Authors:
Abhishek Santra
;
Hafsa Billah
and
Sharma Chakravarthy
Affiliation:
Information Technology Lab, CSE Department, University of Texas at Arlington, Texas, U.S.A.
Keyword(s):
Multilayer Networks, Modeling, Analysis, Big Data.
Abstract:
In this position paper, we make a case for the appropriateness, utility, and effectiveness of graph models for big data analysis focusing on Multilayer Networks (or MLNs) – a specific type of graph. MLNs have been shown to be more appropriate for modeling complex data compared to their traditional counterparts. MLNs have also been shown to be useful for diverse data types, such as videos and information integration. Further, MLNs have been shown to be flexible for computing analysis objectives from diverse application domains using extant and new algorithms. There is research for automating the modeling of MLNs using widely used EER (Enhanced/Extended Entity Relationship) or Unified Modeling Language (UML) approaches. We start by discussing different graph models and their benefits and limitations. We demonstrate how MLNs can be effectively used to model applications with complex data. We also summarize the work on the use of EER models to generate MLNs in a principled manner. We ela
borate on analysis alternatives provided by MLNs and their ability to match analysis needs. We show the use of MLNs for - i) traditional data analysis, ii) video content analysis, iii) complex data analysis, and iv) propose the use of MLNs for information integration or fusion. We show examples drawn from the literature of their modeling and analysis usage. We conclude that graphs, specifically MLNs provide a rich alternative to model and analyze big data. Of course, this certainly does not preclude newer data models that are likely to come along.
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