Approaches to analyse corporate tags for business intelligence purposes

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.

REFERENCES

• 1: Rakesh Agrawal , Tomasz Imieliński , Arun Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.207-216, May 25-28, 1993, Washington, D.C., United States
• 2: Alavi, M., and Leidner, D. E. Knowledge management and knowledge management systems - conceptual foundations and research issues. MIS Quarterly 25, 1 (2001).
• 3: Aaron Ceglar , John F. Roddick, Association mining, ACM Computing Surveys (CSUR), v.38 n.2, p.5-es, 2006 [doi>10.1145/1132956.1132958]
• 4: Cleveland, R., Cleveland, W., McRae, J., and Terpenning, I. STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics 6, 1 (1990), 3--73.
• 5: Peter F. Drucker, Post-Capitalist Society, HarperCollins Publishers, New York, NY, 1994
• 6: Edmunds, A., and Morris, A. The problem of information overload in business organisations: a review of the literature. International Journal of Information Management 20, 1 (2000), 17--28.
• 7: John, A., and Seligmann, D. Collaborative tagging and expertise in the enterprise. In Collaborative Web Tagging Workshop at WWW2006, Edinburgh (2006).
• 8: Zack Jourdan , R. Kelly Rainer , Thomas E. Marshall, Business Intelligence: An Analysis of the Literature, Information Systems Management, v.25 n.2, p.121-131, March 2008 [doi>10.1080/10580530801941512]
• 9: Makridakis, S., Wheelwright, S., et al. Forecasting: methods and applications. New York: John Wiley & Sons, 1978.
• 10: Millen, D., and Feinberg, J. Using social tagging to improve social navigation. In Workshop on the Social Navigation and Community based Adaptation Technologies (2006).
• 11: David Millen , Jonathan Feinberg , Bernard Kerr, Social bookmarking in the enterprise, Queue, v.3 n.9, November 2005 [doi>10.1145/1105664.1105676]
• 12: Nonaka, I., and Takeuchi, H. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, May 1995.
• 13: Ross, S. The Economic Theory of Agency: The Principal's Problem. American Economic Review 63, 2 (1973), 134--139.
• 14: Scott Spangler , Jeffrey Kreulen, Mining the Talk: Unlocking the Business Value in Unstructured Information (IBM Press), IBM Press, 2007
• 15: Van Damme, C., Coenen, T., and Vandijck, E. Deriving a lightweight corporate ontology from a corporate folksonomy. In Proceedings of the 11th International Conference on Business Information Systems (BIS 2008) (2008), Springer, pp. 207--216.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing

General Terms:
Algorithms

Keywords:
algorithms, business intelligence, corporate tags, unstructured information