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Running data-intensive scientific workflow across multiple data centers faces massive data transfer problem which leads to low efficiency in actual workflow application for scientists. By considering data size and data dependency, we propose a k-means algorithm based initial data placement strategy that places the most related initial data sets into the same data center at workflow preparation stage...
Approximate duplicate-detection (or membership query) in data streams answers the question of whether an element from a large universe U (a query element) is present in a small subsequence of a data stream or not. It is an important query that has many Internet applications, such as web crawling, social networks and so on. Existing approximate duplicatedetection methods in the sliding window model...
Massive cloud-based data-intensive applications (e.g., iterative MapReduce-based) could involve graph data processing. How to effectively analyze and process large-scale graph data is an unsolved challenging problem. We present a parallel computation framework, named MyBSP, which is inspired by Google's Pregel system. MyBSP supports and implements the Bulk Synchronous Parallel (BSP) programming model,...
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