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International Conference on Big Data Analytics

BDA 2013: Big Data Analytics pp 116–132Cite as

Challenges and Approaches for Large Graph Analysis Using Map/Reduce Paradigm

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8302))

Abstract

Analysis of big graphs has become possible due to the convergence of many technologies such as server farms, new paradigms for processing data in massively parallel ways (e.g., Map/Reduce, Bulk Synchronous Parallelization), as well as the ability to process unstructured data. This has allowed one to solve problems that were not possible (or extremely time consuming) earlier. Many algorithms are being mapped to new paradigms to deal with larger versions with a meaningful response time.

This paper analyses a few related problems in the context of graph analysis. Our goal is to analyse the challenges of adapting/extending algorithms for graph analysis (graph mining, graph matching and graph search) to exploit the Map/Reduce paradigm for very large graph analysis in order to achieve scalability. We intend to explore alternative paradigms that may be better suited for a class of applications.

As an example, finding interesting and repetitive patterns from graphs forms the crux of graph mining. For processing large social network and other graphs, extant main memory and disk-based) approaches are not appropriate. Hence, it is imperative that we explore massively parallel paradigms for their processing to achieve scalability. This is also true for other problems such as in-exact or approximate match of graphs, and answering queries on large data sets represented as graphs (e.g. Freebase). In this paper, we identify the challenges of harmonizing the existing approaches with Map/Reduce and our preliminary approaches to solve these problems using the new paradigm.

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Author information

Authors and Affiliations

  1. University of Texas at Arlington, USA

    Soumyava Das & Sharma Chakravarthy

Authors
  1. Soumyava Das
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  2. Sharma Chakravarthy
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Editor information

Editors and Affiliations

  1. Department of Computer Science, South Asian University, Akhar Bhavan, 110021, Chanakyapuri, New Delhi, India

    Vasudha Bhatnagar

  2. International Institute of Information Technology, Bangalore, India

    Srinath Srinivasa

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© 2013 Springer International Publishing Switzerland

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Das, S., Chakravarthy, S. (2013). Challenges and Approaches for Large Graph Analysis Using Map/Reduce Paradigm. In: Bhatnagar, V., Srinivasa, S. (eds) Big Data Analytics. BDA 2013. Lecture Notes in Computer Science, vol 8302. Springer, Cham. https://doi.org/10.1007/978-3-319-03689-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-03689-2_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03688-5

  • Online ISBN: 978-3-319-03689-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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