We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
Detecting bursts in data streams is an important and challenging task, especially in stock market, traffic control or sensor network streams. Burst detection means the identificat...
The martingale framework for detecting changes in data stream, currently only applicable to labeled data, is extended here to unlabeled data using clustering concept. The one-pass...
Abstract Current, data-driven applications have become more dynamic in nature, with the need to respond to events generated from distributed sources or to react to information extr...
The environment around us is progressively equipped with various sensors, producing data continuously. The applications using these data face many challenges, such as data stream i...
Pervasive information systems give an overview of what digital environments should look like in the future. From a data-centric point of view, traditional databases have to be use...
A lot of work has been done in the area of data stream processing. Most of the previous approaches regard only relational or XML based streams but do not cover semantically richer ...
A large class of applications require real-time processing of continuous stream data resulting in the development of data stream management systems (DSMS). Since many of these app...
Weihan Wang, Mohamed A. Sharaf, Shimin Guo, M. Tam...
Data warehouses are increasingly supplied with data produced by a large number of distributed sensors in many applications: medicine, military, road traffic, weather forecast, util...
The problem of discovering episode rules from static databases has been studied for years due to its wide applications in prediction. In this paper, we make the first attempt to st...