Learn 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)
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)
Query processing on uncertain data streams has attracted a lot of attentions lately, due to the imprecise nature in the data generated from a variety of streaming applications, such as readings from a sensor network. However, all of the existing works on uncertain data streams study unbounded streams. This paper takes the first step towards the important(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)
Data uncertainty widely exists in many web applications, financial applications and sensor networks. Ranking queries that return a number of tuples with maximal ranking scores are important in the field of database management. Most existing work focuses on proposing static solutions for various ranking semantics over uncertain data. Our focus is to handle(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)