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Issue No.03 - March (2009 vol.21)
pp: 415-427
Jörg Ontrup , Bielefeld University, Bielefeld
Helge Ritter , Bielefeld University, Bielefeld
Sören W. Scholz , Bielefeld University, Bielefeld
Ralf Wagner , University of Kassel, Kassel
ABSTRACT
The ability to assess the relevance of topics and related sources in information-rich environments is a key to success when scanning business environments. This paper introduces a hybrid system to support managerial information gathering. The system is made up of three components: (1) a hierarchical hyperbolic SOM for structuring the information environment and visualizing the intensity of news activity with respect to identified topics, (2) a spreading activation network for the selection of the most relevant information sources with respect to an already existing knowledge infrastructure, and (3) measures of interestingness for association rules as well as statistical testing facilitates the monitoring of already identified topics. Embedding the system by a framework describing three modes of human information seeking behavior endorses an active organization, exploration and selection of information that matches the needs of decision makers in all stages of the information gathering process. By applying our system in the domain of the hotel industry we demonstrate how typical information gathering tasks are supported. Moreover, we present an empirical study investigating the effectiveness and efficiency of the visualization framework of our system.
INDEX TERMS
Human information processing, Data and knowledge visualization, Clustering, Graphical user interfaces, Search process, Information Search and Retrieval, Information Storage and Retrieval, Information Technology, Text mining, Database Applications, Database Management, Information Technology and Systems
CITATION
Jörg Ontrup, Helge Ritter, Sören W. Scholz, Ralf Wagner, "Detecting, Assessing and Monitoring Relevant Topics in Virtual Information Environments", IEEE Transactions on Knowledge & Data Engineering, vol.21, no. 3, pp. 415-427, March 2009, doi:10.1109/TKDE.2008.149
REFERENCES
 [1] H.A. Simon, “Designing Organizations for an Information-Rich World,” Computers, Comm., and the Public Interest, M.Greenberger, ed., pp. 37-52, 1971. [2] C.-P. Wei and Y.-H. Lee, “Event Detection from Online News Documents for Supporting Environmental Scanning,” Decision Support Systems, vol. 36, pp. 385-401, 2004. [3] R. Decker, R. Wagner, and S. Scholz, “Environmental Scanning in Marketing Planning—An Internet-Based Approach,” Marketing Intelligence and Planning, vol. 23, no. 2, pp. 189-199, 2005. [4] S. Scholz and R. Wagner, “Autonomous Environmental Scanning in the World Wide Web,” IT-Enabled Strategic Management: Increasing Returns for the Organization, B. Walters and Z. Tang, eds., pp. 213-242, Hershey: Idea Group, 2006. [5] M.L. Neugarten, “Seeing and Noticing: An Optical Perspective on Competitive Intelligence,” J. Competitive Intelligence and Management, vol. 1, pp. 93-104, 2003. [6] H. Chen, M. Chau, and D. Zeng, “CI-Spider: A Tool for Competitive Intelligence on the Web,” Decision Support Systems, vol. 34, pp. 1-17, 2002. [7] R.R. Hoffman and J.F. Yates, “Decision(?) Making(?),” IEEE Intelligent Systems, vol. 20, pp. 76-82, 2005. [8] M. Endsley and R.R. Hoffman, “The Sacagawea Principle,” IEEE Intelligent Systems, vol. 17, pp. 80-85, 2002. [9] J. Holland, K. Holyoak, R. Nisbett, and P. Thagard, Induction: Process of Inference, Learning, and Discovery. MIT Press, 1986. [10] B. Wierenga, G.V. Bruggen, and R. Stealin, “The Success of Marketing Management Support Systems,” Marketing Science, vol. 18, pp. 196-207, 1999. [11] J. Ontrup and H. Ritter, “Large-Scale Data Exploration with the Hierarchically Growing Hyperbolic SOM,” Neural Networks, vol. 19, no. 6, pp. 751-761, 2006. [12] J. Lamping and R. Rao, “Laying Out and Visualizing Large Trees Using a Hyperbolic Space,” Proc. Seventh ACM Symp. User Interface Software and Technology (UIST '94), pp. 13-14, 1994. [13] P. Pirolli, S.K. Card, and M.M. van der Wege, “Visual Information Foraging in a Focus $+$ Context Visualization,” Proc. ACM Conf. Human Factors in Computing Systems (CHI '01), CHI Letters, vol. 3, no. 1, pp. 506-513, 2001. [14] H. Ritter, “Self-Organizing Maps in Non-Euclidian Spaces,” Kohonen Maps, E. Oja and S. Kaski, eds., pp.97-110, Amer Elsevier, 1999. [15] J. Walter, J. Ontrup, D. Wessling, and H. Ritter, “Interactive Visualization and Navigation in Large Data Collections Using the Hyperbolic Space,” Proc. Third IEEE Int'l Conf. Data Mining (ICDM'03), Nov. 2003. [16] H.S.M. Coxeter, Non-Euclidean Geometry. Univ. of Toronto Press, 1957. [17] T. Kohonen, Self-Organizing Maps, third ed., ser. Springer Series in Information Sciences, 2001. [18] P. Pirolli and S. Card, “Information Foraging,” Psychological Rev., vol. 106, pp. 643-675, 1999. [19] D.W. Stephens and J.R. Krebs, Foraging Theory. Princeton Univ. Press, 1986. [20] K. Carley and M. Palmquist, “Extracting, Representing, and Analyzing Mental Models,” Social Forces, vol. 34, pp. 921-945, 1995. [21] J.P. Walsh, “Managerial and Organizational Cognition: Notes from a Trip Down Memory Lane,” Organization Science, vol. 6, no. 3, pp. 280-321, 1995. [22] G.P. Hodgkinson, “Cognitive Inertia in a Turbulent Market: The Case of UK Residential Estate Agents,” J. Management Studies, vol. 70, no. 3, pp. 601-636, 1997. [23] D.S. Kaufer and K.M. Carley, Communication at a Distance: The Effect of Print on Socio-Cultural Organization and Change. Lawrence Erlbaum Assoc., 1993. [24] J.R. Anderson, The Adaptive Control of Thought. Lawrence Erlbaum, 1990. [25] S. Scholz and R. Wagner, “The Quality of Prior Information Structure in Business Planning: An Experiment in Environmental Scanning,” Proc. Operations Research (OR '04), pp. 238-245, 2005. [26] W. Boulding, M. Moore, R. Staelin, K. Corfman, P. Dickson, G. Fitzsimons, S. Gupta, D. Lehmann, D. Mitchell, J. Urbany, and B. Weitz, “Understanding Managers' Strategic Decision Making Process,” Marketing Letters, vol. 5, no. 4, pp. 413-426, 1994. [27] B. Liu, Y. Ma, and R. Lee, “Analyzing the Interestingness of Association Rules from the Temporal Dimension,” Proc. First IEEE Int'l Conf. Data Mining (ICDM '01), pp. 377-384, 2001. [28] S. Baron, M. Spiliopoulou, and O. Günther, “Efficient Monitoring of Patterns in Data Mining Environments,” Proc. Seventh East European Conf. Advances in Databases and Information Systems (ADBIS '03), pp. 253-265, 2003. [29] C.-H. Lee, M.-S. Chen, and C.-R. Lin, “Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules,” IEEE Trans. Knowledge and Data Eng., vol. 15, pp. 1004-1017, 2003. [30] R. Hilderman and H. Hamilton, “Evaluation of Interestingness Measures for Ranking Discovered Knowledge,” Advances in Knowledge Discovery and Data Mining, G. Williams and Q. Li, eds., pp. 247-259, 2001. [31] S. Brin, R. Motwani, J. Ullman, and S. Tsur, “Dynamic Itemset Counting and Implication Rules for Market Basket Data,” Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '97), J.Peckham, ed., pp. 255-264, 1997. [32] R. Wagner, Mining Promising Qualification Patterns, D. Baier and K.Wernecke, eds., pp. 249-256, Springer, 2005. [33] S. Kaski, K. Lagus, T. Honkela, and T. Kohonen, “WEBSOM— Self-Organizing Maps of Document Collections,” Neurocomputing, vol. 21, pp. 101-117, 1998. [34] J. Lamping, R. Rao, and P. Pirolli, “A Focus $+$ Content Technique Based on Hyperbolic Geometry for Viewing Large Hierarchies,” Proc. ACM SIGCHI Conf. Human Factors in Computing Systems (CHI'95), pp. 401-408, May 1995. [35] M. Czerwinski and K. Larson, “The New Web Browsers: They're Cool but Are They Useful,” Proc. Seventh Int'l Conf. Human-Computer Interaction (HCI '97), People and Computers XII, 1997. [36] K. Mullet, C. Fry, and D. Schiano, “On Your Marks, Get Set, Browse!” Proc. ACM Conf. Human Factors in Computing Systems (CHI), 1997. [37] P. Pirolli, S. Card, and M. van der Wege, “The Effects of Information Scent on Visual Search in the Hyperbolic Tree Browser,” ACM Trans. Computer-Human Interaction (TOCHI '03), vol. 10, no. 1, pp. 20-53, 2003.