diva: a visualization system for exploring document databases for technology forecasting

• Published in: Computers & industrial engineering Vol. 43; no. 4; pp. 841 - 862
• Main Authors: Morris, Steven, DeYong, Camille, Wu, Zheng, Salman, Sinan, Yemenu, Dagmawi
• Format: Journal Article
• Language: English
• Published: Seoul Elsevier Ltd 01.09.2002; Oxford Pergamon Press; New York, NY Pergamon Press Inc
• Subjects: Applied sciences; Bibliographic data bases; Bibliometrics; Citation analysis; Computer science; control theory; systems; Data mining; Document mapping; Documents; Exact sciences and technology; Forecasting techniques; Information; Information systems. Data bases; Information visualization; KDD; Knowledge discovery in databases; Knowledge management; Memory organisation. Data processing; Scientometrics; Software; Studies; Technology; Technology forecasting; Visualization; Document mapping; Scientometrics; Information visualization; Citation analysis; Bibliometrics; Knowledge discovery in databases; Technology forecasting; KDD; Data mining; Scientific literature; Prospective; Patents; Data visualization; Classification; Knowledge discovery; Patent rights; Document analysis
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/S0360-8352(02)00143-2

Database Information Visualization and Analysis system ( diva) is a computer program that helps perform bibliometric analysis of collections of scientific literature and patents for technology forecasting. Documents, drawn from the technological field of interest, are visualized as clusters on a two dimensional map, permitting exploration of the relationships among the documents and document clusters and also permitting derivation of summary data about each document cluster. Such information, when provided to subject matter experts performing a technology forecast, can yield insight into trends in the technological field of interest. This paper discusses the document visualization and analysis process: acquisition of documents, mapping documents, clustering, exploration of relationships, and generation of summary and trend information. Detailed discussion of diva exploration functions is presented and followed by an example of visualization and analysis of a set of documents about chemical sensors.