We presented this week our tutorial “IoT Big Data Stream Mining” at KDD 2016 in San Francisco.

This tutorial was 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.



  • 1. IoT Fundamentals and Stream Mining Algorithms
    •  IoT Stream mining setting
    •  Concept drift
    •  Classification and Regression
    •  Clustering
    •  Frequent Pattern mining
    •  Concept Evolution
    •  Limited Labeled Learning
  • 2. IoT Distributed Big Data Stream Mining
    •  Distributed Stream Processing Engines
    •  Classification
    •  Regression
    •  Open Source Tools
    •  Applications

Slides available in : https://sites.google.com/site/iotminingtutorial/



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