Identifying frequent items in sliding windows over on-line packet streams
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Time-decaying aggregates in out-of-order streams
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Missing data imputation: a fuzzy K-means clustering algorithm over sliding window
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
A new paradigm of ranking & searching in learning object repository
Proceedings of the second ACM international workshop on Multimedia technologies for distance leaning
A spatio-temporal approach to the discovery of online social trends
COCOA'11 Proceedings of the 5th international conference on Combinatorial optimization and applications
Maintaining moving sums over data streams
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Querying sliding windows over online data streams
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Sketch-based querying of distributed sliding-window data streams
Proceedings of the VLDB Endowment
Modeling user-generated contents: an intelligent state machine for user-centric search support
Personal and Ubiquitous Computing
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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.