NDLI: Predictive analytics on evolving data streams anticipating and adapting to changes in known and unknown contexts

Content Provider	IEEE Xplore Digital Library
Author	Pechenizkiy, M.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Eindhoven Univ. of Technol., Eindhoven, Netherlands (Pechenizkiy, M.)
Abstract	Ever increasing volumes of sensor readings, transactional records, web data and event logs call for next generation of big data mining technology providing effective and efficient tools for making use of the streaming data. Predictive analytics on data streams is actively studied in research communities and used in the real-world applications that in turn put in the spotlight several important challenges to be addressed. In this talk I will focus on the challenges of dealing with evolving data streams. In dynamically changing and nonstationary environments, the data distribution can change over time. When such changes can be anticipated and modeled explicitly, we can design context-aware predictive models. When such changes in underlying data distribution over time are unexpected, we deal with the so-called problem of concept drift. I will highlight some of the recent developments in the proactive handling of concept drift and link them to research in context-aware predictive modeling. I will also share some of the insights we gained through the performed case studies in the domains of web analytics, stress analytics, and food sales analytics.
Starting Page	658
Ending Page	659
File Size	610086
Page Count	2
File Format	PDF
ISBN	9781467378123
e-ISBN	9781467378130
DOI	10.1109/HPCSim.2015.7237112
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-20
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Adaptation models Analytical models Concept drift Predictive models Eloving data streams Data models Context-awareness Data mining Predictive analytics Context modeling Adaptation
Content Type	Text
Resource Type	Article

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