Optimizing complex queries based on similarities of subqueries
dc.contributor.author | Tao, Yingying | en_US |
dc.contributor.author | Zhu, Qiang | en_US |
dc.contributor.author | Zuzarte, Calisto | en_US |
dc.date.accessioned | 2006-09-11T17:09:25Z | |
dc.date.available | 2006-09-11T17:09:25Z | |
dc.date.issued | 2005-09 | en_US |
dc.identifier.citation | Zhu, Qiang; Tao, Yingying; Zuzarte, Calisto; (2005). "Optimizing complex queries based on similarities of subqueries." Knowledge and Information Systems 8(3): 350-373. <http://hdl.handle.net/2027.42/45922> | en_US |
dc.identifier.issn | 0219-3116 | en_US |
dc.identifier.issn | 0219-1377 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/45922 | |
dc.description.abstract | As database technology is applied to more and more application domains, user queries are becoming increasingly complex (e.g. involving a large number of joins and a complex query structure). Query optimizers in existing database management systems (DBMS) were not developed for efficiently processing such queries and often suffer from problems such as intolerably long optimization time and poor optimization results. To tackle this challenge, we present a new similarity-based approach to optimizing complex queries in this paper. The key idea is to identify similar subqueries that often appear in a complex query and share the optimization result among similar subqueries in the query. Different levels of similarity for subqueries are introduced. Efficient algorithms to identify similar queries in a given query and optimize the query based on similarity are presented. Related issues, such as choosing good starting nodes in a query graph, evaluating identified similar subqueries and analyzing algorithm complexities, are discussed. Our experimental results demonstrate that the proposed similarity-based approach is quite promising in optimizing complex queries with similar subqueries in a DBMS. | en_US |
dc.format.extent | 907160 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Springer-Verlag | en_US |
dc.subject.other | Query Optimization | en_US |
dc.subject.other | Business Information Systems | en_US |
dc.subject.other | Algorithm | en_US |
dc.subject.other | Computational Complexity | en_US |
dc.subject.other | Computer Science | en_US |
dc.subject.other | Information Systems and Communication Service | en_US |
dc.subject.other | Complex Query | en_US |
dc.subject.other | Database System | en_US |
dc.subject.other | Query Similarity | en_US |
dc.title | Optimizing complex queries based on similarities of subqueries | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Philosophy | en_US |
dc.subject.hlbsecondlevel | Computer Science | en_US |
dc.subject.hlbtoplevel | Humanities | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Computer and Information Science, The University of Michigan–Dearborn, Dearborn, MI, 48187, USA | en_US |
dc.contributor.affiliationum | Department of Computer and Information Science, The University of Michigan–Dearborn, Dearborn, MI, 48187, USA | en_US |
dc.contributor.affiliationother | IBM Toronto Laboratory, Markham, Ontario, Canada | en_US |
dc.contributor.affiliationumcampus | Dearborn | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/45922/1/10115_2004_Article_189.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/s10115-004-0189-y | en_US |
dc.identifier.source | Knowledge and Information Systems | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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