Abstract

Business processes are generally fixed and enforced strictly, as reflected by the static nature of underlying software systems and datasets. However, internal and external situations, organizational changes and various other factors trigger dynamism, which is reflected in the form of issues, complains, Q & A, opinions, reviews, etc., over a plethora of communication channels, such as email, chat, discussion forums, and internal social network. Careful and timely analysis and processing of such channels may lead to early detection of emerging trends, critical issues, opportunities, topics of interests, contributors, experts, etc. Social network analytics have been successfully applied in general purpose, online social network platforms, like Facebook and Twitter. However, in order for such techniques to be useful in business context, it is mandatory to integrate them with underlying business systems, processes and practices. Such integration problem is increasingly recognized as Big Data problem. We argue that Semantic Web technology applied with social network analytics can solve enterprise knowledge management, while achieving integration.

Keywords

Cite this article

@article{cai-2214,
  author  = {Charalampos Chelmis and Hao Wu and Vikram Sorathia and Viktor K. Prasanna},
  title   = {Semantic Social Network Analysis for an Enterprise},
  journal = {Computing and Informatics},
  volume  = {33},
  number  = {3},
  pages   = {479--502},
  year    = {2014},
  issn    = {1335-9150},
  url     = {https://cai.type.sk/content/2014/3/semantic-social-network-analysis-for-an-enterprise/}
}
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