Minimum-Effort Driven Dynamic Faceted Search in Structured Databases

1. Presented by: Sandeep Chittal Minimum-Effort Driven Dynamic Faceted Search in Structured Databases Authors: Senjuti Basu Roy, Haidong Wang, Gautam Das, Ullas Nambiar, Mukesh Mahanja

2. Introduction Faceted Search as an alternative to Ranked Retrieval Faceted Search in conjunction with Ranking Functions Evaluation Related Work Conclusion Agenda

3. Paradigm: Suggest facets to drill down into database such that the cost of navigation in minimized. Facet selection based on: Ability to rapidly drill down to most promising tuples. Ability of user to provide desired values for the facets. Proposed dynamic technique: Ask user question/s on different facets Dynamically fetch next most promising set of facets based on user response Repeat above steps Faceted Search

4. Primary problem • Facilitate effective search for data records within vast data warehouses. • Sub problems: • Non-unique identifier for relevant tuple • Partial information Examples: Bank scenario and Car Buyer scenario • Necessity of an effective search procedure.

5. Approaches for tuple search • Ranked Retrieval from databases • Rank and retrieve top-k most relevant tuples that satisfy given conditions. Example: Selection of Car • Faceted search in databases • Drill down to the tuple via different facets of dataset. Example: search for pic of Great Wall of China

6. Main Goal of paper • Explore opportunities of adapting principles of faceted search paradigm for tuple search in structured database. • Motivation: • Structured databases associated with rich meta-data. (tables, attributes, dimensions, domain ranges, …) • Challenge: • To determine best suited attributes for enabling faceted search interface from abundance of meta-data.

7. Broad ProblemAreas • Faceted Search as an alternative to Ranked Retrieval • No tuple relevance and ranking function available • Develop dialog with user to judiciously next facets dynamically. • Metric of effort: • Expected no. of queries user has to answer to reach tuples of interest. • Idea proposed: • Cost model for fast tuple search assuming that attributes are associated with uncertainties

8. Broad ProblemAreas • Faceted Search that leverages Ranking Functions • Question: • Can faceted search work in conjunction with ranking functions? • Complications: • Ranking functions impose skew over user preferences. • Reevaluation of ranking function necessary as the faceted search progresses. • Benefits: • Focused retrieval as well as drill-down flexibility.

9. MainContributions • Initiate research into problem of automated faceted discovery for enabling minimal effort browsing of tuples in structured databases. • Extend methods to work in conjunction with ranking functions for tuples.

10. FS as an Alternative to Ranked Retrieval • Notations: • D be a relational table • tuples set, D= {t1, t2, . . . , tn} • Attributes set, A = {A1,A2, . . . , Am} • Each with Ai domain Domi • Task: • To build a decision tree which distinguishes each tuple testing attribute values • Node = Ai • Edge = Domiof Ai

11. FS as an Alternative to Ranked Retrieval

12. where ht(ti) = height of leaf ti • Approach: • Make the attribute that distinguishes max no. of pairs of tuples as the root of the tree. • Intuition: • Select root that minimizes no. of indistinguishable pairs of tuples. • Function formulated: • Cost of Tree: Average tree height = Indg(Actor) = (2)(2-1)/2 + (1)(1-1)/2 + (1)(1-1)/2 = 1 Indg(Genre) = Indg(Color) = (3)(3-1)/2 + (1)(1-1)/2 = 3 Thus, Actor should be root.

13. Another decision tree Optimal decision tree Cost of Optimal tree = (2+2+1+1)/4 = 1.5

14. Other Attribute Selection Procedures • Information gain heuristic produces different trees than the approach of minimizing indistinguishable pairs of tuples. • Select facet with largest information gain.

15. Difference between prior and papers Algorithm • Prior Algorithms: • Designed for classification problem • Maximize classification accuracy • Avoid over-fitting • This paper’s Algorithm: • Build full decision trees • Minimize average root-to-leaf path lengths

16. Comparing against PCA • PCA developed for dimensionality reduction in numerical datasets. • Our case: reduce from 3 to 2 attributes. • Cost to retain (Genre, Color) = 2 • Cost to retain (Actor, Genre) = 1 • Retain ones with smallest modes.

17. Modeling Uncertainty in User knowledge Decision tree with uncertainty models

18. Single Facet Based Search Algorithm • Obscure attribute that has little chance of being answered correctly by most users, but is otherwise very effective in distinguishing attributes, will be overlooked in favor of other attributes in the decision tree construction.

19. Designing a Fixed k-Facets Interface • k-Facet Selection • Why extend ? • Problem: • Given: database D, number k, uncertainties pi for attributes Ai • Select k attributes such that expected no. of tuples that can be distinguished is maximized. • Overall Idea: • Given: set A’ of k’ attributes • Select next attribute Al such that expected number of pairs of tuples that cannot be distinguished by A’ U {Al} is minimized

20. Implementation techniques • If database is static • Pre-compute decision trees • If database is dynamic • Construct partial tree with few look-ahead nodes • If database is highly dynamic (constant updates) • Persist with decision tree created at start • Fresh construction deferred to reasonable intervals.

21. FS in Conjunction with Ranking Functions • Ranking functions help to focus retrieval according to user selection • Cost of Decision Tree: • Facet Selection Algorithm: • Comparison against other Attribute Selection Procedures

22. Evaluation • Cost • Average no. of user interactions (facets selected) before tuple is identified • Time • Complexity of node creation step of tree building • Comparison of Selection techniques • Existing techniques with paper’s approach.

23. Cost decreases with higher attribute probability

24. Cost increases with increasing database size

25. Average node creation time increases with increase in database size / width

26. Paper’s Algorithm performs better than Existing approach.

27. Average node creation time increases with increase in database size

28. Difference of approach from prior work • Considers uncertainty models (inability of user to answer certain attributes) • Decision-tree based and depends on user interaction • Algorithms can work in conjunction with available ranking functions

29. Conclusion • Tackled problem of building faceted search interfaces over enterprise data warehouses for providing minimal effort tuples navigation solution • Selection of facets based on: • Ability to rapidly drill down most promising tuples • Ability of user to provide desired values for the facet • Provided solutions that can consider bias over tuple introduced by ranking function • Future work: • Techniques to work with multi-table databases • Faceted interfaces that span both structured and unstructured data sources

30. Thank you!!!