I was happy to be invited to give a keynote talk at BASNA 2014, the 5th International Workshop on Business Applications of Social Network Analysis, that was co-located with the 2014 IEEE International Conference on Data Mining (ICDM 2014) in Shenzhen, China.
The talk was on Real-Time Big Data Stream Analytics, about new techniques in Big Data mining that are able using a small amount of time and memory resources to adapt to changes. As an example, I discussed a social network application of data stream mining to compute user influence probabilities. And I presented the MOA software framework, and the SAMOA distributed streaming software that runs on top of Storm, Samza and S4. Here are the slides:
Gianmarco de Francisci Morales presented this week our tutorial “Big Data Stream Mining” at IEEE Big Data 2014 in Washington DC.
This tutorial was a gentle introduction to mining big data streams. The first part introduced data stream learners for classification, regression, clustering, and frequent pattern mining. The second part discussed data stream mining on distributed engines such as Storm, S4, and Samza.
- Fundamentals and Stream Mining Algorithms
- Stream mining setting
- Concept drift
- Classification and Regression
- Frequent Pattern mining
- Distributed Big Data Stream Mining
- Distributed Stream Processing Engines
Slides available in : https://sites.google.com/site/iotminingtutorial/