D 2IA: User-defined interval analytics on distributed streams
Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
ProQuest Central
Abstract
Nowadays, modern Big Stream Processing Solutions (e.g. Spark, Flink) are working towards being
the ultimate framework 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 computation 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 for all the relations this interval subsumes. To tackle this challenge, we present
D
2IA; a set of novel abstract operators to define analytics on user-defined event intervals based on
raw events and to efficiently reason about temporal relationships between intervals and/or point
events. We realize the implementation of the concepts of D
2IA on top of Flink, a distributed stream
processing engine for big data.
Description
Keywords
Big Stream ProcessingComplex Event ProcessingUser-defined event intervals
Citation
Awad, A. et al. (2022) “D2IA: User-defined interval analytics on distributed streams,” Information Systems, 104, p. 1.