Realizing Cascading Stream Reasoning with Streaming MASSIF

Tracking #: 1748-2960

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Authors: 
Pieter Bonte
Riccardo Tommasini
Emanuele Della Valle
Filip De Turck
Femke Ongenae

Responsible editor: 
Guest Editors Stream Reasoning 2017

Submission type: 
Full Paper
Abstract: 
To perform meaningful analysis over multiple streams of heterogeneous data, stream processing needs to support expressive reasoning capabilities to infer implicit facts and temporal reasoning to capture temporal dependencies. However, current stream reasoning approaches cannot perform the required reasoning expressivity while detecting time dependencies over high frequency data streams. Cascading Reasoning was meant to solve the problem of expressive reasoning over high frequency streams by introducing a hierarchical approach consisting of multiple layers. Each layer minimizes the processed data and increases the complexity of the data processing. However, the original Cascading Reasoning vision was never fully realized. Therefore, we propose a renewed and more generalized vision on Cascading Reasoning, serving as a blueprint for existing and future hierarchical approaches. Furthermore, we introduce Streaming MASSIF, a new Cascading Reasoning approach, performing expressive reasoning and complex event processing over high velocity streams. We show that our approach is able to handle high velocity streams up to hundreds of events per second, in combination with expressive reasoning and complex event processing.
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