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Query Processing over Incomplete Autonomous Databases

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goldy
23 diapositives · 361 Views · Sep 03, 2014

This work, presented by an Arizona State University team, addresses the challenges of query processing in incomplete autonomous databases, which often face issues such as incomplete entries, inaccurate extractions, and heterogeneous schemas. The proposed QPIAD approach generates rewritten queries using mined correlation rules to improve the accuracy and precision of query results. By leveraging Approximate Functional Dependencies and Naïve Bayes classifiers, QPIAD effectively handles missing values and offers higher retrieval precision compared to traditional methods, thereby enhancing database robustness in e-business contexts.

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Query Processing over Incomplete Autonomous Databases
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  1. Query Processing over Incomplete Autonomous Databases Presented By Garrett Wolf, Hemal Khatri, Bhaumik Chokshi, Jianchun Fan, Yi Chen, Subbarao Kambhampati Arizona State University 2008-02-04 Summerized By Sungchan Park

  2. Introduction • More and more data is becoming accessible via web servers which are supported by backend autonomous databases • E.g. Cars.com, Realtor.com, Google Base, Etc. Autonomous Database Mediator Autonomous Database Autonomous Database Center for E-Business Technology

  3. Web DB.s are Incomplete! • Incomplete Entry • Inaccurate Extraction • Heterogeneous Schemas • User-Defined Schemas Center for E-Business Technology

  4. Problem • Current autonomous database systems only return certain answers, namely those which exactly satisfy all the user query constraints • Although there has been work on handling incompleteness in databases, much of it has been focused on single databases on which the query processor has complete control. • Modify databases directly by replacing null values with likely values. • Not applicable to autonomous databases Center for E-Business Technology

  5. Possible Naïve Approaches Query Q: (Body Style = Convt) • CERTAINONLY • Return only certain answer • Low Recall • ALLRETURNED • Return all answer having Body Style = Convt or Body Style = Null • Low Precision, Infeasible • ALLRANKED • Return all answers having Body Style = Convt. Additionally, rank all answers having body style as null by predicting the missing values and return them to the user • Costly, Infeasible Center for E-Business Technology

  6. QPIAD • Solved the problem by generating rewritten queries according to a set of mined attribute correlation rules. • Approximate Functional Dependency(AFD) • Naïve Bayesian Classifier Center for E-Business Technology

  7. QPIAD Solution Center for E-Business Technology

  8. QPIAD Architecture Center for E-Business Technology

  9. Overall Process • Learn • Rewrite • Rank • Explain Center for E-Business Technology

  10. #1. Learn - AFD • Learn Attribute Correlations • Approximate Functional Dependencies(AFD) • Approximate Keys(Akeys) • For pruning • Learn by TANE algorithm • Y. Huhtala, et al. Efficient discovery of functional and approximate dependencies using partition. 1998. • Pruning example • AFD {A1, A2} ~> A3 • Akey {A1} Center for E-Business Technology

  11. #1. Learn - Naïve Bayesian Classifier • Learn Value distribution by NBC • Using mined AFD as selected feature • E.g. • AFD {Make, Body} ~> Model • P(Model = Accord | Make = Honda, Body = Coupe) = ? Center for E-Business Technology

  12. #1. Learn - Selectivity • SmplSel(Q)*SmplRatio(R)*PerInc(R) • SmplSel(Q) = Selectivity of rewritten query issued on sample • SmplRatio(R) = Ratio of original database size over sample • PerInc(R) = Percent of incomplete tuples while creating sample Center for E-Business Technology

  13. #2. Rewrite • Get base result(Certain answers) • Generate rewritten queries by base result and learned AFD Rewritten Queries Center for E-Business Technology

  14. #3. Rank • Select top-k queries based on F-Measure • Reorder selected query based on P • Retrieve tuples P = learned Prob. R = selectivity Center for E-Business Technology

  15. #4. Explain Center for E-Business Technology

  16. Other Issues: Correlated Source Center for E-Business Technology

  17. Other Issues: Handling Aggregation Center for E-Business Technology

  18. Empirical Evaluation: Quality • QPIAD vs. ALLRETURNED • ALLRETURNEDhas low precision because not all tuples with missing values on the constrained attributes are relevant to the query • QPIAD has a much higher precision than ALLRETURNED as it aims to retrieve tuples with missing values on the constrained attributes which are very likely to be relevant to the query Center for E-Business Technology

  19. Empirical Evaluation: Efficiency • QPIAD vs. ALLRANKED • ALLRANKED approach is often infeasible as direct retrieval of null values is not often allowed • QPIAD is able to achieve the same level of recall as ALLRANKED while requiring much fewer tuples to be retrieved Center for E-Business Technology

  20. Empirical Evaluation: Robustness • Robustness w.r.t. Sample Size • QPIAD is robust even when face with a relatively small data sample Center for E-Business Technology

  21. Empirical Evaluation: Extensions • Aggregates • Prediction of missing values increases the fraction of queries that achieve higher levels of accuracy • Approximately 20% more queries achieve 100% accuracy when prediction is used • Join • As alpha is increased, we obtain a higher recall without sacrificing much precision Center for E-Business Technology

  22. Related Work • Querying Incomplete Databases • Possible World Approaches – tracks the completions of incomplete tuples (CoddTables, V-Tables, Conditional Tables) • Probabilistic Approaches – quantify distribution over completions to distinguish between likelihood of various possible answers • Probabilistic Databases • Tuples are associated with an attribute describing the probability of its existence • However, in our work, the mediator does not have the capability to modify the underlying autonomous databases • Query Reformulation / Relaxation • Aims to return similar or approximate answers to the user after returning or in the absence of exact answers • Our focus is on retrieving tuples with missing values on constrained attributes • Learning Missing Values • Common imputation approaches replace missing values by substituting the mean, most common value, default value, or using kNN, association rules, etc. • Our work requires schema level dependencies between attributes as well as distribution information over missing values Center for E-Business Technology

  23. Contribution • Efficiently retrieve relevant uncertain answers from autonomous sources given only limited query access patterns • Query Rewriting • Retrieves answers with missing values on constrained attributes without modifying the underlying databases • AFD-Enhanced Classifiers • Rewriting & ranking considers the natural tension between precision and recall • F-Measure based ranking • AFDs play a major role in: • Query Rewriting • Feature Selection • Explanations Center for E-Business Technology