Kanban-based framework for analysis of heterogeneous academic data

Bedoor Alshebli, Armin Alibasic, Wei Lee Woon, D. Svetinovic

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Bibliometric techniques are widely used to study the factors leading to successful research, though this is not without its challenges. One notable problem is that academic data is very diverse, and involves complex interactions between many different entities and players. In this paper, a novel framework for analyzing heterogeneous academic data is proposed. While such a framework would have many different applications, this paper focuses on the design of an academic recommendation system, which is one interesting use case for this framework.

Original languageBritish English
Title of host publication2017 25th Telecommunications Forum, TELFOR 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781538630723
StatePublished - 5 Jan 2018
Event25th Telecommunications Forum, TELFOR 2017 - Belgrade, Serbia
Duration: 21 Nov 201722 Nov 2017

Publication series

Name2017 25th Telecommunications Forum, TELFOR 2017 - Proceedings


Conference25th Telecommunications Forum, TELFOR 2017


  • academic data
  • bibliometrics
  • big data
  • kanban
  • recommender systems
  • requirements engineering


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