Web of Things Data Storage

Cai, Hongming; Vasilakos, Athanasios V.

doi:10.1016/b978-0-12-809764-9.00015-9

Cited by 5 publications

(1 citation statement)

References 81 publications

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“…The stream of sensor data serves as the backbone of emerging smart applications, such as smart warehousing, smart buildings, and smart cities. Therefore, the real-time monitoring [3,4] and analyzing [5,6] of streaming data are important in intelligent WoT and WoE event processing and decision making [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Multi-dimensional Probabilistic Regression over Imprecise Data Streams

Xie

Zou

Pedersen

2022

Proceedings of the ACM Web Conference 2022

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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 demonstrate the efficiency and scalability of our proposals.

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