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Big Data and MapReduce Challenges, Opportunities and Trends

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 6 No. 6 (2016)

DOI
10.11591/ijece.v6i6.10555
ISSN
2088-8708, 2088-8708
Published
2016-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc-nd/4.0

Abstract

Nowadays we all are surrounded by Big data. The term ‘Big Data’ itself indicates huge volume, high velocity, variety and veracity i.e. uncertainty of data which gave rise to new difficulties and challenges. Big data generated may be structured data, Semi Structured data or unstructured data. For existing database and systems lot of difficulties are there to process, analyze, store and manage such a Big Data. The Big Data challenges are Protection, Curation, Capture, Analysis, Searching, Visualization, Storage, Transfer and sharing. Map Reduce is a framework using which we can write applications to process huge amount of data, in parallel, on large clusters of commodity hardware in a reliable manner. Lot of efforts have been put by different researchers to make it simple, easy, effective and efficient. In our survey paper we emphasized on the working of Map Reduce, challenges, opportunities and recent trends so that researchers can think on further improvement.

Collaboration network of the authors

8 authors · 10 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Koneru Lakshmaiah Education Foundation
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
A. Kousar NikhathKoneru Lakshmaiah Education Foundation1
K Varada RajkumarKoneru Lakshmaiah Education Foundation1
Adimulam YesubabuAffiliation unknown1
T. Vijaya SaradhiKoneru Lakshmaiah Education Foundation1
Ch. V. Phani KrishnaKoneru Lakshmaiah Education Foundation1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
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