Title:
Scaling Continuous Query Services for Future Computing Platforms and Applications

dc.contributor.advisor Liu, Ling
dc.contributor.author Gedik, Bugra en_US
dc.contributor.committeeMember Pu, Calton
dc.contributor.committeeMember Jensen, Christian S.
dc.contributor.committeeMember Ramachandran, Kishore
dc.contributor.committeeMember Mark, Leo
dc.contributor.committeeMember Shatz, Sol M.
dc.contributor.department Computing en_US
dc.date.accessioned 2006-09-01T19:04:30Z
dc.date.available 2006-09-01T19:04:30Z
dc.date.issued 2006-06-13 en_US
dc.description.abstract The ever increasing rate of digital information available from on-line sources drives the need for building information monitoring applications to assist users in tracking relevant changes in these sources and accessing information that is of interest to them in a timely manner. Continuous queries (CQs) are standing queries that are continuously evaluated over dynamic sources to track information changes that meet user specified thresholds and notify users of new results in real-time. CQ systems can be considered as powerful middleware for supporting information monitoring applications. A significant challenge in building CQ systems is scalability, caused by the large number of users and queries, and by the large and growing number of information sources with high update rates. In this thesis we use CQs to shepherd through and address the challenges involved in supporting information monitoring applications in future computing platforms. The focus is on P2P web monitoring in Internet systems, location monitoring in mobile systems, and environmental monitoring in sensor systems. Although different computing platforms require different software architectures for building scalable CQ services, there is a common design philosophy that this thesis advocates for making CQ services scalable and efficient. This can be summarized as "move computation close to the places where the data is produced." A common challenge in scaling CQ systems is the resource-intensive nature of query evaluation, which involves continuously checking updates in a large number of data sources and evaluating trigger conditions of a large number of queries over these updates, consuming both cpu and network bandwidth resources. If some part of the query evaluation can be pushed close to the sources where the data is produced, the resulting early filtering of updates will save both bandwidth and cpu resources. In summary, in this thesis we show that distributed CQ architectures that are designed to take advantage of the opportunities provided by ubiquitous computing platforms and pervasive networks, while at the same time recognizing and resolving the challenges posed by these platforms, lead to building scalable and effective CQ systems to better support the demanding information monitoring applications of the future. en_US
dc.description.degree Ph.D. en_US
dc.format.extent 3953633 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/11452
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Continuous queries en_US
dc.subject Information monitoring
dc.title Scaling Continuous Query Services for Future Computing Platforms and Applications en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Liu, Ling
local.contributor.corporatename College of Computing
relation.isAdvisorOfPublication 96391b98-ac42-4e2c-93ee-79a5e16c2dfb
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
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