Data Stream Management Systems are useful when large volumes of data need to be processed in real time. Examples include monitoring network traffic, monitoring financial transacti...
Theodore Johnson, S. Muthukrishnan, Vladislav Shka...
In this paper, we present a novel feedback control-based load shedding scheme for data stream processing. Firstly we apply system identification to establish a dynamic model to de...
Outlier detection has recently become an important problem in many industrial and financial applications. This problem is further complicated by the fact that in many cases, outlie...
Dragoljub Pokrajac, Aleksandar Lazarevic, Longin J...
Abstract— A wireless sensor network (WSN) is energy constrained, and the extension of its lifetime is one of the most important issues in its design. Usually, a WSN collects a la...
Mining frequent patterns in a data stream is very challenging for the high complexity of managing patterns with bounded memory against the unbounded data. While many approaches as...
We consider the read/write streams model, an extension of the standard data stream model in which an algorithm can create and manipulate multiple read/write streams in addition to...
A major challenge in using multi-modal, distributed sensor systems for activity recognition is to maintain a temporal synchronization between individually recorded data streams. A ...
Some challenges in frequent pattern mining from data streams are the drift of data distribution and the computational efficiency. In this work an additional challenge is considered...
Fabio Fumarola, Anna Ciampi, Annalisa Appice, Dona...
—The Gap-Hamming-Distance problem arose in the context of proving space lower bounds for a number of key problems in the data stream model. In this problem, Alice and Bob have to...
Mining frequent itemsets in data streams is beneficial to many real-world applications but is also a challenging task since data streams are unbounded and have high arrival rates...