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Knowledge Computing and Its Applications pp 131–160Cite as

Framework for Geospatial Query Processing by Integrating Cassandra with Hadoop

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Abstract

Nowadays we are moving towards digitization and making all our devices such as sensors, cameras connected to Internet producing big data. This big data has variety of data and has paved the way for the emergence of NoSQL databases, like Cassandra for achieving scalability and availability. Hadoop framework has been developed for storing and processing distributed data. In this work, we mainly investigated on storage and retrieval of geospatial data by integrating Hadoop and Cassandra using prefix-based partitioning and Cassandra’s default partitioning algorithm, i.e. Murmur3Partitioner techniques. Geohash value is generated that acts as a partition key and also helps in effective search. Hence, the time taken for retrieving data is optimized. When user requests for spatial queries like finding nearest locations, searching in Cassandra database starts using both partitioning techniques. A comparison on query response time is made so as to verify which method is more effective. Results showed that prefix-based partitioning technique is efficient than Murmur3 partitioning technique.

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Authors and Affiliations

  1. VR Siddhartha Engineering College, Kanuru, Andhra Pradesh, India

    S. Vasavi & M. Padma Priya

  2. Illinois State University, Normal, IL, USA

    Anu A. Gokhale

Authors
  1. S. Vasavi
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  2. M. Padma Priya
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  3. Anu A. Gokhale
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Correspondence to S. Vasavi .

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Editors and Affiliations

  1. Computer Science and Engineering, VIT University, Vellore, Tamil Nadu, India

    S. Margret Anouncia

  2. The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark

    Uffe Kock Wiil

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Vasavi, S., Padma Priya, M., Gokhale, A.A. (2018). Framework for Geospatial Query Processing by Integrating Cassandra with Hadoop. In: Margret Anouncia, S., Wiil, U. (eds) Knowledge Computing and Its Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-6680-1_7

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  • DOI: https://doi.org/10.1007/978-981-10-6680-1_7

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  • Print ISBN: 978-981-10-6679-5

  • Online ISBN: 978-981-10-6680-1

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