CS 85 : Data Stream
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COMPUTING SCIENCE Distributed Event Processing For Activity Recognition
Stream-processing systems inevitably face unpredictable variations in incoming event loads. One way of handling this without a ecting end-to-end performance metrics, will be to dynamically distribute event-processing on multiple computers and thus avail compute power for optimal performance. More precisely, data streams are processed in part or in parallel on multiple computers connected by a h...
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In many applications such as IP network management, data arrives in streams, and queries over those streams need to be processed online using limited storage. Correlated-sum (CS) aggregates are a natural class of queries formed by composing basic aggregates on (x, y) pairs, and are of the form SUM{g(y) : x ≤ f(AGG(x))}, where AGG(x) can be any basic aggregate and f(), g() are user-specified fun...
متن کاملCS 49 : Data Stream Algorithms Lecture Notes , Fall 2011 Amit
Acknowledgements These lecture notes began as rough scribe notes for a Fall 2009 offering of the course " Data Stream Algorithms " at Dartmouth College. The initial scribe notes were prepared mostly by students enrolled in the course in 2009. Subsequently, during a Fall 2011 offering of the course, I edited the notes heavily, bringing them into presentable form, with the aim being to create a r...
متن کاملCS 730R: Topics in Data and Information Management
1. Summary. In this paper the authors presents two major contributions to the recent work of privacy on streaming data initially introduced by Dwork et al. [2]. First the authors study the family of problems related to the decayed sums. These problems are motivated by the fact that only the most recent part of the stream is often considered important in real applications rather than considering...
متن کاملImproving Power Efficiency in Stream Processors Through Dynamic Cluster Reconfiguration
Stream processors support hundreds of functional units in a programmable architecture by clustering functional units and utilizing a bandwidth hierarchy. Clusters are the dominant source of power consumption in stream processors. When the data parallelism falls below the number of clusters, unutilized clusters can be turned off to save power. This paper improves power efficiency in stream proce...
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