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This paper presents methods for detecting, assessing, and monitoring relevant topics in virtual information environments, focusing on the challenges posed by information overload. It introduces automated Environmental Scanning (ES) systems that help managers efficiently interpret business information, employing techniques like Hierarchically Growing Hyperbolic Self-Organizing Maps (H2SOM) and Information Foraging Theory (IFT). The study evaluates the performance and usability of these systems, highlighting their role in information discovery, organization, and maintenance, ultimately improving decision-making processes in business management.
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Detecting, Assessing and Monitoring Relevant Topics in Virtual Information Environments Jo¨ rgOntrup, Helge Ritter, So¨ ren W. Scholz, and Ralf Wagner TKDE, Vol.21, No. 3, 2009, pp. 415-427. Presenter : Wei-Shen Tai 2009/4/8
Outline • Introduction • Managerial information seeking • Methods • Hierarchically growing hyperbolic self-organizing maps • Information foraging theory • Assessment of association rules and statistical testing for changes • Performance evaluation • Usability evaluation • Discussion and conclusions • Comments
Motivation • Environmental Scanning (ES) activities are hampered by an information overload • It caused by the dramatic increase of relevant documents and messages emitted. • Managers need efficient ways to understand their business environment as well as to integrate this understanding into their planning and decision-making processes.
Objective • Automated ES systems • Supports the limited information processing capacity of humans. • Facilitates sensitiveand context dependent reductions of the information overload.
Managerial information seeking • Situation awareness • A manager identifies a topic relevant to his or her business decisions, he or she is interested in precise information and, particularly, in changes of the relations of facts. • Application domain • Example of 2,314 documents obtained from the Internet-based hospitality industry newsletter, ehotelier.com.
Hierarchically growing hyperbolic SOM • Hierarchically Growing Hyperbolic SOM (H2SOM) • Node’s quantization error QE as the growth criterion. If a given threshold QE for a node is exceeded, that node is expanded.
Hierarchical Document Organization • Labeling • Terms correspond to the maximal values in the prototype vectors. • Interactive message level display • Each node represents a subset of messages, which can be displayed via “drill down “.
Topic Detection in Document Streams • Time-dependent activation potential • A distinct peak dominates the message landscape.
Information foraging theory (IFT) • Information scent • ghi is appraised by means of its relevance in the actual context. Ak is the relevance of a term k via Bayesian prediction. • Information diet • B is total time spent on searching this information, T is the total time spent on extracting and handling the relevant information.
Assessment of association rules andstatistical testing for changes • Lift and interestingness • Statistical testing with the measures of interestingness • For rule 1 (hotel chain reports), χ2(A →C)=11.82. In contrast, for rule 2 (Bali attacks), χ2(A →C)= 53.10, and for rule 3 (Iraq war), χ2(A →C)= 65.63.
Performance evaluation • Fast tree search capability of the H2SOM • Usability evaluation • The degree of completion of both tasks is equal or significantly lower for subjects using the standard tree browser.
Discussion and conclusions • An intelligent system for supporting ES process • Discovery of new information • H2SOM and an interactive visual exploration. • Expansion of knowledge • IFT to digest relevant information sources. • Monitoring of already identified topics • More precise assessment of changes in the document stream.
Comments • Advantage • This hybrid intelligent system provides an interactive information exploration tool via visual interface. • It can be integrated into discovery, expansion, and monitoring concepts in cognitive phases of ES. • Drawback • It lacks of enough persuasiveness to determine the branching factor nbas an esthetic view. • The growth threshold Θ QE was set to zero but limited the expansion of the network to a depth of five hierarchy levels. • Application • Information discovery, organizing and maintenance.