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    Integrating distributed data streams

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    Distributed stream reasoning

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    Stream Reasoning is the combination of reasoning techniques with data streams. In this paper, we present our approach to enable rule-based reasoning on semantic data streams in a distributed manne

    Continuous Monitoring of Distributed Data Streams over a Time-based Sliding Window

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    The past decade has witnessed many interesting algorithms for maintaining statistics over a data stream. This paper initiates a theoretical study of algorithms for monitoring distributed data streams over a time-based sliding window (which contains a variable number of items and possibly out-of-order items). The concern is how to minimize the communication between individual streams and the root, while allowing the root, at any time, to be able to report the global statistics of all streams within a given error bound. This paper presents communication-efficient algorithms for three classical statistics, namely, basic counting, frequent items and quantiles. The worst-case communication cost over a window is O(kϵlogϵNk)O(\frac{k} {\epsilon} \log \frac{\epsilon N}{k}) bits for basic counting and O(kϵlogNk)O(\frac{k}{\epsilon} \log \frac{N}{k}) words for the remainings, where kk is the number of distributed data streams, NN is the total number of items in the streams that arrive or expire in the window, and ϵ<1\epsilon < 1 is the desired error bound. Matching and nearly matching lower bounds are also obtained.Comment: 12 pages, to appear in the 27th International Symposium on Theoretical Aspects of Computer Science (STACS), 201
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