Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-00887-0_17
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dc.titleQoS-oriented multi-query scheduling over data streams
dc.contributor.authorWu, J.
dc.contributor.authorTan, K.-L.
dc.contributor.authorZhou, Y.
dc.date.accessioned2013-07-04T08:00:26Z
dc.date.available2013-07-04T08:00:26Z
dc.date.issued2009
dc.identifier.citationWu, J.,Tan, K.-L.,Zhou, Y. (2009). QoS-oriented multi-query scheduling over data streams. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5463 : 215-229. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-00887-0_17" target="_blank">https://doi.org/10.1007/978-3-642-00887-0_17</a>
dc.identifier.isbn9783642008863
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40268
dc.description.abstractExisting query scheduling strategies over data streams mainly focus on metrics in terms of system performance, such as processing time or memory overhead. However, for commercial stream applications, what actually matters most is the users' satisfaction about the Quality of Service (QoS) they perceive. Unfortunately, a system-oriented optimization strategy does not necessarily lead to a high degree of QoS. Motivated by this, we study QoS-oriented query scheduling in this paper. One important contribution of this work is that we correlate the operator scheduling problem with the classical job scheduling problem. This not only offers a new angle in viewing the issue but also allows techniques for the well studied job scheduling problems to be adapted in this new context. We show how these two problems can be related and propose a novel operator scheduling strategy inspired by job scheduling algorithms. The performance study demonstrates a promising result for our proposed strategy.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-00887-0_17
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-642-00887-0_17
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume5463
dc.description.page215-229
dc.identifier.isiutNOT_IN_WOS
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