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...
Processing and extracting meaningful knowledge from count data is an important problem in data mining. The volume of data is increasing dramatically as the data is generated by da...
—Sampling is used as a universal method to reduce the running time of computations – the computation is performed on a much smaller sample and then the result is scaled to comp...
Data stream clustering has emerged as a challenging and interesting problem over the past few years. Due to the evolving nature, and one-pass restriction imposed by the data strea...
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...