Approaches to analyse corporate tags for business intelligence purposes

Authors:
Céline Van Damme
Affiliations:
Vrije Universiteit Brussel, Brussels, Belgium
Venue:
OBI '08 Proceedings of the first international workshop on Ontology-supported business intelligence
Year:
2008

Citing 8
Cited 0

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Post-Capitalist Society

Post-Capitalist Society
Social Bookmarking in the Enterprise

Queue - Social Computing
Association mining

ACM Computing Surveys (CSUR)
Mining the Talk: Unlocking the Business Value in Unstructured Information (IBM Press)

Mining the Talk: Unlocking the Business Value in Unstructured Information (IBM Press)
Business Intelligence: An Analysis of the Literature

Information Systems Management
Review: Knowledge management and knowledge management systems: conceptual foundations and research issues

MIS Quarterly
The problem of information overload in business organisations: a review of the literature

International Journal of Information Management: The Journal for Information Professionals

Quantified Score

Hi-index	0.00

Visualization

Abstract

The information overload in business organizations hampers the information analysis process. Business intelligence tools can be used to analyse large amounts of information, however in most cases they only focus on structured information. More and more companies annotate tags to unstructured information to improve the information retrieval. We propose to exploit tags and tagging data to generate business intelligence. We believe that the analysis of large amounts of unstructured information for business intelligence purposes can be reduced to analysing tags and tag data. In the paper, we suggest that (1) tags and tag data can produce business intelligence from large amounts of unstructured information provided that some prerequisites are taken into account, (2) propose two step-by-step approaches of how existing mining and statistical techniques can be applied on tags and tagged data (3) by means of a tag data set from a European company, we provide preliminary evidence that the proposed approaches applied on corporate tags produce promising results regarding business intelligence.