Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.datak.2004.08.004
DC FieldValue
dc.titleAn adaptable distributed query processing architecture
dc.contributor.authorZhou, Y.
dc.contributor.authorOoi, B.C.
dc.contributor.authorTan, K.-L.
dc.contributor.authorTok, W.H.
dc.date.accessioned2013-07-04T07:34:42Z
dc.date.available2013-07-04T07:34:42Z
dc.date.issued2005
dc.identifier.citationZhou, Y., Ooi, B.C., Tan, K.-L., Tok, W.H. (2005). An adaptable distributed query processing architecture. Data and Knowledge Engineering 53 (3) : 283-309. ScholarBank@NUS Repository. https://doi.org/10.1016/j.datak.2004.08.004
dc.identifier.issn0169023X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39133
dc.description.abstractTraditionally, distributed query optimization techniques generate static query plans at compile time. However, the optimality of these plans depends on many parameters (such as the selectivities of operations, the transmission speeds and workloads of servers) that are not only difficult to estimate but are also often unpredictable and fluctuant at runtime. As the query processor cannot dynamically adjust the plans at runtime, the system performance is often less than satisfactory. In this paper, we introduce a new highly adaptive distributed query processing architecture. Our architecture can quickly detect fluctuations in selectivities of operations, as well as transmission speeds and workloads of servers, and accordingly change the operation order of a distributed query plan during execution. We have implemented a prototype based on the Telegraph system [Telegragraph project. Available from <http://telegraph.cs.berkeley.edu/>]. Our experimental study shows that our mechanism can adapt itself to the changes in the environment and hence approach to an optimal plan during execution. © 2004 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.datak.2004.08.004
dc.sourceScopus
dc.subjectAdaptive query processing
dc.subjectDistributed DBs
dc.subjectMulti-join
dc.subjectQuery optimization
dc.subjectQuery processing
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1016/j.datak.2004.08.004
dc.description.sourcetitleData and Knowledge Engineering
dc.description.volume53
dc.description.issue3
dc.description.page283-309
dc.description.codenDKENE
dc.identifier.isiut000227758100004
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