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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/8167266C-005C-4C48-A9EB-DC0302CB7517 | - |
| Identifier | http://www.cs.berkeley.edu/~istoica/papers/2007/ryan-sigmod07.pdf | - |
| Identifier | https://doi.org/10.1145/1247480.1247535 | - |
| Language | en | - |
| Extent | 12 pages | en |
| Title | Sharing aggregate computation for distributed queries | en |
| Creator | Huebsch Ryan | en |
| Creator | Garofalakis Minos | en |
| Creator | Γαροφαλακης Μινως | el |
| Creator | Hellerstein, Joseph, 1952- | en |
| Creator | Stoica, Ion, 1965- | en |
| Publisher | Association for Computing Machinery | en |
| Content Summary | An emerging challenge in modern distributed querying is to effi- ciently process multiple continuous aggregation queries simultaneously. Processing each query independently may be infeasible, so multi-query optimizations are critical for sharing work across queries. The challenge is to identify overlapping computations that may not be obvious in the queries themselves. In this paper, we reveal new opportunities for sharing work in the context of distributed aggregation queries that vary in their selection predicates. We identify settings in which a large set of q such queries can be answered by executing k ≪ q different queries. The k queries are revealed by analyzing a boolean matrix capturing the connection between data and the queries that they satisfy, in a manner akin to familiar techniques like Gaussian elimination. Indeed, we identify a class of linear aggregate functions (including SUM, COUNT and AVERAGE), and show that the sharing potential for such queries can be optimally recovered using standard matrix decompositions from computational linear algebra. For some other typical aggregation functions (including MIN and MAX) we find that optimal sharing maps to the NP-hard set basis problem. However, for those scenarios, we present a family of heuristic algorithms and demonstrate that they perform well for moderate-sized matrices. We also present a dynamic distributed system architecture to exploit sharing opportunities, and experimentally evaluate the benefits of our techniques via a novel, flexible random workload generator we develop for this setting. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-11-30 | - |
| Date of Publication | 2007 | - |
| Subject | Algorithms | en |
| Subject | Design | en |
| Subject | Measurement | en |
| Bibliographic Citation | R. Huebsch, M. Garofalakis, J. M. Hellerstein and I. Stoica, "Sharing aggregate computation for distributed queries", in ACM SIGMOD International Conference on Management of Data, 2007. doi: 10.1145/1247480.1247535 | en |
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