Design of BBS with Visual Representation for Online Data Analysis
A concept of bulletin board system (BBS) equipped with information visualization techniques is proposed for supporting online data analysis. Although a group discussion is known to be effective for analyzing data from various viewpoints, the number of participants has to be limited in terms of time and space constraints. To solve the problem, this paper proposes to augment BBS, which is one of popular tools on the Web. In order for discussion participants to share the data online, the system provides them with a visual representation of target data, with functions for supporting comment generation as well as retrieving posted comments. In order to show the potential of the concept, a BBS equipped with KeyGraph is also developed for supporting online chance discovery. It has functions for making visual annotations on the KeyGraph, as well as a function for retrieving similar scenarios. The experimental result shows the effectiveness of the BBS in terms of the usefulness of scenario generation support functions as well as that of scenario retrieval engines.
KeywordsBulletin board system (BBS) online data analysis information visualization chance discovery scenario generation
Unable to display preview. Download preview PDF.
- 1.Baeza-Yates R, Ribeiro-Neto B (1999) 25. Classic information retrieval. In: Modern Information Retrieval. Addison Wesley 24–33Google Scholar
- 2.Callan J. P, Croft W. B, Harding S. M (1992) The INQUERY Retrieval System. In: Proc. of 3rd International Conference on Database and Expert Systems Applications (DEXA-92) 78–83Google Scholar
- 3.Iwase Y, Takama Y (2005) Data annotation based on scenario in chance discovery process. In: Workshop on Rough Sets and Chance Discovery, in 8th Joint Conference on Information Sciences (JCIS2005) 1797–1800Google Scholar
- 6.Liora X, Goldberg D. E, Ohsawa Y, Ohnishi K, Tamura H, Washida Y, Yoshikawa M (2004) Chance and Marketing: On-line Conversation Analysis for Creative Scenario Discussion. In: 1st European Workshop on Chance Discovery (EWCD’2004) 152–161Google Scholar
- 9.Ohsawa Y, Benson NE, Yachida M (1998) KeyGraph: Automatic indexing by co-occurrence graph based on building construction metaphor. In: Proc. of Advances in Digital Libraries Conference 12–18Google Scholar
- 10.Ohsawa Y (2003) 18. KeyGraph: Visualized structure among event clusters. In: Ohsawa Y, McBurney P (eds) Chance Discovery. Springer 262–275Google Scholar
- 12.Robertson S. E, Walker S, Hancock-Beaulieu M, Gull A, Lau M (1992) Okapi at TREC 3. In: Text REtrieval Conference 21–30Google Scholar
- 13.Singhal A, Buckley C, Mitra M (1996) Pivoted Document Length Normalization. In: Research and Development in Information Retrieval 21–29Google Scholar
- 16.Takama Y, Ohsawa Y (2003) 13. Effects of scenic information. In: Ohsawa Y, McBurney P (eds) Chance Discovery. Springer 184–188Google Scholar
- 17.Takama Y, Kajinami T (2004) Keyword pair extraction for relevance feedback based on interactive keyword map. In: 1st European Workshop on Chance Discovery in ECAI2004 41–50Google Scholar
- 18.Takama Y, Iwase Y (2005) Scenario to data mapping for chance discovery process. In: Abraham A, Dote Y, Furuhashi T, Koppen M, Ohuchi A, Ohsawa Y (eds) Proc. of 4th IEEE International Workshop on WSTST’05, Soft Computing as Transdisciplinary Science and Technology 470–479Google Scholar