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Search Results (14)
  • Open Access

    ARTICLE

    Core – An Optimal Data Placement Strategy in Hadoop for Data Intentitive Applications Based on Cohesion Relation

    Vengadeswaran, Balasundaram

    Computer Systems Science and Engineering, Vol.34, No.1, pp. 47-60, 2019, DOI:10.32604/csse.2019.34.047

    Abstract The tremendous growth of data being generated today is making storage and computing a mammoth task. With its distributed processing capability Hadoop gives an efficient solution for such large data. Hadoop’s default data placement strategy places the data blocks randomly across the nodes without considering the execution parameters resulting in several lacunas such as increased execution time, query latency etc., Also, most of the data required for a task execution may not be locally available which creates data-locality problem. Hence we propose an innovative data placement strategy based on dependency of data blocks across the nodes. Our strategy dynamically analyses… More >

  • Open Access

    ARTICLE

    Forensic Investigation Through Data Remnants on Hadoop Big Data Storage System

    Myat Nandar Oo1, Sazia Parvin2, Thandar Thein3

    Computer Systems Science and Engineering, Vol.33, No.3, pp. 203-217, 2018, DOI:10.32604/csse.2018.33.203

    Abstract Forensic examiners are in an uninterrupted battle with criminals in the use of Big Data technology. The underlying storage system is the main scene to trace the criminal activities. Big Data Storage System is identified as an emerging challenge to digital forensics. Thus, it requires the development of a sound methodology to investigate Big Data Storage System. Since the use of Hadoop as Big Data Storage System continues to grow rapidly, investigation process model for forensic analysis on Hadoop Storage and attached client devices is compulsory. Moreover, forensic analysis on Hadoop Big Data Storage System may take additional time without… More >

  • Open Access

    ARTICLE

    Sentiment Analysis System in Big Data Environment

    Wint Nyein Chan1, Thandar Thein2

    Computer Systems Science and Engineering, Vol.33, No.3, pp. 187-202, 2018, DOI:10.32604/csse.2018.33.187

    Abstract Nowadays, Big Data, a large volume of both structured and unstructured data, is generated from Social Media. Social Media are powerful marketing tools and social big data can offer the business insights. The major challenge facing social big data is attaining efficient techniques to collect a large volume of social data and extract insights from the huge amount of collected data. Sentiment Analysis of social big data can provide business insights by extracting the public opinions. The traditional analytic platforms need to be scaled up for analyzing a large volume of social big data. Social data are by nature shorter… More >

  • Open Access

    ARTICLE

    An Optimized Resource Scheduling Strategy for Hadoop Speculative Execution Based on Non-cooperative Game Schemes

    Yinghang Jiang1, Qi Liu2,3,*, Williams Dannah1, Dandan Jin2, Xiaodong Liu3, Mingxu Sun4,*

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 713-729, 2020, DOI:10.32604/cmc.2020.04604

    Abstract Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes. “Straggling” tasks, however, have a serious impact on task allocation and scheduling in a Hadoop system. Speculative Execution (SE) is an efficient method of processing “Straggling” Tasks by monitoring real-time running status of tasks and then selectively backing up “Stragglers” in another node to increase the chance to complete the entire mission early. Present speculative execution strategies meet challenges on misjudgement of “Straggling” tasks and improper selection of backup nodes, which leads to inefficient implementation of speculative executive processes. This paper has proposed an Optimized Resource… More >

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