dc.contributor.authorChen, Zhida
dc.contributor.authorCong, Gao
dc.contributor.authorZhang, Zhenjie
dc.contributor.authorFu, Tom Z. J.
dc.contributor.authorChen, Lisi
dc.date.accessioned2017-07-04T02:37:37Z
dc.date.available2017-07-04T02:37:37Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10220/42788
dc.description.abstractHuge amount of data with both space and text information, e.g., geo-tagged tweets, is flooding on the Internet. Such spatio-textual data stream contains valuable information for millions of users with various interests on different keywords and locations. Publish/subscribe systems enable efficient and effective information distribution by allowing users to register continuous queries with both spatial and textual constraints. However, the explosive growth of data scale and user base has posed challenges to the existing centralized publish/subscribe systems for spatiotextual data streams. In this paper, we propose our distributed publish/subscribe system, called PS2Stream, which digests a massive spatio-textual data stream and directs the stream to target users with registered interests. Compared with existing systems, PS2Stream achieves a better workload distribution in terms of both minimizing the total amount of workload and balancing the load of workers. To achieve this, we propose a new workload distribution algorithm considering both space and text properties of the data. Additionally, PS2Stream supports dynamic load adjustments to adapt to the change of the workload, which makes PS2Stream adaptive. Extensive empirical evaluation, on commercial cloud computing platform with real data, validates the superiority of our system design and advantages of our techniques on system performance improvement.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.description.sponsorshipASTAR (Agency for Sci., Tech. and Research, S’pore)en_US
dc.description.sponsorshipMOE (Min. of Education, S’pore)en_US
dc.format.extent12 p.en_US
dc.language.isoenen_US
dc.rights© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [https://dx.doi.org/10.1109/ICDE.2017.154].en_US
dc.subjectServersen_US
dc.subjectDistributed databasesen_US
dc.titleDistributed Publish/Subscribe Query Processing on the Spatio-Textual Data Streamen_US
dc.typeConference Paper
dc.contributor.conferenceProceedings of the 2017 IEEE 33rd International Conference on Data Engineeringen_US
dc.contributor.researchRapid-Rich Object Search Laben_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doihttps://dx.doi.org/10.1109/ICDE.2017.154
dc.description.versionAccepted versionen_US
dc.contributor.organizationRapid-Rich Object Search Laboratoryen_US


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