This paper presents a prototype-driven framework for classifying evolving data streams. Our framework uses cluster prototypes to summarize the data and to determine whether the cur...
In applications such as fraud and intrusion detection, it is of great interest to measure the evolving trends in the data. We consider the problem of quantifying changes between tw...
In this paper, we propose an efficient algorithm, called MQSchange (changes of Music Query Streams), to detect the changes of maximal melody structures in user-centered music quer...
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...
Data-intensive, interactive applications are an important class of metacomputing (Grid) applications. They are characterized by large, time-varying data flows between data provid...