A Framework for Scalable Correlation of Spatio-temporal Event Data
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Spatio-temporal event data do not only arise from sensor readings, but also in information retrieval and text analysis. However, such events extracted from a text corpus may be imprecise in both dimensions. In this paper we focus on the task of event correlation, i.e., finding events that are similar in terms of space and time. We present a framework for Apache Spark that provides correlation operators that can be configured to deal with such imprecise event data.
KeywordsApache Spark Event Data Model Skyline Processing Resilient Distributed Datasets (RDD) Geographic Component
This work was funded by the DFG under grant no. SA782/22.
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