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MapReduce: Review and open challenges

Author

Listed:
  • Ibrahim Abaker Targio Hashem

    (University of Malaya)

  • Nor Badrul Anuar

    (University of Malaya)

  • Abdullah Gani

    (University of Malaya)

  • Ibrar Yaqoob

    (University of Malaya)

  • Feng Xia

    (Dalian University of Technology)

  • Samee Ullah Khan

    (North Dakota State University)

Abstract

The continuous increase in computational capacity over the past years has produced an overwhelming flow of data or big data, which exceeds the capabilities of conventional processing tools. Big data signify a new era in data exploration and utilization. The MapReduce computational paradigm is a major enabler for underlying numerous big data platforms. MapReduce is a popular tool for the distributed and scalable processing of big data. It is increasingly being used in different applications primarily because of its important features, including scalability, fault tolerance, ease of programming, and flexibility. Thus, bibliometric analysis and review was conducted to evaluate the trend of MapReduce research assessment publications indexed in Scopus from 2006 to 2015. This trend includes the use of the MapReduce framework for big data processing and its development. The study analyzed the distribution of published articles, countries, authors, keywords, and authorship pattern. For data visualization, VOSviewer program was used to produce distance- and graph-based maps. The top 10 most cited articles were also identified based on the citation count of publications. The study utilized productivity measures, domain visualization techniques and co-word to explore papers related to MapReduce in the field of big data. Moreover, the study discussed the most influential articles contributed to the improvements in MapReduce and reviewed the corresponding solutions. Finally, it presented several open challenges on big data processing with MapReduce as future research directions.

Suggested Citation

  • Ibrahim Abaker Targio Hashem & Nor Badrul Anuar & Abdullah Gani & Ibrar Yaqoob & Feng Xia & Samee Ullah Khan, 2016. "MapReduce: Review and open challenges," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 389-422, October.
    • Handle: RePEc:spr:scient:v:109:y:2016:i:1:d:10.1007_s11192-016-1945-y
    DOI: 10.1007/s11192-016-1945-y
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    References listed on IDEAS

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    1. Lokman I. Meho & Kiduk Yang, 2007. "Impact of data sources on citation counts and rankings of LIS faculty: Web of science versus scopus and google scholar," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(13), pages 2105-2125, November.
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    Cited by:

    1. Fernando Garrigós-Simón & Silvia Sanz-Blas & Yeamduan Narangajavana & Daniela Buzova, 2021. "The Nexus between Big Data and Sustainability: An Analysis of Current Trends and Developments," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    2. Altarturi, Hamza H.M. & Saadoon, Muntadher & Anuar, Nor Badrul, 2020. "Cyber parental control: A bibliometric study," Children and Youth Services Review, Elsevier, vol. 116(C).
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    4. Hashem, Ibrahim Abaker Targio & Chang, Victor & Anuar, Nor Badrul & Adewole, Kayode & Yaqoob, Ibrar & Gani, Abdullah & Ahmed, Ejaz & Chiroma, Haruna, 2016. "The role of big data in smart city," International Journal of Information Management, Elsevier, vol. 36(5), pages 748-758.
    5. Salvatore Carta & Alessandro Sebastian Podda & Diego Reforgiato Recupero & Roberto Saia, 2020. "A Local Feature Engineering Strategy to Improve Network Anomaly Detection," Future Internet, MDPI, vol. 12(10), pages 1-30, October.
    6. Centobelli, Piera & Cerchione, Roberto & Esposito, Emilio & Oropallo, Eugenio, 2021. "Surfing blockchain wave, or drowning? Shaping the future of distributed ledgers and decentralized technologies," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    7. P. V. Thayyib & Rajesh Mamilla & Mohsin Khan & Humaira Fatima & Mohd Asim & Imran Anwar & M. K. Shamsudheen & Mohd Asif Khan, 2023. "State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary," Sustainability, MDPI, vol. 15(5), pages 1-38, February.

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