Mining evolving data streams for concept drifts has gained importance in applications like customer behavior analysis, network intrusion detection, credit card fraud detection. Se...
We approached this line of inquiry by questioning the conventional wisdom that audit logs are too large to be analyzed and must be reduced and filtered before the data can be anal...
Most data mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by...
Lior Cohen, Gil Avrahami, Mark Last, Abraham Kande...
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract and label a person’s activities and signiď¬...
The ever increasing speed of Internet connections has led to a point where it is actually possible for every end user to seamlessly share data on Internet. Peer-To-Peer (P2P) netw...