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Relax and Adapt: Computing Top -k Matches to XPath Queries

Relax and Adapt: Computing Top -k Matches to XPath Queries. Amélie Marian (Columbia University) Joint work with: Sihem Amer-Yahia (AT&T Research) Nick Koudas (University of Toronto) Divesh Srivastava (AT&T Research). book. info. edition (paperback). author (Dickens). title

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Relax and Adapt: Computing Top -k Matches to XPath Queries

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  1. Relax and Adapt: Computing Top-k Matches to XPath Queries Amélie Marian (Columbia University) Joint work with: Sihem Amer-Yahia (AT&T Research) Nick Koudas (University of Toronto) Divesh Srivastava (AT&T Research)

  2. book info edition (paperback) author (Dickens) title (Great Expectations) book info edition (paperback) author (Dickens) title (Great Expectations) Example book book • Heterogeneous XML Data about books • Query: book[./info/title=“Great Expectations”] and [./info/author=“Dickens”] and [./edition=“paperback”] info info author (Dickens) title (Great Expectations) edition (paperback) title (Great Expectations) author (Dickens) Query root node: Distinguished node Amélie Marian - Columbia University

  3. book book info edition (paperback) info edition (paperback) author (Dickens) author (Dickens) title (Great Expectations) title (Great Expectations) XML Query Relaxation Query [Amer-Yahia et al. EDBT’02] • Tree pattern relaxations: • Leaf node deletion • Edge generalization • Subtree promotion book book Data edition? info info author (Dickens) title (Great Expectations) edition (paperback) title (Great Expectations) author (Dickens) Amélie Marian - Columbia University

  4. Top-k Queries over XML Data:Motivations and Challenges • Structure heterogeneity • Efficient identification of approximate matches • Top-k • Ranking of approximate matches based on similarity to query • Early pruning • Query processing cost • Cost increases with number of matches evaluated • Data explosion • Many approximate matches • XML path queries akin to joins • Prioritization to increase pruning Amélie Marian - Columbia University

  5. Contributions • Whirlpool: adaptive architecture and top-k query processing strategy for XPath queries • Goal: early pruning of non-top-k partial matches • Approach: partial matches may follow different plans, and may be at different stages of query execution • Real prototype implementation of Whirlpool • Instantiation of Whirlpool for various “routing strategies” and “prioritization” alternatives Amélie Marian - Columbia University

  6. Closely Related Work • Adaptive query processing • Eddies: • Dynamic query join plans to adapt to processing environment • No pruning • Adaptive top-k query processing • Upper: • Prioritization of partial matches based on maximum possible scores • Adaptive routing based on scores • No joins [Avnur and Hellerstein. SIGMOD’00] [Bruno et al. ICDE’01] Amélie Marian - Columbia University

  7. Outline • Whirlpool Architecture • Query Processing • Strategy • Alternatives • Evaluation Settings • Evaluation Results Amélie Marian - Columbia University

  8. Whirlpool Architecture book info edition (paperback) Router author (Dickens) title (Great Expectations) book server edition server title server info server author server Top-k Set Amélie Marian - Columbia University

  9. Whirlpool Architecture:Components • Top-k Set • Only one match with a given root node • Used for pruning • Complete matches are not processed further, incomplete matches are sent to the router • Router • Router Queue is based on partial matches maximum possible final scores • Dynamically choose which server to send partial match based on routing strategy Amélie Marian - Columbia University

  10. Whirlpool Architecture:Components • Root server: • Generates candidate matches • Node servers: • Maintain priority queue of partial matches • For each partial match that is processed: • Compute a set of extended partial (or complete) matches • Compute scores of new matches • Checks partial matches against current top-k set Amélie Marian - Columbia University

  11. Query Processing Alternatives • Prioritization Strategies (at each server) • FIFO • Current Score • Maximum Possible Next Score • Maximum Possible Final Score • Routing Decisions (at the router) • Static • Score-based • Likely to increase score the most • Likely to increase score the least • Size-based • Likely to produce the fewest matches Amélie Marian - Columbia University

  12. Evaluation Strategies • Lockstep (Static) • Partial matches follow same execution plan • Partial matches have gone through exactly the same number of operations • Whirlpool Single-threaded (Adaptive) • Partial matches adaptively routed • Process the partial match with the highest maximum final score (Query processing similar to Upper) • Only one partial match processed at a time • Whirlpool Multi-threaded (Adaptive) • Prioritization strategy at server decides which partial match to process next at server • System determines which server to process next Amélie Marian - Columbia University

  13. Evaluation Metrics • Parameters: • Query size • Document size • k • Parallelism • Scoring function (tf.idf proposed in paper) • Measures: • Query execution time • Number of server operations • Number of partial matches created Amélie Marian - Columbia University

  14. Evaluation Setting • C++ implementation, with POSIX threads • Default machine: • Red Hat 7.1 Linux • 1.4GHz dual processor • 2Gb RAM • XML Documents generated using XMark generating tool • XPath Queries chosen from XMark to illustrate different relaxations • XML nodes stored using Dewey encoding Amélie Marian - Columbia University

  15. Comparison of Adaptive Routing Strategies Whirlpool-S and Whirlpool-M perform approximately the same number of server operations Amélie Marian - Columbia University

  16. Static Routing Strategies vs. Best Adaptive Amélie Marian - Columbia University

  17. Effect of Parallelism Amélie Marian - Columbia University

  18. Varying Query Size and k (log scale) 60% 48% 20% For large queries and high values of k, Whirlpool-M performs less server operations that Whirlpool-S (and is faster even on a one-processor machine)! (27% less server operations for q3 k=75) Amélie Marian - Columbia University

  19. Varying Query Size and Document Size Almost twice as fast Amélie Marian - Columbia University

  20. Scalability Percentage of partial matches created by Whirlpool-M as a function of the maximum possible number of partial matches Amélie Marian - Columbia University

  21. Conclusions • Efficient adaptive top-k query processing strategy • Minimize number of partial matches evaluated • Benefit from parallelism with little threading overhead • Adapt to different environments • Score distribution • Selectivity distribution • Extensive experimental evaluation • Good scalability Amélie Marian - Columbia University

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