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Conference Paper: TOAIN: A Throughput Optimizing Adaptive Index for Answering Dynamic kNN Queries on Road Networks
Title | TOAIN: A Throughput Optimizing Adaptive Index for Answering Dynamic kNN Queries on Road Networks |
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Authors | |
Issue Date | 2018 |
Publisher | Very Large Data Base (VLDB) Endowment Inc. The Journal's web site is located at http://vldb.org/pvldb/index.html |
Citation | The 44th International Conference on Very Large Data Bases (VLDB), Rio de Janeiro, Brazil, 27-31 August 2018. In Proceedings of the VLDB Endowment, 2018, v. 11 n. 5, p. 594-606 How to Cite? |
Abstract | We study the classical kNN queries on road networks. Existing solutions mostly focus on reducing query processing time. In many applications, however, system throughput is a more important measure. We devise a mathematical model that describes throughput in terms of a number of system characteristics. We show that query time is only one of the many parameters that impact throughput. Others include update time and query/update arrival rates. We show that the traditional approach of improving query time alone is generally inadequate in optimizing throughput. Moreover, existing solutions lack flexibility in adapting to environments of different characteristics. We propose Toain, which is a very flexible algorithm that can be easily trained to adapt to a given environment for maximizing query throughput. We conduct extensive experiments on both real and synthetic data and show that Toain gives significantly higher throughput compared with existing solutions. |
Persistent Identifier | http://hdl.handle.net/10722/262473 |
ISSN | 2023 Impact Factor: 2.6 2023 SCImago Journal Rankings: 2.666 |
DC Field | Value | Language |
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dc.contributor.author | Luo, S | - |
dc.contributor.author | Kao, CM | - |
dc.contributor.author | Li, G | - |
dc.contributor.author | Hu, J | - |
dc.contributor.author | Cheng, CK | - |
dc.contributor.author | Zheng, Y | - |
dc.date.accessioned | 2018-09-28T04:59:54Z | - |
dc.date.available | 2018-09-28T04:59:54Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | The 44th International Conference on Very Large Data Bases (VLDB), Rio de Janeiro, Brazil, 27-31 August 2018. In Proceedings of the VLDB Endowment, 2018, v. 11 n. 5, p. 594-606 | - |
dc.identifier.issn | 2150-8097 | - |
dc.identifier.uri | http://hdl.handle.net/10722/262473 | - |
dc.description.abstract | We study the classical kNN queries on road networks. Existing solutions mostly focus on reducing query processing time. In many applications, however, system throughput is a more important measure. We devise a mathematical model that describes throughput in terms of a number of system characteristics. We show that query time is only one of the many parameters that impact throughput. Others include update time and query/update arrival rates. We show that the traditional approach of improving query time alone is generally inadequate in optimizing throughput. Moreover, existing solutions lack flexibility in adapting to environments of different characteristics. We propose Toain, which is a very flexible algorithm that can be easily trained to adapt to a given environment for maximizing query throughput. We conduct extensive experiments on both real and synthetic data and show that Toain gives significantly higher throughput compared with existing solutions. | - |
dc.language | eng | - |
dc.publisher | Very Large Data Base (VLDB) Endowment Inc. The Journal's web site is located at http://vldb.org/pvldb/index.html | - |
dc.relation.ispartof | Proceedings of the VLDB Endowment (PVLDB) | - |
dc.rights | Proceedings of the VLDB Endowment (PVLDB). Copyright © Very Large Data Base (VLDB) Endowment Inc. | - |
dc.title | TOAIN: A Throughput Optimizing Adaptive Index for Answering Dynamic kNN Queries on Road Networks | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Kao, CM: kao@cs.hku.hk | - |
dc.identifier.email | Cheng, CK: ckcheng@cs.hku.hk | - |
dc.identifier.authority | Kao, CM=rp00123 | - |
dc.identifier.authority | Cheng, CK=rp00074 | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1145/3187009.3177736 | - |
dc.identifier.hkuros | 292729 | - |
dc.identifier.volume | 11 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 594 | - |
dc.identifier.epage | 606 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2150-8097 | - |