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Due to rapid growth of the Internet technology and new scientific/technological advances, the number of applications that model data as graphs increases, because graphs have high expressive power to model complicated structures. The dominance of graphs in real-world applications asks for new graph data management so that users can access graph data effectively and efficiently. In this paper, we study...
High-volume source streams, coupled with fluctuating rates, necessitate adaptive load shedding in data stream processing. When ignored, a continual query (CQ) server may randomly drop items, when its capacity is inadequate to handle the arriving data, and degrade the quality of the query results. To alleviate this problem, filters can be used at the source nodes. However, regular source filtering...
Tuple dropping, though commonly used for load shedding in most data stream operations, is generally inadequate for multiway windowed stream joins. The join output rate can be unnecessarily reduced because tuple dropping fails to exploit the time correlations that are likely to exist among interrelated streams. In this paper, we introduce GrubJoin-an adaptive multiway windowed stream join that effectively...
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