p29 hemal khatri

Information about p29 hemal khatri

Published on February 7, 2008

Author: Tomasina

Source: authorstream.com

Content

QUIC: Handling Query Imprecision & Data Incompleteness in Autonomous Databases:  QUIC: Handling Query Imprecision & Data Incompleteness in Autonomous Databases Subbarao Kambhampati (Arizona State University) Garrett Wolf (Arizona State University) Yi Chen (Arizona State University) Hemal Khatri (Microsoft) Bhaumik Chokshi (Arizona State University) Jianchun Fan (Amazon) Ullas Nambiar (IBM Research, India) Challenges in Querying Autonomous Databases:  Challenges in Querying Autonomous Databases Imprecise Queries User’s needs are not clearly defined hence: Queries may be too general Queries may be too specific General Solution: “Expected Relevance Ranking” Challenge: Automated & Non-intrusive assessment of Relevance and Density functions Incomplete Data Databases are often populated by: Lay users entering data Automated extraction Challenge: Rewriting a user’s query to retrieve highly relevant Similar/ Incomplete tuples However, how can we retrieve similar/ incomplete tuples in the first place? Challenge: Provide explanations for the uncertain answers in order to gain the user’s trust Once the similar/incomplete tuples have been retrieved, why should users believe them? Expected Relevance Ranking Model:  Expected Relevance Ranking Model Problem: How to automatically and non-intrusively assess the Relevance & Density functions? Estimating Relevance (R): Learn relevance for user population as a whole in terms of value similarity Sum of weighted similarity for each constrained attribute Content Based Similarity (Mined from probed sample using SuperTuples) Co-click Based Similarity (Yahoo Autos recommendations) Co-occurrence Based Similarity (GoogleSets) Estimating Density (P): Learn density for each attribute independent of the other attributes AFDs used for feature selection AFD-Enhanced NBC Classifiers AFDs play a role in: Attribute Importance Feature Selection Query Rewriting Retrieving Relevant Answers via Query Rewriting:  Given an AFD, rewrite the query using the determining set attributes in order to retrieve possible answers Q1’: Make=Honda Λ Body Style=coupe Retrieving Relevant Answers via Query Rewriting Retrieve certain answers namely tuples t1 and t6 Q2’: Make=Honda Λ Body Style=sedan Certain Answers Thus we retrieve: Incomplete Answers Similar Answers Problem: How to rewrite a query to retrieve answers which are highly relevant to the user? Given a query Q:(Model=Civic) retrieve all the relevant tuples Explaining Results to Users:  Explaining Results to Users Problem: How to gain users trust when showing them similar/incomplete tuples? View Live QUIC Demo Empirical Evaluation:  Empirical Evaluation Ranking Order User Study: 14 queries & ranked lists of uncertain tuples Asked to mark the Relevant tuples R-Metric used to determine ranking quality Similarity Metric User Study: Each user shown 30 lists Asked which list is most similar Users found Co-click to be the most similar to their personal relevance function Query Rewriting Evaluation: Measure inversions between rank of query and actual rank of tuples By ranking the queries, we are able to (with relatively good accuracy) retrieve tuples in order of their relevance to the user 2 User Studies (10 users, data extracted from Yahoo Autos) Conclusion:  Conclusion QUIC is able to handle both imprecise queries and incomplete data over autonomous databases By an automatic and non-intrusive assessment of relevance and density functions, QUIC is able to rank tuples in order of their expected relevance to the user By rewriting the original user query, QUIC is able to efficiently retrieve both similar and incomplete answers to a query By providing users with a explanation as to why they are being shown answers which do not exactly match the query constraints, QUIC is able to gain the user’s trust http://styx.dhcp.asu.edu:8080/QUICWeb

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