Abnormal frequency threshold queries for objects over data stream

Querying and processing data streams are widely used in many applications, such as financial systems and so on. For example, in bank card transaction systems, there exist abnormal trading records caused by terminal multiplexers. In general, such records often contain many abnormal objects that occur frequently with high fluctuating rate. However, existing work on frequent item mining cannot be used to handle this issue directly since only the item's frequency, not the fluctuating rate, is considered. In this paper, we first define the query formally, and then propose several novel solutions to handle this issue. Moreover, we extend our work to the sliding-window model to meet the requirement of stream evolving issue. Analysis in theorem and experimental reports show that our methods have low space-and time-complexities.