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

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dc.contributor.authorBaier, Lucas
dc.contributor.authorKühl, Niklas
dc.contributor.authorSatzger, Gerhard
dc.date.accessioned2019-01-02T23:48:52Z
dc.date.available2019-01-02T23:48:52Z
dc.date.issued2019-01-08
dc.description.abstractCompanies 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.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.133
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59548
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBig Data and Analytics: Pathways to Maturity
dc.subjectDecision Analytics, Mobile Services, and Service Science
dc.subjectConcept drift, Conceptual framework, Machine learning, Predictive services
dc.titleHow to Cope with Change? - Preserving Validity of Predictive Services over Time
dc.typeConference Paper
dc.type.dcmiText

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