IoT Big Data Stream Mining

dc.contributor.author Morales,GDF en
dc.contributor.author Bifet,A en
dc.contributor.author Khan,L en
dc.contributor.author João Gama en
dc.contributor.author Fan,W en
dc.date.accessioned 2018-01-03T10:36:50Z
dc.date.available 2018-01-03T10:36:50Z
dc.date.issued 2016 en
dc.description.abstract The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in IoT stream mining. This tutorial is a gentle introduction to mining IoT big data streams. The first part introduces data stream learners for classification, regression, clustering, and frequent pattern mining. The second part deals with scalability issues inherent in IoT applications, and discusses how to mine data streams on distributed engines such as Spark, Flink, Storm, and Samza. © 2016 Copyright held by the owner/author(s). en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/5335
dc.identifier.uri http://dx.doi.org/10.1145/2939672.2945385 en
dc.language eng en
dc.relation 5120 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title IoT Big Data Stream Mining en
dc.type conferenceObject en
dc.type Publication en
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