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CONQUEST:: A coarse-grained algorithm for constructing summaries of distributed discrete datasets

ALGORITHMICA 2006年第3期45卷 377-401页

作者： Chi, Jie Koyutuerk, Mehmet Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

In this paper we present a coarse-grained parallel algorithm, CONQUEST, for constructing bounded-error summaries of high-dimensional binary attributed data in a distributed environment. Such summaries enable more expensive analysis techniques to be applied efficiently under constraints on computation, communication, and privacy with little loss in accuracy. While the discrete and high-dimensional nature of the dataset makes the problem difficult in its serial formulation, the loose-coupling of distributed servers hosting the data and the heterogeneity in network bandwidth present additional challenges. CONQUEST is based on a novel linear algebraic tool, PROXIMUS, which is shown to be highly effective on a serial platform. In contrast to traditional fine-grained parallel techniques that distribute the kernel operations, CONQUEST adopts a coarse-grained parallel formulation that relies on the principle of sampling to reduce communication overhead while maintaining high accuracy. Specifically, each individual site computes its local patterns independently. Various sites cooperate in dynamically orchestrated work groups to construct consensus patterns from these local patterns. Individual sites may then decide to continue their participation in the consensus or leave the group. Such parallel formulation implicitly resolves load-balancing and privacy issues while reducing communication volume significantly. Experimental results on an Intel Xeon cluster demonstrate that this strategy is capable of excellent performance in terms of compression time, ratio, and accuracy with respect to post-processing tasks.

关键词： coarse-grained data mining algorithms compressing binary attributed vectors non-orthogonal matrix decompositions correlations in high dimensions

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CONQUEST:: A distributed tool for constructing summaries of high-dimensional discrete attributed datasets

CONQUEST:: A distributed tool for constructing summaries of ...

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4th SIAM International Conference on Data Mining

作者： Chi, J Koyutürk, M Grama, A Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

ISBN: (纸本)0898715687

The problem of constructing bounded-error summaries of binary attributed data of very high dimensions is an important and difficult one. These summaries enable more expensive analysis techniques to be applied efficiently with little loss in accuracy. Recent work in this area has resulted in the use of discrete linear algebraic transforms to construct such summaries efficiently. This paper addresses the problem of constructing summaries of distributed datasets. Specifically, the problem can be stated as follows: given a set of n discrete attributed vectors distributed across p sites, construct a summary of k << n vectors such that each of the input vectors is within given bounded distance from some output vector. In addition to being algorithmically efficient (i.e., must do no more work than corresponding serial algorithm), the distributed formulation must have low parallelization overheads. We present here, CONQUEST, a tool that achieves excellent performance and scalability for summarizing distributed datasets. In contrast to traditional parallel techniques that distribute the kernel operations, CONQUEST uses a less aggressive parallel formulation that relies on the principle of sampling to reduce communication overhead while maintaining high accuracy. Specifically, each individual site computes its local patterns independently. Various sites cooperate within dynamically orchestrated workgroups to construct consensus patters from these local patterns. Individual sites then decide to participate in the consensus or leave the group. Experimental results on a set of Intel Xeon servers demonstrate that this strategy is capable of excellent performance in terms of compression time, ratio, and accuracy with respect to postprocessing tasks. The communication overhead associated with CONQUEST is also shown to be minimal, making it ideally suited to wide-area deployment.

关键词： distributed data mining compressing binary attributed vectors non-orthogonal matrix decompositions correlations in high dimensions

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