1. Intensional Associations in Dataspaces Marcos Vaz Salles Cornell University Jens Dittrich Saarland University Lukas Blunschi ETH Zurich ICDE 2010

2. Irrelevant results that sound like Kevin Spacey Potentially relevant results What is missing?

3. Potentially relevant results Colleagues Who acted together with Kevin Spacey Other Members of the Spacey Family in the Trade Other Folks from NJ in the Trade Items connected to Kevin Spacey by relationships

4. Potentially relevant results Movies in CommonSame Last Name Same Place of Birth Items connected to Kevin Spacey by relationships Samuel L. Jackson (37) Tom Hanks (34) Robin Williams (34) Dustin Hoffman (34) Morgan Freeman (32) John Graham Spacey (great-great-uncle!) Zach Braff, Adam Horovitz, Andrew Shue Joel Silver, Craig Kingsbury, Joseph Kraft Drew Rosenhaus, Lauryn Hill, Stacey Kent

5. The Problem Keywords are not enough –If item is not tagged, it is not returned –No meaningful definition of relatedness Relationships essential, but hard to get right –Searches do not include related items –Adding relationships to search queries hurts response time –The more flexible the definition of relatedness, the higher the cost

6. Our Solution Keywords are not enough –Declarative mini-language to define intensional associations Relationships essential, but hard to get right –Special class of neighborhood-enriched search queries over virtual associations –New index structure for neighborhood searches to process these queries efficiently

7. Association Trails A: Q L  Q R Example: Actors in the same movies moviesInCommon: //person[type=“actor”]  //person[type=“actor”], θ 1 = (  m l  L/movies: m l  R/movies) Meaning: Every element from query on the left has an intensional edge to θ- matching elements from query on the right θ(L, R) Join Predicate that relates elements from the left with elements from the right Search queries that select elements in the dataspace θ1θ1

8. Neighborhood Search Queries Combine search with pre-defined joins in association trails to get related items Examples: –Search for “kevin spacey” also returns colleagues who acted together, other family members, etc –Search for “actors who won the Oscar” also returns other actors strongly related to this set by virtual associations Search Results Related Items

9. Query Processing over Association Trails Intuition: Index at association trail definition time to avoid costly joins at runtime Naive Approach –Materialize all association trails into join index –Probe join index to get related items Naive Approach: Given m association trails and n items, index size is worst-case O(mn 2 )

10. Grouping-Compressed Index (GCI) Still materializes join, but in compressed form Takes advantage of redundancy in join output –O(mn) worst-case on equi-joins Intuition: samePlaceOfBirth θ(L,R)=(L.placeOfBirth = R.placeOfBirth) NJ CA NJ For each clique, only represent pivot, edges from pivot, and elements in clique

11. Grouping-Compressed Index (GCI) Technical challenge is to answer neighborhood queries without decompressing Intuition: Details on full version of the paper NJ CA NJ Search Results Probe pivot only once Search: actors who won the Oscar samePlaceOfBirth θ(L,R)=(L.placeOfBirth = R.placeOfBirth)

12. Experiments with IMDb Dataset Dataset: –IMDb biographies and filmographies –~2M people, ~1.5M movies Queries: –Original search returns a subset of people –Neighborhood processing includes all people related to original set through association trails Association trails: moviesInCommon, samePlaceOfBirth, sameHeight, sameLastName, sameBirthdate

13. Experiments with IMDb Dataset Indexing: over order- of-magnitude gains Querying: –Naive method very sensitive to selectivity –Querying compressed index comparable to uncompressed one with high selectivity

14. Related Work Neighborhood queries in dataspaces / IR: Dong & Halevy [SIGMOD 2007], Carmel et al. [SIGIR 2003] Intensional Associations: Srivastava & Velegrakis [SIGMOD 2007] Graph Indexing: Trissl and Leser [SIGMOD 2007], Neumann & Weikum [VLDB 2008], Weiss et al. [VLDB 2008], XML Recursive Queries: Declarative Networking & Datalog [SIGMOD 2006]

15. Conclusion Association Trails –Declarative mini-language to specify intensional associations in dataspaces Neighborhood Search Queries –Return associated items along with search results –Search combined with joins Grouping-Compressed Index (GCI) –Efficient scheme to index intensional associations and process neighborhood search queries Thank you!

16. Backup Slides

17. Association Trail Examples Actors in the same movies moviesInCommon: //person[type=“actor”]  //person[type=“actor”], θ 1 = (  m l  L/movies: m l  R/movies) Actors born in same place samePOB: //person[type=“actor”]  //person[type=“actor”], θ 2 = (L.placeOfBirth = R.placeOfBirth) θ1θ1 θ2θ2