jianchun thesis talk

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Published on February 5, 2008

Author: Michelangelo

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Adaptive Query Processing for Data Aggregation::  Adaptive Query Processing for Data Aggregation: Mining, Using and Maintaining Source Statistics M.S Thesis Defense by Jianchun Fan Committee Members: Dr. Subbarao Kambhampati (chair) Dr. Huan Liu Dr. Yi Chen April 13, 2006 Introduction:  Introduction Data Aggregation: Vertical Integration Mediator R (A1, A2, A3, A4, A5, A6) S1 R1 (A1, A2, _, _, A5, A6) S2 R2 (A1, _, A3, A4, A5, A6) S3 R1 (A1, A2, A3, A4, A5, _) Introduction:  Introduction Query Processing in Data Aggregation Sending every query to all sources ? Increasing work load on sources Consuming a lot of network resources Keeping users waiting Primary processing task: Selecting the most relevant sources regarding difference user objectives, such as completeness and quality of the answers and response time Need several types of sources statistics to guide source selection Usually not directly available Introduction:  Introduction Challenges Automatically gather various types of source statistics to optimize individual goal Many answers (high coverage) Good answers (high density) Answered quickly (short latency) Combine different statistics to support multi-objective query processing Maintain statistics dynamically System Overview:  System Overview System Overview:  System Overview Test beds: Bibfinder: Online bibliography mediator system, integrating DBLP, IEEE xplore, CSB, Network Bibligraph, ACM Digital Library, etc. Synthetic test bed: 30 synthetic data sources (based on Yahoo! Auto database) with different coverage, density and latency characteristics. Outline:  Outline Introduction & Overview Coverage/Overlap Statistics Learning Density Statistics Learning Latency Statistics Multi-Objective Query Processing Other Contribution Conclusion Coverage/Overlap Statistics:  Coverage/Overlap Statistics Coverage: how many answers a source provides for a given query Overlap: how many common answers a set of sources share for a given query Based on Nie & Kambkampati [ICDE 2004] Density Statistics:  Density Statistics Coverage measures “vertical completeness” of the answer set “horizontal completeness” is important too – quality of the individual answers Density statistics measures the horizontal completeness of the individual answer tuples Defining Density:  Defining Density Density of a source w.r.t a given query: Average of density of all answers Select A1, A2, A3, A4 From S Where A1 > v1 Density = (1 + 0.5 + 0.5 + 0.75) / 4 = 0.675 Learning density for every possible source/query combination? – too costly The number of possible queries is exponential to the number of attributes Projection Attribute set Selection Predicates Learning Density Statistics:  Learning Density Statistics A more realistic solution: classify the queries and learn density statistics only w.r.t the classes Select A1, A2, A3, A4 From S Where A1 > v1 Projection Attribute set Selection Predicates Assumption: If a tuple t represents a real world entity E, then whether or not t has missing value on attribute A is independent to E’s actual value of A. Learning Density Statistics:  Learning Density Statistics Query class for density statistics: projection attribute set For queries whose projection attribute set is (A1, A2, …, Am), 2m different types of answers 22 different density patterns: dp1 = (A1, A2) dp2 = (A1, ~A2) dp3 = (~A1, A2) dp4 = (~A1, ~A2) Density([A1, A2] | S) = P(dp1 | S) * 1.0 + P(dp2 | S) * 0.5 + P(dp3 | S) * 0.5 + P(dp4 | S) * 0.0 Learning Density Statistics:  Learning Density Statistics R(A1, A2, …, An) 2n possible projection attribute set (A1) (A1, A2) (A1, A3) … (A1, A2, …, Am) … 2m possible density patterns (A1, A2, …, Am) (~A1, A2, …, Am) (~A1, ~A2, …, Am) … (~A1, ~A2, …, ~Am) For each data source S, the mediator needs to estimate joint probabilities! Learning Density Statistics:  Learning Density Statistics Independence Assumption: the probability of tuple t having a missing value on attribute A1 is independent of whether or not t has a missing value on attribute A2. For queries whose projection attribute set is (A1, A2, …, Am), only need to assess m probability values for each source! Joint distribution: P(A1, ~A2 | S) = P(A1 | S) * (1 - P(A2 | S)) Learned from a sample of the data source Outline:  Outline Introduction & Overview Coverage/Overlap Statistics Learning Density Statistics Learning Latency Statistics Multi-Objective Query Processing Other Contribution Conclusion Latency Statistics:  Latency Statistics Existing work: source specific measurement of response time Variations on time, day of the week, quantity of data, etc. However, latency is often query specific For example, some attributes are indexed How to classify queries to learn latency? Binding Pattern Same different Latency Statistics:  Latency Statistics Using Latency Statistics:  Using Latency Statistics Learning is straightforward: average on a group of training queries for each binding pattern Effectiveness of binding pattern based latency statistics Outline:  Outline Introduction & Overview Coverage/Overlap Statistics Learning Density Statistics Learning Latency Statistics Multi-Objective Query Processing Other Contribution Conclusion Multi-Objective Query Processing:  Multi-Objective Query Processing Users may not be easy to please… “give me some good answers fast” “I need many good answers” … These goals are often conflicting! decoupled optimization strategy won’t work Example: S1(coverage = 0.60, density = 0.10) S2(coverage = 0.55, density = 0.15) S3(coverage = 0.50, density = 0.50) Multi-Objective Query Processing:  Multi-Objective Query Processing The mediator needs to select sources that are good in many dimensions “Overall optimality” Query selection plans can be viewed as 3-dimentional vectors Option1: Pareto Optimal Set Option2: aggregating multi-dimension vectors into scalar utility values Combining Density and Coverage:  Combining Density and Coverage Combining Density and Coverage:  Combining Density and Coverage Combining Density and Coverage:  Combining Density and Coverage Multi-Objective Query Processing:  Multi-Objective Query Processing discount model weighted sum model 2D coverage Multi-Objective Query Processing:  Multi-Objective Query Processing Outline:  Outline Introduction & Overview Coverage/Overlap Statistics Learning Density Statistics Learning Latency Statistics Multi-Objective Query Processing Other Contribution Conclusion Other Contribution:  Other Contribution Incremental Statistics Maintenance (In Thesis) Other Contribution:  Other Contribution A snapshot of public web services (not in Thesis) [Sigmod Record Mar. 2005] Implications and Lessons learned: Most publicly available web services support simple data sensing and conversion, and can be viewed as distributed data sources Discovery/Retrival of public web services are not beyond what the commercial search engines do. Composition: Very few services available – little correlations among them Most composition problems can be solved with existing data integration techniques Other Contribution:  Other Contribution Query Processing over Incomplete Autonomous Database [with Hemal Khatri] Retrieving uncertain answers where constrained attributes are missing Learning Approximate Functional Dependency and Classifiers to reformulate the original user queries Select * from cars where model = “civic” (Make, Body Style) Model Q1: select * from cars where make = Honda and BodyStyle = “sedan” Q2: select * from cars where make = Honda and BodyStyle = “coupe” Conclusion:  Conclusion A comprehensive framework Automatically learns several types of source statistics Uses statistics to support various query processing goal Optimize in individual dimensions (coverage, density & latency) Joint Optimization over multiple objectives Adaptive to different users’ own preferences Dynamically maintains source statistics

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