Abstract
In applications of Web of Things or Web of Events, a massive volume of multi-dimensional streaming data are automatically and continuously generated from different sources, such as GPS, sensors, and other measurement devices, which are essentially imprecise (inaccurate and/or uncertain). It is challenging to monitor and get insights over imprecise and low-level streaming data, in order to capture potentially important data changing trends and to initiate prompt responses. In this work, we investigate solutions for conducting multi-dimensional and multi-granularity probabilistic regression for the imprecise streaming data. The probabilistic nature of streaming data poses big computational challenges to the regression and its aggregation. In this paper, we study a series of techniques on multi-dimensional probabilistic regression, including aggregation, sketching, popular path materialization, and exception-driven querying. Extensive experiments on real and synthetic datasets show the efficiency and scalability of our proposals.
Originalsprog | Engelsk |
---|---|
Titel | WWW 2022 - Proceedings of the ACM Web Conference 2022 |
Antal sider | 10 |
Forlag | Association for Computing Machinery |
Publikationsdato | 25 apr. 2022 |
Sider | 3317-3326 |
ISBN (Elektronisk) | 9781450390965 |
DOI | |
Status | Udgivet - 25 apr. 2022 |
Begivenhed | 31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, Frankrig Varighed: 25 apr. 2022 → 29 apr. 2022 |
Konference
Konference | 31st ACM World Wide Web Conference, WWW 2022 |
---|---|
Land/Område | Frankrig |
By | Virtual, Online |
Periode | 25/04/2022 → 29/04/2022 |
Sponsor | ACM SIGWEB |
Navn | WWW 2022 - Proceedings of the ACM Web Conference 2022 |
---|
Bibliografisk note
Funding Information:This work is supported by NSFC (No. 61772492).
Funding Information:
This research has been supported in part by the National Key Research and Development Program of China under Grant 2020YFB210400 and Grant 2020AAA0106000; in part by the National Natural Science Foundation of China under Grant 61972223, Grant U1936217, Grant U20B2060, and Grant 61971267; in part by the International Postdoctoral Exchange Fellowship Program (Talent-Introduction Program) under YJ20210274; and in part by the Academy of Finland under Project 319669, Project 319670, Project 325570, Project 326305, Project 325774, and Project 335934.
Publisher Copyright:
© 2022 ACM.