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Author: search.filters.author.Gupta, UpaSubject: search.filters.subject.Apache Spark

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    Adapting K-means clustering to identify spatial patterns in storms
    (2016-01-01)
    Gupta, Upa
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Elmasri, Ramez
    ;
    Fegaras, Leonidas
    This paper extends our previous work on deriving meaningful storm patterns from very large rainfall data. In an earlier work, we described MapReduce-based algorithms to identify three types of the storms: local, hourly and overall storms. In general, local storms have temporal characteristics of the storms at a particular site, hourly storms have spatial characteristics of the storms at a particular hour and overall storms have both spatial and temporal characteristics of the storm. We aim to find meaningful patterns and predict trajectories in the spatio-temporal data (i.e. overall storms which are sets of geographically overlapping, consecutive hourly storms). In this paper, we adapt K-Means clustering to find different types of hourly storms based on their shapes and sizes. Since the rainfall data are typically larger than the memory capacity of a single computer, we have implemented this clustering algorithm in Apache Spark, which is a distributed data processing framework, and have run our experiments on a computer cluster.