|
Riding the Big IoT Data Wave: Complex Analytics for IoT Data Series| old_uid | 13265 |
|---|
| title | Riding the Big IoT Data Wave: Complex Analytics for IoT Data Series |
|---|
| start_date | 2017/02/22 |
|---|
| schedule | 14h |
|---|
| online | no |
|---|
| location_info | couloir 26-00, salle 101 |
|---|
| details | organisé en coopération avec le chapitre France de l'IEEE Computational Intelligence Society. |
|---|
| summary | The realization of the Internet of Things (IoT) is creating an unprecedented tidal data wave, consisting of the collection of continuous measurements from an enormous number of sensors. The goal is to better understand, model, and analyze real-world phenomena, interactions, and behaviors. Consequently, there is an increasingly pressing need for developing techniques able to index and mine very large collections of sequences, or data series. This need is also present across several applications in diverse domains, ranging (among others) from engineering, telecommunications, and finance, to astronomy, neuroscience, and the web. It is not unusual for the applications mentioned above to involve numbers of data series in the order of hundreds of millions to billions, which are often times not analyzed in their full detail due to their sheer size.
In this talk, we describe recent efforts in designing techniques for indexing and mining truly massive collections of data series that will enable scientists to easily analyze their data. We show that the main bottleneck in mining such massive datasets is the time taken to build the index, and we thus introduce solutions to this problem. Furthermore, we discuss novel techniques that adaptively create data series indexes, allowing users to correctly answer queries before the indexing task is finished. We also show how our methods allow mining on datasets that would otherwise be completely untenable, including the first published experiments using one billion data series.
Finally, we present our vision for the future in big sequence management research. |
|---|
| responsibles | Piwowarski |
|---|
| |
|