D2IA: Stream Analytics on User-Defined Event Intervals
Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Nature Switzerland AG
Abstract
Nowadays, modern Big Stream Processing Solutions (e.g.
Spark, Flink) are working towards ultimate frameworks for streaming
analytics. In order to achieve this goal, they started to offer extensions
of SQL that incorporate stream-oriented primitives such as windowing
and Complex Event Processing (CEP). The former enables stateful com putation on infinite sequences of data items while the latter focuses on
the detection of events pattern. In most of the cases, data items and
events are considered instantaneous, i.e., they are single time points in
a discrete temporal domain. Nevertheless, a point-based time semantics
does not satisfy the requirements of a number of use-cases. For instance,
it is not possible to detect the interval during which the temperature
increases until the temperature begins to decrease, nor all the relations
this interval subsumes. To tackle this challenge, we present D2IA; a set of
novel abstract operators to define analytics on user-defined event inter vals based on raw events and to efficiently reason about temporal rela tionships between intervals and/or point events. We realize the imple mentation of the concepts of D2IA on top of Esper, a centralized stream
processing system, and Flink, a distributed stream processing engine for
big data.
Description
Keywords
Big Stream Processing · Complex event processing · User-defined event intervals
Citation
Awad, A. et al. (2022) “D2IA: User-defined interval analytics on distributed streams,” Information Systems, 104, p. 1.