IoT big data stream mining

Gianmarco Morales, Albert Bifet, Latifur Khan, Joao Gama, Wei Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationKDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages2119-2120
Number of pages2
Volume13-17-August-2016
ISBN (Electronic)9781450342322
DOIs
Publication statusPublished - 13 Aug 2016
Event22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States
Duration: 13 Aug 201617 Aug 2016

Other

Other22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
CountryUnited States
CitySan Francisco
Period13/8/1617/8/16

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Keywords

  • Big data
  • Data science
  • Data streams
  • IoT

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Morales, G., Bifet, A., Khan, L., Gama, J., & Fan, W. (2016). IoT big data stream mining. In KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. 13-17-August-2016, pp. 2119-2120). Association for Computing Machinery. https://doi.org/10.1145/2939672.2945385