Christian Stab
Fraunhofer Institute for Computer Graphics Research IGD, Germany
Matthias Breyer
Fraunhofer Institute for Computer Graphics Research IGD, Germany
Dirk Burkhardt
Fraunhofer Institute for Computer Graphics Research IGD, Germany
Kawa Nazemi
Fraunhofer Institute for Computer Graphics Research IGD, Germany
Jörn Kohlhammer
Fraunhofer Institute for Computer Graphics Research IGD, Germany
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Published in: Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29-30; 2012; Växjö; Sweden
Linköping Electronic Conference Proceedings 81:11, p. 83-86
Published: 2012-11-20
ISBN: 978-91-7519-723-4
ISSN: 1650-3686 (print), 1650-3740 (online)
Considering the increasing pressure of competition and high dynamics of markets; the early identification and specific handling of novel developments and trends becomes more and more important for competitive companies. Today; those signals are encoded in large amounts of textual data like competitors’ web sites; news articles; scientific publications or blog entries which are freely available in the web. Processing large amounts of textual data is still a tremendous challenge for current business analysts and strategic decision makers. Although current information systems are able to process that amount of data and provide a wide range of information retrieval tools; it is almost impossible to keep track of each thread or opportunity. The presented approach combines semantic search and data mining techniques with interactive visualizations for analyzing and identifying weak signals in large text collections. Beside visual summarization tools; it includes an enhanced trend visualization that supports analysts in identifying latent topic-related relations between competitors and their temporal relevance. It includes a graph-based visualization tool for representing relations identified during semantic analysis. The interaction design allows analysts to verify their retrieved hypothesis by exploring the documents that are responsible for the current view.
H.5.2 [Information Interfaces and Presentations]: User Interfaces—Graphical user interfaces (GUI); Interaction styles H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing—Abstracting methods
[Agu67] AGUILAR F. J.: Scanning the business environment. Collier-Macmillan; 1967. 83
[Ans75] ANSOFF I. H.: Managing Strategic surprise by response to weak signals. California Management Review 18; 2 (1975); 21–33. 83
[BYRN10] BAEZA-YATES R.; RIBEIRO-NETO B.: Modern Information Retrieval; 2nd ed. Addison-Wesley Publishing Company; 2010. 83
[DWCR11] DOU W.; WANG X.; CHANG R.; RIBARSKY W.: Paralleltopics: A probabilistic approach to exploring document collections. In Visual Analytics Science and Technology (VAST); 2011 IEEE Conference on (oct. 2011); pp. 231 –240. 84; 86
[FMG05] FORTUNA B.; MLADENIC D.; GROBELNIK M.: Visualization of Text Document Corpus. Informatica Journal 29; 4 (2005); 497–502. 84; 86
[GACoR05] GAMON M.; AUE A.; CORSTON-OLIVER S.; RINGGER E.: Pulse: Mining customer opinions from free text. In Proc. of the 6th International Symposium on Intelligent Data Analysis (2005); pp. 121–132. 86
[GM04] GROBELNIK M.; MLADENIC D.: Visualization of news articles. In SIKDD 2004 at multiconference IS (2004). 84; 86
[LZP12] LIU S.; ZHOU M. X.; PAN S.; SONG Y.; QIAN W.; CAI W.; LIAN X.: Tiara: Interactive; topic-based visual text summarization and analysis. ACM Trans. Intell. Syst. Technol. 3; 2 (Feb. 2012); 25:1–25:28. 84; 86
[MZ05] MEI Q.; ZHAI C.: Discovering evolutionary theme patterns from text: An exploration of temporal text mining. In Proc. of ACM SIGKDD (2005); KDD ’05; ACM; pp. 198–207. 84
[PM06] PAULOVICH F.; MINGHIM R.: Text map explorer: a tool to create and explore document maps. In Information Visualization; 2006. IV 2006. (july 2006); pp. 245 –251. 84; 86
[Sch05] SCHWARZ J. O.: Pitfalls in implementing a strategic early warning system. Foresight - The journal of future studies; strategic thinking and policy 7; 4 (Apr. 2005); 22–30. 83
[ZS06] ZIEGLER C.-N.; SKUBACZ M.: Towards automated reputation and brand monitoring on the web. In Proc. of IEEE/WIC/ACM (2006); WI ’06; pp. 1066–1072. 84; 86
