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Recently, due to the imprecise nature of the data generated from a variety of streaming applications, such as sensor networks, query processing on uncertain data streams has become an important problem. However, all the existing works on uncertain data streams study unbounded streams. In this paper, we take the first step towards the important and(More)
It is challenge to maintain frequent items over a data stream, with a small bounded memory, in a dynamic environment where both insertion/deletion of items are allowed. In this paper, we propose a new novel algorithm, called <b>hCount</b>, which can handle both insertion and deletion of items with a much less memory space than the best reported algorithm.(More)
Mining data streams poses great challenges due to the limited memory availability and real-time query response requirement. Clustering an evolving data stream is especially interesting because it captures not only the changing distribution of clusters but also the evolving behaviors of individual clusters. In this paper, we present a novel method for(More)
While the lattice Boltzmann method (LBM) has attracted much attention in the area of CFD in recent years, it has also been recognized that it is both computationally demanding and memory intensive. Extensive studies on improving the performance of LBM have been carried out. In this work, various efficient implementation algorithms of LBM are investigated in(More)
Evaluating similarity between sets is a fundamental task in computer science. However, there are many applications in which elements in a set may be uncertain due to various reasons. Existing work on modeling such probabilistic sets and computing their similarities suffers from huge model sizes or significant similarity evaluation cost, and hence is only(More)
Clustering uncertain data streams has recently become one of the most challenging tasks in data management because of the strict space and time requirements of processing tuples arriving at high speed and the difficulty that arises from handling uncertain data. The prior work on clustering data streams focuses on devising complicated synopsis data(More)
Finding matching customers for a given product based on individual user’s preference is critical for many applications, especially in e-commerce. Recently, the reverse top-k query is proposed to return a number of customers who regard a given product as one of the k most favorite products based on a linear model. Although a few “hot” products can be(More)