IoT Big Data Stream Mining

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Morales, GDF (Author) Other; Bifet, A (Author) Other; Khan, L (Author) Other; João Gama (Author) FEP; Fan, W (Author) Other

Conference proceedings International

Title: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016

Pages: 2119-2120

22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016

13 August 2016 through 17 August 2016

Indexing

Scopus - 72 Citations

Other information

Authenticus ID: P-00K-Q6E

DOI: 10.1145/2939672.2945385

Abstract (EN): 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).

Language: English

Type (Professor's evaluation): Scientific