minimum-effort driven dynamic faceted search in structured databases
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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
• Introduction• Faceted Search as an alternative to
Ranked Retrieval• Faceted Search in conjunction with
Ranking Functions• Evaluation• Related Work• Conclusion
Agenda
• 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
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.
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
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.
Broad Problem Areas1) 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
Broad Problem Areas2) 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.
Main Contributions• 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.
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 = Domi of Ai
FS as an Alternative to Ranked Retrieval
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:
Indg(Actor) = (2)(2-1)/2 + (1)(1-1)/2 + (1)(1-1)/2 = 1Indg(Genre) = Indg(Color) = (3)(3-1)/2 + (1)(1-1)/2 = 3Thus, Actor should be root.
• Cost of Tree: Average tree height =
Another decision tree Optimal decision tree
Cost of Optimal tree = (2+2+1+1)/4 = 1.5
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.
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
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.
Modeling Uncertainty in User knowledge
Decision tree with uncertainty models
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.
Designing a Fixed k-Facets Interface
• k-Facet Selection• Why extend ? • Problem:
– Given: database D, number k, uncertainties p i 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
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.
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
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.
Cost decreases with higher attribute probability
Cost increases with increasing database size
Average node creation time increases with increase in database size / width
Paper’s Algorithm performs better than Existing approach.
Average node creation time increases with increase in database size
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
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
Thank you!!!
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