Mining Ubiquitous Data Streams
date
time schedule
| date |
time |
room |
| Monday, 21. July |
10:00 |
C 252 |
| Tuesday, 22. July |
10:00 |
C 252 |
description
abstract
Many sources produce data continuously. Examples include customer click streams, wireless sensors (e.g bio sensors, environmental sensors etc.), telephone records, web logs, multimedia data, and retail chain transactions. These datasets of continuous and distributed nature are called data streams. Processing distributed data streams is of paramount importance in the research community, as new algorithms are needed to process this streaming data in real time. The goal of this tutorial is to present the state-of-the-art in mining data streams and discuss open research problems, issues, and challenges in this area. We will present techniques for change detection, clustering, classification, frequent patterns, and time series analysis from distributed data streams. Finally the tutorial will be concluded with open issues and future directions. The tutorial also provides a substantial list of data stream mining resources.
