Sequential pattern mining (SPAM) is one of the most interesting research issues of data mining. In this paper, a new research problem of mining data streams for sequential patterns is defined. A data stream is an unbound sequence of data elements arriving at a rapid rate. Based on the characteristics of data streams, the problem complexity of mining data streams for sequential patterns is more ...

In this paper we study the extraction of representative elements in the data stream model in the form of submodular maximization. Different from the previous work on streaming submodular maximization, we are interested only in the recent data, and study the maximization problem over sliding windows. We provide a general reduction from the sliding window model to the standard streaming model, an...

Statistical learning problems in many fields involve sequential data. This paper formalizes the principal learning tasks and describes the methods that have been developed within the machine learning research community for addressing these problems. These methods include sliding window methods, recurrent sliding windows, hidden Markov models, conditional random fields, and graph transformer net...

This paper considers the problem of mining recent frequent itemsets over data streams. As the data grows without limit at a rapid rate, it is hard to track the new changes of frequent itemsets over data streams. We propose an efficient one-pass algorithm in sliding windows over data streams with an error bound guarantee. This algorithm does not need to refer to obsolete transactions when 316 C....

Many networking applications require timely access to recent network measurements,which can be captured using a sliding window model. Maintaining such measurements isa challenging task due to the fast line speed and scarcity of fast memory in routers. Inthis work, we study the efficiency factor that can be gained by approximating the windowsize. That is, we allow the algorit...

The term M out of N sliding window detectors refers to processes that determine whether there have been M successes in a sequence of N binary trials, where the window (of N trials under examination) slides along a possibly M t e sequence stopping when the criterion (of M successes in N successive trials) is met. They are frequently used to model the operators of naval surveillance systems such ...

Many transcription factor binding sites (TFBSs) involved in gene expression regulation are preferentially located relative to the transcription start site. This property is exploited in in silico prediction approaches, one of which involves studying the local overrepresentation of motifs using a sliding window to scan promoters with considerable accuracy. Nevertheless, the consequences of the c...