1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology Detecting, Assessing and Monitoring Relevant Topics in Virtual Information Environments Jo¨ rg Ontrup, 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

2. N.Y.U.S.T. I. M. Intelligent Database Systems Lab 2 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

3. N.Y.U.S.T. I. M. Intelligent Database Systems Lab 3 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.

4. N.Y.U.S.T. I. M. Intelligent Database Systems Lab 4 Objective Automated ES systems  Supports the limited information processing capacity of humans.  Facilitates sensitive and context dependent reductions of the information overload.

5. N.Y.U.S.T. I. M. Intelligent Database Systems Lab 5 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.

6. N.Y.U.S.T. I. M. Intelligent Database Systems Lab 6 Hierarchically growing hyperbolic SOM Hierarchically Growing Hyperbolic SOM (H 2 SOM)  Node’s quantization error QE as the growth criterion. If a given threshold QE for a node is exceeded, that node is expanded.

7. N.Y.U.S.T. I. M. Intelligent Database Systems Lab 7 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 “.

8. N.Y.U.S.T. I. M. Intelligent Database Systems Lab 8 Topic Detection in Document Streams  Time-dependent activation potential  A distinct peak dominates the message landscape.

9. N.Y.U.S.T. I. M. Intelligent Database Systems Lab 9 Information foraging theory (IFT)  Information scent g hi is appraised by means of its relevance in the actual context. A k 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.

10. N.Y.U.S.T. I. M. Intelligent Database Systems Lab 10 Assessment of association rules and statistical testing for changes  Lift and interestingness  Statistical testing with the measures of interestingness For rule 1 (hotel chain reports), χ 2(A →C)=11.82. In contrast, for rule 2 (Bali attacks), χ 2(A →C)= 53.10, and for rule 3 (Iraq war), χ 2(A →C)= 65.63.

11. N.Y.U.S.T. I. M. Intelligent Database Systems Lab 11 Performance evaluation  Fast tree search capability of the H 2 SOM  Usability evaluation The degree of completion of both tasks is equal or significantly lower for subjects using the standard tree browser.

12. N.Y.U.S.T. I. M. Intelligent Database Systems Lab 12 Discussion and conclusions An intelligent system for supporting ES process  Discovery of new information H 2 SOM 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.

13. N.Y.U.S.T. I. M. Intelligent Database Systems Lab 13 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 n b as 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.