Multi-objective Query Processing for Database Systems

doi:10.1016/B978-012088469-8.50082-6

Proceedings 2004 VLDB Conference

2004, Pages 936-947

https://doi.org/10.1016/B978-012088469-8.50082-6 Get rights and content

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This chapter addresses the issue of multi-objective retrieval in database query processing. It discusses the applications for this retrieval paradigm and the meaning of “overall best” objects from a database perspective. Multi-objective retrieval is especially useful for personalization problems, where multiple user preferences are taken into account, and one has to compromise among certain desired characteristics of database objects to deliver high quality results. The chapter investigates two extreme cases of such retrieval—top k retrieval and skyline queries. Handling cases involving several distinct objectives still needs to access and compare all database objects. The chapter presents a novel multi-objective retrieval algorithm, and proves that it always retrieves a correct result set and uses only an instance-optimal number of object accesses. Finally, the chapter addresses preliminary practical experiences with applications of this algorithm. The multi-objective retrieval algorithm can be easily integrated into practical personalization frameworks or relational query processing.

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Fig. 3 shows the top-k processing techniques discussed in paper [19]. Various top-k query techniques have been proposed: TA (Threshold Algorithm) [10], BPA (Best Position Algorithm) [1], IO-top-k algorithm [4], several database-based top-k query techniques [5,3], and other various optimizations [7,12]. However, the general top-k query techniques are not able to address the high dimensional data query challenge [9].
The analytics in online performance monitoring systems have often been limited due to the query performance of large scale multidimensional data. In this paper, we introduce a faster query approach using the bit-sliced index (BSI). Our study covers multidimensional grouping and preference top-k queries with the BSI, algorithms design, time complexity evaluation, and the query time comparison on a real-time production performance monitoring system. Our research work extended the BSI algorithms to cover attributes filtering and multidimensional grouping. We evaluated the query time with the single attribute, multiple attributes, feature filtering, and multidimensional grouping. To compare with the existing prior arts, we made a benchmarking comparison with the bitmap indexing, sequential scan, and collection streaming grouping. In the result of our experiments with large scale production data, the proposed BSI approach outperforms the existing prior arts: 3 times faster than the bitmap indexing approach on single attribute top-k queries, 10 times faster than the collection stream approach on the multidimensional grouping. While comparing with the baseline sequential scan approach, our proposed algorithm BSI approach outperforms the sequential scan approach with a factor of 10 on multiple attributes queries and a factor of 100 on single attribute queries. In the previous research, we had evaluated the BSI time complexity and space complexity on simulation data with various distributions, this research work further studied, evaluated, and concluded the BSI approach query performance with real production data.
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