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Semantic weak signal tracing

 
: Thorleuchter, Dirk; Scheja, Tobias; Poel, Dirk van den

:
Postprint urn:nbn:de:0011-n-3035121 (533 KByte PDF)
MD5 Fingerprint: e1421626c92312b6ba692280fe013300
Erstellt am: 19.8.2014


Expert Systems with Applications 41 (2014), Nr.11, S.5009-5016
ISSN: 0957-4174
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer INT ()
time series; trend identification; Latent Semantic Indexing; web mining

Abstract
The weak signal concept according to Ansoff has the aim to advance strategic early warning. It enables to predict the appearance of events in advance that are relevant for an organization. An example is to predict the appearance of a new and relevant technology for a research organization. Existing approaches detect weak signals based on an environmental scanning procedure that considers textual information from the internet. This is because about 80% of all data in the internet are textual information. The texts are processed by a specific clustering approach where clusters that represent weak signals are identified. In contrast to these related approaches, we propose a new methodology that investigates a sequence of clusters measured at successive points in time. This enables to trace the development of weak signals over time and thus, it enables to identify relevant weak signal developments for organizations decision making in strategic early warning environment.

: http://publica.fraunhofer.de/dokumente/N-303512.html