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Consider a large-scale wireless sensor network measuring compressible data, where n distributed data values can be well-approximated using only k <g n coefficients of some known transform. We address the problem of recovering an approximation of the n data values by querying any L sensors, so that the reconstruction error is comparable to the optimal fc-term approximation. To solve this problem,...
Many applications require the management of spatial data in very large spatial databases. We propose a grid-based clustering algorithm to process spatial data. This algorithm uses a grid-based method to identify data that are compressible and data that must be maintained in memory. Thus the algorithm compresses data without decreasing the quality of clustering. Through one scan over a database, our...
The problem of finding the size of the largest clique in an undirected graph isNP-hard, even to well-approximate, in the worst case. Simple algorithms,including some we study here, work quite well however, on graphs sampled fromu(n), the uniform distribution on n-vertex graphs. It is felt by many,however, that u(n) does not accurately reflect the nature ofinstances that come up in practice. It is...
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