Holistic analysis of multi-source, multi-feature data: Modeling and computation challenges

Abhishek Santra, Sanjukta Bhowmick

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


As a result of our increased ability to collect data from different sources, many real-world datasets are increasingly becoming multi-featured and these features can also be of different types. Examples of such multi-feature data include different modes of interactions among people (Facebook, Twitter, LinkedIn,..) or traffic accidents associated with diverse factors (speed, light conditions, weather,..). Efficiently modeling and analyzing these complex datasets to obtain actionable knowledge presents several challenges. Traditional approaches, such as using single layer networks (or monoplexes) may not be sufficient or appropriate for modeling and computation scalability. Recently, multiplexes have been proposed for the elegant handling of such data. In this position paper, we elaborate on different types of multiplexes (homogeneous, heterogeneous and hybrid) for modeling different types of data. The benefits of this modeling in terms of ease, understanding, and usage are highlighted. However, this model brings with it a new set of challenges for its analysis. The bulk of the paper discusses these challenges and the advantages of using this approach. With the right tools, both computation and storage can be reduced in addition to accommodating scalability.

Original languageEnglish (US)
Title of host publicationBig Data Analytics - 5th International Conference, BDA 2017, Proceedings
EditorsAshish Sureka, Sharma Chakravarthy, P. Krishna Reddy, Subhash Bhalla
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319724126
StatePublished - 2017
Event5th International Conference on Big Data Analytics, BDA 2017 - Hyderabad, India
Duration: Dec 12 2017Dec 15 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10721 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other5th International Conference on Big Data Analytics, BDA 2017


  • Aggregation functions
  • Big data analytics
  • Graph analysis and query processing
  • Lossless composability
  • Multi-source, disparate data
  • Multiplex

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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