Supporting keyword queries over databases: A simpl{e/istic} first step

1. Supporting keyword queries over databases: A simpl{e/istic} first step Slide added after the class discussion • As you may have heard, Google struck deals with AZ, UT and some other states to make their public records databases searchable by “Google Users” • Clearly the databases are not going to be searched with SQL queries, but rather with keyword search. How does one do keyword search over database records? • One simple idea is to • Generate the universal relation of the database (i.e., de-normalize and create a single big table) • Write out each tuple as a simple HTML file • Support keyword queries on these HTML files • Looks like an obviously dumb idea (we are destroying the structure in the tuple by writing it out as an html file!) • So we need a “smart-sounding” name for the idea • We shall call it “SURFACING THE DEEP WEB” • We surface the deepweb into HTML pages since we have a very nice hammer in terms of keyword based search that can then be used on those pages…

3. Soft Joins..WHIRL [Cohen] • We can extend the notion of Joins to “Similarity Joins” where similarity is measured in terms of vector similarity over the text attributes. So, the join tuples are output in a ranked form—with the rank proportional to the similarity • Neat idea… but does have some implementation difficulties • Most tuples in the cross-product will have non-zero similarities. So, need query processing that will somehow just produce highly ranked tuples • Uses A*-search to focus on top-K answers • (See Surajit et. Al. CIDR 2005 who argue for a whole new query algebra to help support top-K query processing)

4. WHIRL queries • Assume two relations: review(movieTitle,reviewText): archive of reviews listing(theatre, movieTitle, showTimes, …): now showing

5. WHIRL queries • “Find reviews of sci-fi comedies [movie domain] FROM review SELECT * WHERE r.text~’sci fi comedy’ (like standard ranked retrieval of “sci-fi comedy”) • “ “Where is [that sci-fi comedy] playing?” FROM review as r, LISTING as s, SELECT * WHERE r.title~s.title and r.text~’sci fi comedy’ (best answers: titles are similar to each other – e.g., “Hitchhiker’s Guide to the Galaxy” and “The Hitchhiker’s Guide to the Galaxy, 2005” and the review text is similar to “sci-fi comedy”)

6. WHIRL queries • Similarity is based on TFIDF rare wordsare most important. • Search for high-ranking answers uses inverted indices….

7. Years are common in the review archive, so have low weight WHIRL queries • Similarity is based on TFIDF rare wordsare most important. • Search for high-ranking answers uses inverted indices…. - It is easy to find the (few) items that match on “important” terms - Search for strong matches can prune “unimportant terms”

8. WHIRL results • This sort of worked: • Interactive speeds (<0.3s/q) with a few hundred thousand tuples. • For 2-way joins, average precision (sort of like area under precision-recall curve) from 85% to 100% on 13 problems in 6 domains. • Average precision better than 90% on 5-way joins

9. WHIRL worked for a number of web-based demo applications. e.g., integrating data from 30-50 smallish web DBs with <1 FTE labor WHIRL could link many data types reasonably well, without engineering WHIRL generated numerous papers (Sigmod98, KDD98, Agents99, AAAI99, TOIS2000, AIJ2000, ICML2000, JAIR2001) WHIRL was relational But see ELIXIR (SIGIR2001) WHIRL users need to know schema of source DBs WHIRL’s query-time linkage worked only for TFIDF, token-based distance metrics  Text fields with few misspellimgs WHIRL was memory-based all data must be centrally stored—no federated data.  small datasets only WHIRL and soft integration

10. String Similarity Metrics • Tf-idf measures are not really very good at handling similarity between “short textual attributes” (e.g. titles) • String similarity metrics are more suitable • String similarity can be handled in terms of • Edit distance (# of primitive ops such as “backspace”, “overtype”) needed to convert one string into another • N-gram distance (see next slide)

11. N-gram distance • An n-gram of a string is a contiguous n-character subsequence of the string • 3 grams of string “hitchhiker” are • {hit, itc, tch, chh, hhi, hik, ike, ker} • “space” can be treated as a special character • A string S can be represented as a set of its n-grams • Similarity between two strings can be defined in terms of the similarity between the sets • Can do jaccard similarity • N-grams are to strings what K-shingles are to documents • Document duplicate detection is often done in terms of the set similarity between its shingles • Each shingle is hashed to a hash signature. A jaccard similarity is computed between the document shingle sets • Useful for plagiarism detection (see Turnitin software does it..)

12. Performance

13. Query Processing in Data Integration(Gathering and Using Source Statistics)

15. Query Optimization Challenges -- Deciding what to optimize --Getting the statistics on sources --Doing the optimization

16. What to Optimize • Traditional DB optimizers compare candidate plans purely in terms of the time they take to produce all answers to a query. • In Integration scenarios, the optimization is “multi-objective” • Total time of execution • Cost to first few tuples • Often, the users are happier with plans that give first tuples faster • Coverage of the plan • Full coverage is no longer an iron-clad requirement • Too many relevant sources, Uncontrolled overlap between the sources • Can’t call them all! • (Robustness, • Access premiums…)

17. Roadmap • We will first focus on optimization issues in vertical integration (“data aggregation” ) scenarios • Learning source statistics • Using them to do source selection • Then move to optimization issues in horizontal integration (“data linking”) scenarios. • Join optimization issues in data integration scenarios

18. Query Processing Issues in Data Aggregation • Recall that in DA, all sources are exporting fragments of the same relation R • E.g. Employment opps; bibliography records; item/price records etc • The fragment of R exported by a source may have fewer columns and/or fewer rows • The main issue in DA is “Source Selection” • Given a query q, which source(s) should be selected and in what order • Objective: Call the least number of sources that will give most number of high-quality tuples in the least amount of time • Decision version: Call k sources that …. • Quality of tuples– may be domain specific (e.g. give lowest price records) or domain independent (e.g. give tuples with fewest null values)

19. Issues affecting Source Selection in DA • Source Overlap • In most cases you want to avoid calling overlapping sources • …but in some cases you want to call overlapping sources • E.g. to get as much information about a tuple as possible; to get the lowest priced tuple etc. • Source latency • You want to call sources that are likely to respond fast • Source quality • You want to call sources that have high quality data • Domain independent: E.g. High density (fewer null values) • Domain specific E.g. sources having lower cost books • Source “consistency”? • Exports data that is error free

20. Learning Source Statistics • Coverage, overlap, latency, density and quality statistics about sources are not likely to be exported by sources! • Need to learn them • Most of the statistics are source and query specific • Coverage and Overlap of a source may depend on the query • Latency may depend on the query • Density may depend on the query • Statistics can be learned in a qualitative or quantitative way • LCW vs. coverage/overlap statistics • Feasible access patterns vs. binding pattern specific latency statistics • Quantitative is more general and amenable to learning • Too costly to learn statistics w.r.t. each specific query • Challenge: Find right type of query classes with respect to which statistics are learned • Query class definition may depend on the type of statistics • Since sources, user population and network are all changing, statistics need to be maintained (through incremental changes)

21. BibFinder Case Study See the bibfinder slides

22. BibFinder: A popular CS bibliographic mediator Integrating 8 online sources: DBLP, ACM DL, ACM Guide, IEEE Xplore, ScienceDirect, Network Bibliography, CSB, CiteSeer More than 58000 real user queries collected Mediated schema relation in BibFinder: paper(title, author, conference/journal, year) Primary key: title+author+year Focus on Selection queries Q(title, author, year) :- paper(title, author, conference/journal, year), conference=SIGMOD Case Study: BibFinder

26. Selecting top-K sources for a given query • Given a query Q, and sources S1….Sn, we need the coverage and overlap statistics of sources Si w.r.t. Q • P(S|Q) is the coverage (Probability that a random tuple belonging to Q is exported by source S) • P({S1..Sj}|Q) is the overlap between S1..Sj w.r.t. query Q (Probability that a random tuple belonging to Q is exported by all the sources S1..Sj). • If we have the coverage and overlap statistics, then it is possible to pick the top-K sources that will give maximal number of tuples for Q.

27. Computing Effective Coverage provided by a set of sources Suppose we are calling 3 sources S1, S2, S3 to answer a query Q. The effective coverage we get is P(S1US2US3|Q). In order to compute this union, we need the intersection (overlap) statistics (in addition to the coverage statistics) Given the above, we can pick the optimal 3-sources for answering Q by considering all 3-sized subsets of source set S1….Sn, and picking the set with highest coverage

28. Selecting top-K sources: the greedy way Selecting optimal K sources is hard in general. One way to reduce cost is to select sources greedily, one after other. For example, to select 3 sources, we select first source Si as the source with highest P(Si|Q) value. To pick the jth source, we will compute the residual coverage of each of the remaining sources, given the 1,2…j-1 sources we have already picked The residual coverage computation requires overlap statistics). For example picking a third source in the context of sources S1 and S2 will require us to calculate:

29. Challenges in gathering overlap statistics • Sources are incomplete and partially overlapping • Calling every possible source isinefficient and impolite • Need coverage and overlap statistics to figure out what sources are most relevant for every possible query! • We introduce a frequency-based approach for mining these statistics

30. BibFinder/StatMiner

31. Query List • Each query q corresponds to a • Vector of coverage/overlap • Statistics. If there are 3 sources • S1, s2, s3, we have: • [P(S1|q),P(S2|q), P(S3|q) • P(S1&S2|q), P(S2&S3|q) P(S1&S3|q) • P(S1&S2&S3|q) ] • A sparse vector with exponential • dimensions • By keeping thresholds on min • overlap, we can avoid remembering • small values • The larger the thresholds, the sparser • the vectors

32. Issues in Storing & Using Statistics • Storing statistics for each query is disadvantageous • Too many queries • Stored statistics can only be useful if the same query comes up again • Idea1: Focus on only frequently asked queries • Idea 2: Store statistics w.r.t. query classes • Generate query classes by clustering.. • When a new query comes, we can map it to some existing query classes • But Clustering directly on queries won’t work • Because we won’t know how to map a new query into existing query classes • Idea: First do “subspace” clustering—cluster attribute values • A query class is then defined as a cross product of attribute value clusters

33. AV Hierarchies and Query Classes

34. StatMiner A query is a vector of overlap statistics

35. Learned Conference Hierarchy

36. Using Coverage and Overlap Statistics to Rank Sources

37. Latency statistics(Or what good is coverage without good response time?) • Sources vary significantly in terms of their response times • The response time depends both on the source itself, as well as the query that is asked of it • Specifically, what fields are bound in the selection query can make a difference • ..So, learn statistics w.r.t. binding patterns

38. Query Binding Patterns • A binding pattern refers to which arguments of a relational query are “bound” • Given a relation S(X,Y,Z) • A query S(“Rao”, Y, “Tom”) has binding pattern bfb • A query S(X,Y, “TOM”) has binding pattern ffb • Binding patterns can be generalized to take “types” of bindings • E.g. S(X,Y,1) may be ffn (n being numeric binding) and • S(X,Y, “TOM”) may be ffs (s being string binding) • Sources tend to have different latencies based on the binding pattern • In extreme cases, certain binding patterns may have infinite latency (i.e., you are not allowed to ask that query) • Called “infeasible” binding patterns

39. (Digression) • LCWs are the “qualitative” versions of quantitative coverage/overlap statistics • Feasible binding patterns are “qualitative” versions of quantitative latency statistics

40. Binding-specific latency stats are more effective

41. Combining coverage and response time • Qn: How do we define an optimal plan in the context of both coverage/overlap and response time requirements? • An instance of “multi-objective” optimization • General solution involves presenting a set of “pareto-optimal” solutions to the user and let her decide • Pareto-optimal set is a set of solutions where no solution is dominated by another one in all optimization dimensions (i.e., both better coverage and lower response time) • Another idea is to combine both objectives into a single weighted objective

42. It is possible to optimize for first tuples

43. Different “kinds” of plans