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International Conference on Computational Intelligence, Communications, and Business Analytics

CICBA 2017: Computational Intelligence, Communications, and Business Analytics pp 59–71Cite as

A Storage Model for Handling Big Data Variety

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 775))

Abstract

In present scenario of data storage world, selection of storage system is performed on the basis of performance which can be hardware compatibility, cost, response time or application features. In case of big data, data is collected in different format. At the other end, each and every data storage system is not suitable for storing any types of data. Here, the question is, how to prepare a storage system that can be able to store any types of data in the corresponding data storage system. Again, the data format conversion of heterogeneous raw data into a particular format is costly and time consuming. Therefore, it is require to have such data storage mechanism which can store the raw data in the data storage system in their originated data format. In this paper, we proposed an xml based storage model which is going to solve the above mentioned problem. The proposed model makes a decision regarding the allocation of storage resources (or databases) on the basis of data type. Our storage model can customized the storage structure of each and individual storage resource. Collection of data fragmentation and selection of the compatible database are the responsibility of our storage model. Also, the applicability of this storage model is described through an experiment in this paper.

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Notes

  1. 1.

    http://www.datastax.com/dev/blog/basic-rules-of-cassandra-data-modeling.

  2. 2.

    https://docs.mongodb.com/manual/core/data-modeling-introduction/.

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Author information

Authors and Affiliations

  1. Department of Information Technology, Jadavpur University, Kolkata, India

    Samiran Chattopadhyay

  2. School of Mobile Computing, Jadavpur University, Kolkata, India

    Anindita Sarkar

Authors
  1. Anindita Sarkar
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  2. Samiran Chattopadhyay
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Corresponding author

Correspondence to Anindita Sarkar .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

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Sarkar, A., Chattopadhyay, S. (2017). A Storage Model for Handling Big Data Variety. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 775. Springer, Singapore. https://doi.org/10.1007/978-981-10-6427-2_5

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  • DOI: https://doi.org/10.1007/978-981-10-6427-2_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6426-5

  • Online ISBN: 978-981-10-6427-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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