Supporting Queries with Imprecise Constraints Ullas Nambiar Dept. of Computer Science University of California, Davis Subbarao Kambhampati Dept. of Computer Science Arizona State University 18 th July, AAAI -06, Boston, USA [WebDB 2004; VLDB 2005 (d); WWW 2005 (p); ICDE 2006]

Supporting Queries with Imprecise Constraints Dichotomy in Query Processing Databases User knows what she wants User query completely expresses the need Answers exactly matching query constraints IR Systems User has an idea of what she wants User query captures the need to some degree Answers ranked by degree of relevance Autonomous Un-curated DB Inexperienced, Impatient user population

Supporting Queries with Imprecise Constraints Why Support Imprecise Queries ? Want a ‘sedan’ priced around $7000 A Feasible Query Make =“Toyota”, Model=“Camry”, Price ≤ $7000 What about the price of a Honda Accord? Is there a Camry for $7100? Solution: Support Imprecise Queries  ………  1998  $6500  Camry  Toyota  2000  $6700  Camry  Toyota  2001  $7000  Camry  Toyota  1999  $7000  Camry  Toyota

Supporting Queries with Imprecise Constraints Others are following …

Supporting Queries with Imprecise Constraints The Problem: Given a conjunctive query Q over a relation R, find a set of tuples that will be considered relevant by the user. Ans(Q) ={x|x Є R, Rel(x|Q,U) >c} Constraints – Minimal burden on the end user – No changes to existing database – Domain independent What does Supporting Imprecise Queries Mean? Autonomous Un-curated DB Inexperienced, Impatient user population

Supporting Queries with Imprecise Constraints Assessing Relevance Function Rel(x|Q,U)  We looked at a variety of non-intrusive relevance assessment methods – Basic idea is to learn the relevance function for user population rather than single users  Methods – From the analysis of the (sample) data itself Allows us to understand the relative importance of attributes, and the similarity between the values of an attribute  [ICDE 2006;WWW 2005 poster] – From the analysis of query logs Allows us to identify related queries, and then throw in their answers  [WIDM 2003; WebDB 2004] – From co-click patterns Allows us to identify similarity based on user click pattern  [Under Review]

Supporting Queries with Imprecise Constraints Our Solution: AIMQ

Supporting Queries with Imprecise Constraints The AIMQ Approach [For the special case of empty query, we start with a relaxation that uses AFD analysis]

Supporting Queries with Imprecise Constraints An Illustrative Example Relation:- CarDB(Make, Model, Price, Year) Imprecise query Q :− CarDB(Model like “Camry”, Price like “10k”) Base query Q pr :− CarDB(Model = “Camry”, Price = “10k”) Base set A bs Make = “Toyota”, Model = “Camry”, Price = “10k”, Year = “2000” Make = “Toyota”, Model = “Camry”, Price = “10k”, Year = “2001”

Supporting Queries with Imprecise Constraints Obtaining Extended Set  Problem: Given base set, find tuples from database similar to tuples in base set.  Solution: – Consider each tuple in base set as a selection query. e.g. Make = “Toyota”, Model = “Camry”, Price = “10k”, Year = “2000” – Relax each such query to obtain “similar” precise queries. e.g. Make = “Toyota”, Model = “Camry”, Price = “”, Year =“2000” – Execute and determine tuples having similarity above some threshold.  Challenge: Which attribute should be relaxed first? – Make ? Model ? Price ? Year ? Solution: Relax least important attribute first.

Least Important Attribute Definition: An attribute whose binding value when changed has minimal effect on values binding other attributes. Does not decide values of other attributes Value may depend on other attributes E.g. Changing/relaxing Price will usually not affect other attributes but changing Model usually affects Price Dependence between attributes useful to decide relative importance Approximate Functional Dependencies & Approximate Keys  Approximate in the sense that they are obeyed by a large percentage (but not all) of tuples in the database Can use TANE, an algorithm by Huhtala et al [1999]

Supporting Queries with Imprecise Constraints Deciding Attribute Importance  Mine AFDs and Approximate Keys  Create dependence graph using AFDs – Strongly connected hence a topological sort not possible  Using Approximate Key with highest support partition attributes into – Deciding set – Dependent set – Sort the subsets using dependence and influence weights  Measure attribute importance as CarDB(Make, Model, Year, Price) Decides: Make, Year Depends: Model, Price Order: Price, Model, Year, Make 1- attribute: { Price, Model, Year, Make} 2-attribute: {(Price, Model), (Price, Year), (Price, Make).. } Attribute relaxation order is all non- keys first then keys Greedy multi-attribute relaxation

Tuple Similarity Tuples obtained after relaxation are ranked according to their similarity to the corresponding tuples in base set where Wi = normalized influence weights, ∑ Wi = 1, i = 1 to |Attributes(R)| Value Similarity Euclidean for numerical attributes e.g. Price, Year Concept Similarity for categorical e.g. Make, Model

Supporting Queries with Imprecise Constraints Categorical Value Similarity  Two words are semantically similar if they have a common context – from NLP  Context of a value represented as a set of bags of co-occurring values called Supertuple  Value Similarity: Estimated as the percentage of common {Attribute, Value} pairs – Measured as the Jaccard Similarity among supertuples representing the values ST(Q Make=Toy ota ) ModelCamry: 3, Corolla: 4,…. Year2000:6,1999:5 2001:2,…… Price5995:4, 6500:3, 4000:6 Supertuple for Concept Make=Toyota JaccardSim(A,B) =

August 15 th 2005Answering Imprecise Queries over Autonomous Databases Value Similarity Graph Ford Chevrolet Toyota Honda Dodge Nissan BMW

Supporting Queries with Imprecise Constraints Empirical Evaluation  Goal – Evaluate the effectiveness of the query relaxation and similarity estimation  Database – Used car database CarDB based on Yahoo Autos CarDB( Make, Model, Year, Price, Mileage, Location, Color) Populated using 100k tuples from Yahoo Autos – Census Database from UCI Machine Learning Repository Populated using 45k tuples  Algorithms – AIMQ RandomRelax – randomly picks attribute to relax GuidedRelax – uses relaxation order determined using approximate keys and AFDs – ROCK: RObust Clustering using linKs (Guha et al, ICDE 1999) Compute Neighbours and Links between every tuple  Neighbour – tuples similar to each other  Link – Number of common neighbours between two tuples Cluster tuples having common neighbours

Supporting Queries with Imprecise Constraints Efficiency of Relaxation Average 8 tuples extracted per relevant tuple for Є =0.5. Increases to 120 tuples for Є=0.7. Not resilient to change in Є Average 4 tuples extracted per relevant tuple for Є=0.5. Goes up to 12 tuples for Є= 0.7. Resilient to change in Є Random Relaxation Guided Relaxation

Supporting Queries with Imprecise Constraints Accuracy over CarDB 14 queries over 100K tuples Similarity learned using 25k sample Mean Reciprocal Rank (MRR) estimated as Overall high MRR shows high relevance of suggested answers

Supporting Queries with Imprecise Constraints Handling Imprecision & Incompleteness  Incompleteness in data – Databases are being populated by Entry by lay people Automated extraction  E.g. entering an “accord” without mentioning “Honda”  Imprecision in queries – Queries posed by lay users Who combine querying and browsing General Solution: “Expected Relevance Ranking” Relevance Function Density Function Challenge: Automated & Non-intrusive assessment of Relevance and Density functions

Supporting Queries with Imprecise Constraints Handling Imprecision & Incompleteness