@inproceedings{ae60c821b3fb4be1ac8d2753a7f2cfde,
title = "Kanban-based framework for analysis of heterogeneous academic data",
abstract = "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.",
keywords = "academic data, bibliometrics, big data, kanban, recommender systems, requirements engineering",
author = "Bedoor Alshebli and Armin Alibasic and Woon, {Wei Lee} and D. Svetinovic",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 25th Telecommunications Forum, TELFOR 2017 ; Conference date: 21-11-2017 Through 22-11-2017",
year = "2018",
month = jan,
day = "5",
doi = "10.1109/TELFOR.2017.8249470",
language = "English (US)",
series = "2017 25th Telecommunications Forum, TELFOR 2017 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--4",
booktitle = "2017 25th Telecommunications Forum, TELFOR 2017 - Proceedings",
}