188/4 E-Commerce Group
Institute of Software Technology and Interactive Systems
Vienna University of Technology
Favoritenstrasse 9-11/188, A-1040 Vienna, Austria

Multi-dimensional Information Ordering to Support Decision-making Processes

Authors: 
S. Marchand-Maillet, B. Hofreiter
Publisher: 
IEEE
Proceedings: 
15th IEEE Conference on Business Informatics
Pages: 
Year: 
2013
Type: 
Speech with CD or web proceedings
Hidden Keywords: 
Department Focus: 
Business Informatics
TU Focus: 
Information and Communication Technology
ISBN: 
ISBN: 978-0-7695-5072-5
Abstract: 
Massive amounts of textual and digital data are<br> created daily from business or public activities. The organisation,<br> mining and summarization of such a rich and large<br> information source is required to capture the essential and<br> critical knowledge it contains. Such a mining is of strategic<br> importance in many domains including innovation (eg to mine<br> technological reviews and scientific literature) and electronic<br> commerce (eg to mine customer reviews).<br> Information content generally bears several important aspects,<br> mapped onto visualisation dimensions, whose number<br> needs to be reduced to enable relevant interactive exploration.<br> In this paper, we propose a novel strategy to mine and organise<br> document sets, in order to present them in a consistent manner<br> and to highlight interesting and relevant information patterns<br> they contain.<br> We base our method on the formulation of a global optimisation<br> problem solved by using the Traveling Salesman Problem<br> (TSP) approach. We show how this compact formulation opens<br> interesting possibilities for the mining of document collections<br> mapped onto multi-dimensional information sets. We discuss<br> the issue of scalability and show that associated scalable<br> solutions exist. We demonstrate the effectiveness of our method<br> over several types of documents, embedded into real business<br> cases.
Abstract German: