Authors:
Andreas Becks
and
Jessica Huster
Affiliation:
Fraunhofer-Institute for Applied Information Technology FIT, Germany
Keyword(s):
Text mining, data mining, association analysis, concept-drifts, ontology-based knowledge-flow system.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Data Warehouses and OLAP
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Ontologies and the Semantic Web
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Strategic Decision Support Systems
;
Verification and Validation of Knowledge-Based Systems
;
Web Information Systems and Technologies
Abstract:
Trend-related industries like the European home-textile industry have to quickly adapt to evolving product trends and consumer behaviour in order to avoid economic risks generated by misproduction. Trend indicators are manifold, reaching from changes in ordered products and consumer behaviour to ideas and concepts published in magazines or presented at trade fairs. In this paper we report on the overall design of the Trend Analyser, a decision support system that helps designers and product developers of textile producers to perform market basket analyses as well as mining trend-relevant fashion magazines and other publications by trend-setters. Our tool design brings together explorative text and data mining methods in an ontology-based knowledge flow system, helping
decision-makers to perform a better planning of their production.