Interval-based queries over lossy IoT event streams

Busany, Nimrod ; Aa, Han van der ; Senderovich, Arik ; Gal, Avigdor ; Weidlich, Matthias

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Document Type: Article
Year of publication: 2020
The title of a journal, publication series: ACM/IMS Transactions on Data Science
Volume: 1
Issue number: 4
Page range: Article 27, 1-27
Place of publication: New York, NY
Publishing house: Association for Computing Machinery
ISSN: 2577-3224 , 2691-1922
Publication language: English
Institution: School of Business Informatics and Mathematics > Methoden der künstlichen Intelligenz (Juniorprofessur) (van der Aa 2020-)
Subject: 004 Computer science, internet
Abstract: Recognising patterns that correlate multiple events over time becomes increasingly important in applications that exploit the Internet of Things, reaching from urban transportation, through surveillance monitoring to business workflows. In many real-world scenarios, however, timestamps of events may be erroneously recorded and events may be dropped from a stream due to network failures or load shedding policies. In this work, we present SimpMatch, a novel simplex-based algorithm for probabilistic evaluation of event queries using constraints over event orderings in a stream. Our approach avoids learning probability distributions for time-points or occurrence intervals. Instead, we employ the abstraction of segmented intervals and compute the probability of a sequence of such segments using the notion of order statistics. The algorithm runs in linear time to the number of lost events, and shows high accuracy, yielding exact results if event generation is based on a Poisson process and providing a good approximation otherwise. We demonstrate empirically that SimpMatch enables efficient and effective reasoning over event streams, outperforming state-of-the-art methods for probabilistic evaluation of event queries by up to two orders of magnitude.
Additional information: Online-Ressource

Dieser Eintrag ist Teil der Universitätsbibliographie.

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