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Presentation (discussion)
This Friday Laurynas will give the paper presentation on the Similarity-aware Query Processing and Optimization.
Abstract
Many application scenarios, e.g., marketing analysis, sensor networks, and medical and biological applications, require or can significantly benefit from the identification and processing of similarities in the data. Even though some work has been done to extend the semantics of some operators, e.g., join and selection, to be aware of data similarities; there has not been much study on the role, interaction, and implementation of similarity-aware operators as first-class database operators. The focus of the thesis work presented in this paper is the proposal and study of several similarity-aware database operators and a systematic analysis of their role as query operators, interactions, optimizations, and implementation techniques. This paper presents the core research
questions that drive our research work and the physical database operators that were studied as part of this thesis work so far, i.e., Similarity Group-by and Similarity Join. We describe multiple optimization techniques for the introduced operators. Specifically, we present: (1) multiple non-trivial equivalence rules that enable similarity query transformations, (2) Eager and Lazy aggregation
transformations for Similarity Group-by and Similarity Join to allow pre-aggregation before potentially expensive joins, and (3) techniques to use materialized views to answer similarity-based queries. This paper also presents the main guidelines to implement the presented operators as integral components of a DBMS query engine and some of the key performance evaluation results of this
implementation in an open source DBMS. In addition, we present the way the proposed operators are efficiently exploited to answer more useful business questions in a decision support system.
Attendance: