Abstract
Big Data and the Internet of Things (IoT) have the potential to fundamentally shift the way we interact with our surroundings. 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 stream mining. Dealing with this setting, MOA is a software framework with classification, regression, and frequent pattern methods, and the new APACHE SAMOA is a distributed streaming software for mining IoT data streams.
| Original language | English |
|---|---|
| Pages (from-to) | 15-16 |
| Number of pages | 2 |
| Journal | CEUR Workshop Proceedings |
| Volume | 1743 |
| Publication status | Published - 1 Jan 2016 |
| Externally published | Yes |
| Event | 3rd Annual International Symposium on Information Management and Big Data, SIMBig 2016 - Cusco, Peru Duration: 1 Sept 2016 → 3 Sept 2016 |