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Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
ISBN: 978-1-4244-5445-7
pp: 733-744
Venkatesh Raghavan , Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, MA, USA
ABSTRACT
Multi-criteria decision support (MCDS) is crucial in many business and web applications such as web searches, B2B portals and on-line commerce. Such MCDS applications need to report results early; as soon as they are being generated so that they can react and formulate competitive decisions in near real-time. The ease in expressing user preferences in web-based applications has made Pareto-optimal (skyline) queries a popular class of MCDS queries. However, state-of-the-art techniques either focus on handling skylines on single input sets (i.e., no joins) or do not tackle the challenge of producing progressive early output results. In this work, we propose a progressive query evaluation framework ProgXe that transforms the execution of queries involving skyline over joins to be non-blocking, i.e., to be progressively generating results early and often. In ProgXe the query processing (join, mapping and skyline) is conducted at multiple levels of abstraction, thereby exploiting the knowledge gained from both input as well as mapped output spaces. This knowledge enables us to identify and reason about abstract-level relationships to guarantee correctness of early output. It also provides optimization opportunities previously missed by current techniques. To further optimize ProgXe, we incorporate an ordering technique that optimizes the rate at which results are reported by translating the optimization of tuple-level processing into a job-sequencing problem. Our experimental study over a wide variety of data sets demonstrates the superiority of our approach over state-of-the-art techniques.
CITATION
Venkatesh Raghavan, "Progressive result generation for multi-criteria decision support queries", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 733-744, doi:10.1109/ICDE.2010.5447928
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