Article in Proceedings INPROC-2016-17

BibliographyMayer, Ruben; Mayer, Christian; Tariq, Muhammad Adnan; Rothermel, Kurt: GraphCEP - Real-time Data Analytics Using Parallel Complex Event and Graph Processing.
In: Proceedings of the 10th ACM International Conference on Distributed Event-Based Systems, DEBS'16.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology.
pp. 1-8, english.
ACM, June 20, 2016.
DOI: 10.1145/2933267.2933509.
Article in Proceedings (Conference Paper).
CR-SchemaC.2.4 (Distributed Systems)

In recent years, the proliferation of highly dynamic graph-structured data streams fueled the demand for real-time data analytics. For instance, detecting recent trends in social networks enables new applications in areas such as disaster detection, business analytics or health-care. Parallel Complex Event Processing has evolved as the paradigm of choice to analyze data streams in a timely manner, where the incoming data streams are split and processed independently by parallel operator instances. However, the degree of parallelism is limited by the feasibility of splitting the data streams into independent parts such that correctness of event processing is still ensured. In this paper, we overcome this limitation for graph-structured data by further parallelizing individual operator instances using modern graph processing systems. These systems partition the graph data and execute graph algorithms in a highly parallel fashion, for instance using cloud resources. To this end, we propose a novel graph-based Complex Event Processing system GraphCEP and evaluate its performance in the setting of two case studies from the DEBS Grand Challenge 2016.

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Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems
Entry dateMay 9, 2016
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