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A Data Management Scheme for Micro-Level Modular Computation-Intensive Programs in Big Data Platforms

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Data Management and Analysis

Part of the book series: Studies in Big Data ((SBD,volume 65))

Abstract

Big Data analytics or systems developed with parallel distributed processing frameworks (e.g., Hadoop and Spark) are becoming popular for finding important insights from a huge amount of heterogeneous data (e.g., image, text, and sensor data). These systems offer a wide range of tools and connect them to form workflows for processing Big Data. Independent schemes from different studies for managing programs and data of workflows have been already proposed by many researchers and most of the systems have been presented with data or metadata management. However, to the best of our knowledge, no study particularly discusses the performance implications of utilizing intermediate states of data and programs generated at various execution steps of a workflow in distributed platforms. In order to address the shortcomings, we propose a scheme of Big Data management for micro-level modular computation-intensive programs in a Spark and Hadoop-based platform. In this paper, we investigate whether management of the intermediate states can speed up the execution of an image processing pipeline consisting of various image processing tools/APIs in Hadoop Distributed File System (HDFS) while ensuring appropriate reusability and error monitoring. From our experiments, we obtained prominent results, e.g., we have reported that with the intermediate data management, we can gain up to 87% computation time for an image processing job.

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Acknowledgement

This work is supported in part by the Canada First Research Excellence Fund (CFREF) under the Global Institute for Food Security (GIFS).

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Authors and Affiliations

  1. Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada

    Debasish Chakroborti, Banani Roy, Amit Mondal, Golam Mostaeen, Chanchal K. Roy, Kevin A. Schneider & Ralph Deters

Authors
  1. Debasish Chakroborti
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  2. Banani Roy
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  3. Amit Mondal
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  4. Golam Mostaeen
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  5. Chanchal K. Roy
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  6. Kevin A. Schneider
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  7. Ralph Deters
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Corresponding author

Correspondence to Debasish Chakroborti .

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Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Department of Computer Engineering Istanbul Medipol University Istanbul, Turkey, Calgary, AB, Canada

    Reda Alhajj

  2. Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada

    Mohammad Moshirpour

  3. Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada

    Behrouz Far

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Chakroborti, D. et al. (2020). A Data Management Scheme for Micro-Level Modular Computation-Intensive Programs in Big Data Platforms. In: Alhajj, R., Moshirpour, M., Far, B. (eds) Data Management and Analysis. Studies in Big Data, vol 65. Springer, Cham. https://doi.org/10.1007/978-3-030-32587-9_9

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  • DOI: https://doi.org/10.1007/978-3-030-32587-9_9

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  • Online ISBN: 978-3-030-32587-9

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