CONFIGURATION MODEL FOR FOCUSED CRAWLERS IN TECHNOLOGY INTELLIGENCE

摘要

Due to a steady increase of competitive constraints caused by ongoing globalization and dynamically growing markets, technology intelligence has become an important element of strategic business intelligence. The objective of technology intelligence is to focus on the systematic identification of future chances but also threats to companies caused by new technologies and further technology developments. To operate technology intelligence efficiently, access to up-to-date, relevant, and sufficiently complete information is essential. Indeed, availability of information is higher than ever by reason of digitalization. However, it also causes the problem of information overload. The available mass of data has to be searched, assorted and assessed to identify the actual needed information. In addition, the entire information processing has to be continued permanently or to be repeated for each new object of investigation, otherwise the validity of the results is not given any more. Accordingly, it appears reasonable to automate this process by widely using smart software solutions. One of the promising approaches is "focused crawling" which not just runs through given data sources in the web, but also rates each data record to make an autonomous decision, which information is relevant for the further process, and which data records should reasonably be analyzed next. To implement such crawlers, different approaches exist in the field of information retrieval: For example, different rating and discovery algorithms. This paper presents the status quo of ongoing research to develop a configuration model for focused crawlers to fulfill the varying requirements of technology intelligence tasks. At first, the assessment criteria for information in a technology intelligence process and the configuration possibilities of focused crawlers are described. As a result, a first approach of a matching between the requirements of technology intelligence tasks and the consequences of different focused crawler configurations is presented. Closing, the paper explains how this approach will be improved and validated in case studies prospectively.