Supporting Sliding Window Queries for Continuous Data Streams

Although traditional databases and data warehouses have been exploited widely to manage persistent data, a large number of applications from sensor network need functional supports for transient data in the continuous data stream. One of the crucial functions is to summarize the data items within a sliding window. A sliding window contains a fixed width span of data elements. The data items are implicitly deleted from the sliding window, when it moves out of the window scope. Several one-dimensional histograms have been proposed to store the succinct time information in a sliding window. Such histograms, however, only handle the data items with attribute values in unary domains. In this paper, we explore the problem of extending the value to a multi-valued domain. A two-dimensional histogram, the hybrid histogram, is proposed to support sliding window queries on a practical multi-valued domain. The basic building block of the hybrid histogram is the exponential histogram. The hybrid histogram is maintained to capture the changes of data distribution. To further compress the exponential histograms, we propose a condensed exponential histogram without losing the error bound. Results of an extensive experimental study are included to evaluate the benefits of the proposed technique.