be disclosed. The current government has made transparency a key theme of their coalition agreement. The open data agenda is being led by the Minister for the Cabinet Office, at the heart of government. For once there is a clear leadership message for the government in favour of greater transparency and the maximum disclosure of information compatible with good government. It is early days for seeing how this plays out in practice, but the initial signs are very positive.
Open Government can mean different things to different people in different locations. From Freedom of Information legislation, to proactive disclosures, to opening datasets to the public. Is there a perfect vision for open government? Is there an ideal state that we could be working towards?
I'd hesitate to describe a perfect vision, but I'm clear that it's made up of different components, not just one of those you've listed. Proactive disclosure, an enforceable FOI regime, open data programme and the re-use of datasets for public good are all complementary concepts. They each reinforce each other, so the vision is the maximum disclosure, with appropriate free re-use opportunities and an effective FOI regime for information which is not proactively disclosed.
What is your role in regard to Open Government initiatives in Great Britain? Does the Commissioner's Office become involved in Open Government initiatives directly? Is there an oversight role for Open Data or proactive disclosure?
This is an exciting area of change for us. So far we have been involved in discussions with officials in government who are taking the agenda forward. A new Act of Parliament, just recently passed, includes a provision for datasets published in accordance with a public authority's publication scheme to be released in machine-readable format if required. The Commissioner is responsible for enforcing these provisions, so now we are directly tied into the agenda by legislation, as well as constructive dialogue.
Making data available to the public proactively can be a very resource intensive exercise. It seems likely that as governments move more and more towards open data schemes, they will need to be selective in regard to where they put there efforts. How do public bodies in Britain and elsewhere decide where to focus their efforts? How do they determine what data to make available and what to hold on to perhaps until a Freedom of Information request is made?
Good records management is essential to the effective operation of any access to information regime. The National Archives gives excellent guidance on this in the UK and there is a wealth of useful advice on their website. With the exponential growth in the information we all produce and consume, it's important not only that proper records are retained and can be easily retrieved but that ephemeral, transient stuff is disposed of promptly and efficiently so systems don't get avoidably cluttered. Past FOI requests can give an indication of what might be demanded. It's not always easy to predict what the public will want to see, but if it's been legitimately and appropriately disposed of before the request comes in, there's little to be done except take a lesson for the future.
One of the challenges often faced when preparing data to share in an open data environment is the need to protect privacy while at the same time still providing data sets that are useful. How does Great Britain address this challenge? Do you see advances in this area in the future, whether through technological or policy?
This is a big issue for us, particularly as the Commissioner regulates data protection as well as FOI. With data mining and mashing techniques there are legitimate fears that individuals might be identifiable from apparently anonymous datasets and some sensitive personal information inadvertently disclosed. The ICO is currently working on an Anonymisation Code of Practice to help tackle this issue. A draft for public consultation will be issued very shortly on our website – www.ico.gov.uk Keeping up with, let alone keeping ahead of, technological advances is one of the greatest challenges in information management.
In the United Kingdom there has been some debate about the effectiveness of Freedom of Information legislation. On the one hand, former Prime Minister Tony Blair – whose government introduced the legislation, has written in his memoirs that the Act was a mistake, that it undermines sensible government. Others take a different view, suggesting the Act is working as intended. Is there anything to Mr. Blair's comments? Is it possible that governments change their behavior as a result of FOI – in part defeating the purposes of the Act, or do you think that on balance the Act is working as it should in Great Britain?
FOI has its enthusiasts and its critics. The Justice Committee of the UK's House of Commons is currently conducting a review of the impact and effectiveness of the FOI Act. Its conclusions will be very interesting. Certainly the evidence presented to the Committee has varied. Some suggest that there is a definite chilling effect on civil servants and ministers, in terms of policy discussions not being recorded or not being had at all for fear of disclosure under FOI. But there's little or no hard evidence of this. We certainly recognize and respect the need for safe space in which to development policy in sensitive areas and the need to protect international relations and national security. We believe our decisions do this, but we also respect the fact that others may take a different view of where the public interest lies. On the whole, I think the Act is working well. There will always be marginal cases and it's the controversial ones that get noticed, but these are exceptional. The right of appeal against an ICO decision to a Tribunal provides a further safeguard and ultimately there's a government veto to override a decision of the ICO or the Tribunal in the most exceptional circumstances.
What advice would you give to our readers back in Saskatchewan who may be interested in building an Open Government regime, whether in a small local government or a large public body?
To be clear about red lines, to recognize that once released data can't ever be retrieved, to have proper respect for the private lives of individuals and to appreciate that the potential for open data tomorrow will always be greater than it is today.
Graham Smith was interviewed by Duane Mombourquette, Executive Director of the Access & Privacy Branch with the Saskatchewan Ministry of Justice and Attorney General


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Hi, using VB 2008, I have a datagridview bound to a datasource and want to position to a row based on the value keyed by the user in a textbox. I can sort of accomplish this with one line of code on the textbox changed event i.e.:
Me.BindingSource.Position = Me.BindingSource.Find("ColumnName", TextBox1.Text)
which works great when the user keys in an exact match. Question is, how do I position to a partial match, i.e. the first match found, i.e. "ABC " might position to "ABCDEFG" if it is the first record alphabetically. I can't seem to find a decent example of how to do this.
Bill P.


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Tuesday, January 30, 2007
Emo Unearthed
Yes, the blog is finally back and so is that sad, emo mood it takes to sit here blogging. I am dressed appropriately in one of my favorite cardigans. What have I been doing since I posted my last blogilicious post? A serious amount of reading and baking. Cookies (chocolate cayenne pepper), bread (banana, orange cranberry), cakes (red velvet), brownies, pies (pumpkin, apple). I made lots of caramels and so much fudge that I still have some in my fridge from Christmas.
Posted by Christine at 7:20 PM
2 comments:
- Valerie said...
I want all those baked goods. Especially the red velvet cake. Don't offer me any!
Sorry that the sads have hit...
- Elinoire said...
Clearly you aren't baking enough happy drugs into your products. Surely there's a student around who can procure something for you?
Yeah, I wouldn't try that, either.
So, do all your kid's friends fight to come to your house for dinner (and DESSERT)? Because they should.
Subscribe to: Post Comments (Atom)


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Before we started our personal finance journey in 2015, it seemed that everything was an emergency.
Overspent in a category of our "budget?" Take it out of savings.
Car breaks down in an actual emergency? Take it out of savings.
We didn't differentiate. We didn't analyze. We just spent like the spendy spenders we were, which led to us consistently draining our savings account. Whether we saved $500 or $1,500, you can bet it was gone in a few months.
In retrospect, we probably spent because we lacked goals. Sure, we imagined that we would be doing something other than working W-2 jobs at some point in our lives, but we didn't put pen to paper to outline any specific goals.
"If you don't know where you are going, any road will get you there." – Lewis Carroll
Yep. This.
We lived for years without goals. We were somewhat happy, moving about in our day-to-day lives, doing things that our peers were doing…working…renovating…eating out…spending…
But it got old. It would have been nice to say it got old after six months, but it was much closer to six years. Six years before we took a hard look at everything we had to be thankful for to realize that we're wasting out time, money, and talent. It's a big world out there, lots of good to do.
Thus, we set a lofty goal in spring 2015: to be financially independent by May 18, 2019. For us, to be financially independent by that date means we have all of the following accomplished:
- completely, 100% free of debt;
- an emergency fund that covers one year's worth of expenses, about $25,000; and
- income from REI that covers our household budget.
Now, THIS is a specific goal. Ha!
We've been making progress on our debt. As of today, we're down to $10,679.54 in debt, so we project we'll be debt free by the end of March.
UPDATE: We're completely debt free, including our mortgage!
What's on the docket after the debt? Saving our emergency fund.
For two years, I've read countless blog posts on the subject of emergency funds. I just couldn't find the right solution to counter our behavioral problem. There are PLENTY of articles about savings accounts, but not enough about savings behaviors.
Some say that paying off debt is like saving money, so if we shift our debt-crushing behavior into another category, we should be OK.
However, our attempts to save money in an "emergency fund," i.e. the savings account at our local bank, have failed. We've failed to save an emergency fund throughout this journey to debt freedom, so we've not been saving an e-fund.
Bad, bad personal finance bloggers. *finger wag*
Yeah, we know that saving for emergencies is important.
Yeah, we know that we are taking an enormous risk by focusing on our debt rather than saving money first.
We are two months from debt freedom, so we're going to go ahead and roll the dice again. We'll wait to start saving for our emergency fund in April when we're debt free.
You might think it'll be easy to save the money given the rate at which we paid off our debt, and you might be right, but with our track record, we decided to come up with a new system for managing our emergency fund.
First, we stopped leaving our emergency fund in the personal savings account at our local bank. What a mistake this was for us! With a simple transfer via our online account, we could move money from our savings to our checking account, and we certainly took advantage of that accessibility.
Instead, we started using the personal savings account as an "escrow" account. Since we no longer have a mortgage, we have to save for taxes and insurance; we have the $1,800 saved we need for both. Each month, we have an automated transfer from checking to savings of $175. Knowing this personal savings account is our "escrow" account has been a game-changer. Seriously. All we did was give this account a name and we haven't touched the money since.
Second, we decided we'd create new budgets every month to track every dollar, a process we started this month. We're taking some advice we received last week about our budget and making some tweaks because every dollar really does matter; we can't be lax on budget categories because of perceived needs. Currently, we have a category in our budget for "student loan repayment," but we're changing that category to "emergency fund" in April. This is new for us. We never budgeted savings; instead, we would just move a few bucks from checking to savings at the end of the month. Not anymore. We're setting savings goals.
Third, we opened an account with an online savings bank where we will store our emergency fund. Ally is one of those online or branchless banks that exist in a way I don't think I'll ever understand, but I don't really need to. After considering all of the options suggested via Twitter, Ally seemed like a solid choice.
Here's the deal. If our emergency fund is easy to access, it'll be easy to use. If it's difficult to access, we don't bother. If we need a few extra bucks, we need to buckle down on the frugality front. In the event of a true emergency, like medical and auto, it's not like it's the end of the world that we have to wait a couple of days for Ally to transfer the money back to our personal checking account. Ally makes it just irritating enough to withdraw the money but not so difficult that we have to wait a week for the cash. And irritating is what we need. Irritating is what fosters behavior change.
Related: 3 Tips for Emergency Fund Savings
Fourth, we're going to make weekly transfers from our checking account to our Ally account during our budget meetings until we have $25,000 saved. We could automate our savings, but this hasn't worked either. A couple that saves together, stays together.
Whoa, back up. Don't personal finances experts recommend three to six months for an emergency fund?
True. Since we are not personal finance experts, I wouldn't question that logic and I'm sure it works for many people. But in the event that something catastrophic happens, like an accident, illness, or a job loss, we wanted a little extra cushion to keep us afloat. This is why I started calling the emergency fund our emergency/avenge death fund.
If something were to happen to me, for example, I wouldn't want Garrett to feel the need to rush back to work. Three months' worth of expenses is not enough to cover funeral costs, so he'd have a few days off before he'd have to return to work. How's that enough time to grieve? In the event of my demise, I want Garrett to be able to quit his job and fulfill the plans we set to travel the world and avenge my death, of course.
Saving a full year's worth of expenses means that after funeral costs, he'd have a few months of travel before he'd have to decide what he wants to do with the rest of his life, assuming he hasn't already decided to be Batman. Morbid? Yeah, but it's the reality. Life is short. Sometimes shorter than we know. No amount of money could replace a loved one, but I take comfort in knowing that this emergency/avenge death fund will make life a little easier for the living spouse.
Related Post: Get a will.


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The Pokemon Plot: How One Cartoon Inspired the Army to Dream Up a Seizure Gun• http://www.wired.com, By Spencer Ackerman
This wasn't the only method the Center suggested for taking down combatants. Other exotic, less-lethal weapons included a handheld laser gun for close-range "antiterrorist special operations roles"; a "flood" of network traffic that could overload servers and "elicit a panic in the civilian population"; and radio frequencies that could manipulate someone's body temperature and "mimic a fever."
The military needed weapons like these because TV news had hamstrung the military's traditional proclivities to kill its way to victory: It now lived in a world where "You don't win unless CNN says you win," the report lamented. But while the Pentagon still laments the impact of the 24/7 news cycle on the U.S. military, it hardly thinks less-lethal weapons are a solution to it. In fact, the U.S. has kept most of its electromagnetic arsenal off of the battlefield, in part because the idea of invisible pain rays would sound so bad coming out of an anchor's mouth.


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Prakash John – Bass
Legendary bassist for George Clinton's Parliament-Funkadelic, Lou Reed,Alice Cooper, James Brown, The Blues Brothers, Paul Shaffer, Junior Wells, Murray McLaughlin, Gene McLelland, Rory Block
David Tyson – Keyboards
International 'hit' producer, composer & keyboardist for Alannah Myles "Black Velvet", Amanda Marshall, Hall & Oates, Peter Cetera, Jude Cole to name but a few......
Dylan Burchell – Guitar
Up 'n Coming 18 year old guitar wiz
Julian Psihogios – Drums
21 year old drummer extraordinaire
To say that vocal sensation Jordan John is a more powerful and polished lead vocalist and multi-instrumentalist than many players twice his age would tell just a fraction of the story. Adept on drums, keyboards, guitar and bass, he has already compiled a longer list of credits, and displays more assuredness and maturity, than many performers twice his age.
Be it performing in front of 20,000 + as a lead singing, guitar wielding frontman, opening for The Queen of Soul, Aretha Franklin (at her personal request), jamming at age 16 on drums with the artist formally known as Prince, singing the American National Anthem at the 2011 Toronto Honda Indy, performing as interim bassist for famed Canadian rock trio, Wide Mouth Mason, Jordan's talent and energy is sure to delight and amaze both avid and casual music listeners.
Garnering praise from the who's who of the music industry such as world renown producer David Foster, Kenny "Babyface" Edmonds, John Mayall, Steve Jordan, Keith Richards, Robbie Robertson, you may begin to register the proper amount of astonishment his musicality can command


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1. On page seventy-one, Jude observes that her husband accused her of being a helicopter parent, all noise and movement, hovering too close to her children, but if that were true, he was a satellite, positioned so far up in the sky he needed a telescope to track the goings-on in his own home.
How does this sentence illustrate Jude's view of motherhood?
Is she right?
Is Miles unaware of what's going on in his children's lives?
How does Jude render Miles ineffective and what is the price for that?
Why does she ignore Miles's suggestions and advice? Why does he let her?
3. For years, Jude promised her children that they could "tell her anything, that she would pick them up at night with no questions asked." But when put to the test, she fails.
Can you understand why she disciplined her children for drinking?
What would you have done?