D2IA: Stream Analytics on User-Defined Event Intervals
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...
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| Other Authors: | , , , |
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2019
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| Online Access: | https://bspace.buid.ac.ae/handle/1234/2921 https://dl.acm.org/doi/abs/10.1007/978-3-030-21290-2_22 https://doi.org/10.1016/j.is.2020.101679 |
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| Summary: | 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. |
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