Continuous top-k queries, which report a certain number (k) of top preferred objects from data streams, are important for a broad class of real-time applications, ranging from fi...
Di Yang, Avani Shastri, Elke A. Rundensteiner, Mat...
Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that c...
Adaptivity is a challenging open issue in data stream management. In this paper, we tackle the problem of memory adaptivity inside a system executing temporal sliding window queri...
We consider an environment where distributed data sources continuously stream updates to a centralized processor that monitors continuous queries over the distributed data. Signif...
Data processing applications for sensor streams have to deal with multiple continuous data streams with inputs arriving at highly variable and unpredictable rates from various sour...