Multi-dimensional Probabilistic Regression over Imprecise Data Streams

Ran Gao, Xike Xie, Kai Zou, Torben Bach Pedersen
2022 Proceedings of the ACM Web Conference 2022  
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
more » ... igate 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.
doi:10.1145/3485447.3512150 fatcat:e7ov3sgumndw3dmhmimdf4mckm