How to Cope with Change? - Preserving Validity of Predictive Services over Time

Date
2019-01-08
Authors
Baier, Lucas
Kühl, Niklas
Satzger, Gerhard
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Abstract
Companies more and more rely on predictive services which are constantly monitoring and analyzing the available data streams for better service offerings. However, sudden or incremental changes in those streams are a challenge for the validity and proper functionality of the predictive service over time. We develop a framework which allows to characterize and differentiate predictive services with regard to their ongoing validity. Furthermore, this work proposes a research agenda of worthwhile research topics to improve the long-term validity of predictive services. In our work, we especially focus on different scenarios of true label availability for predictive services as well as the integration of expert knowledge. With these insights at hand, we lay an important foundation for future research in the field of valid predictive services.
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Big Data and Analytics: Pathways to Maturity, Decision Analytics, Mobile Services, and Service Science, Concept drift, Conceptual framework, Machine learning, Predictive services
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