Bidirectional compression algorithms work by substituting repeated substrings by references that, unlike in the famous LZ77-scheme, can point to either direction. We present such an algorithm that is particularly suit...
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ISBN:
(纸本)9783959771245
Bidirectional compression algorithms work by substituting repeated substrings by references that, unlike in the famous LZ77-scheme, can point to either direction. We present such an algorithm that is particularly suited for an external memory implementation. We evaluate it experimentally on large data sets of size up to 128 GiB (using only 16 GiB of RAM) and show that it is significantly faster than all known LZ77 compressors, while producing a roughly similar number of factors. We also introduce an external memory decompressor for texts compressed with any uni- or bidirectional compression scheme.
This Matrix, to support d large inatrices stored in external memory. 'I h ibrary is based on some strategies sit management and its basic purpose is to allow that ni application, originally designed fo erna e ory ...
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ISBN:
(纸本)9789898565594
This Matrix, to support d large inatrices stored in external memory. 'I h ibrary is based on some strategies sit management and its basic purpose is to allow that ni application, originally designed fo erna e ory processing, can be easily adapted for external memory, It provides ni inter& for exter memory access that is similar to the traditional method to access a matrix. The TiledMarrix rded and lested in some applications that require intensive matrix processing such as: computing th s d and the computation of view -shed and HOW accumulation on terrains represented by elevation mati-ix. These applications were implemented in tw:o versions: one using iii dun;and another one using the Septienr library that included in GRASS, an open source GIS. They were executed on many dalasets with different sizes and ticcording the tests, all tipplications ran faster using TiledMatrix than Segment. In average, they were 7 times faster with TiledMatrix and, in some cases, more than 18 times faster, No that processing large matrices external memory) can take hours and, his improvement is ver n ant.
algorithms are presented for external matrix multiplication and for all-pairs shortest path computation. In comparison with earlier algorithms, the amount of I/O is reduced by a constant factor. The all-pairs shortest...
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algorithms are presented for external matrix multiplication and for all-pairs shortest path computation. In comparison with earlier algorithms, the amount of I/O is reduced by a constant factor. The all-pairs shortest path algorithm even performs fewer internal operations, making the algorithm practically interesting. (C) 2004 Elsevier B.V. All rights reserved.
We consider the problem of collectively locating a set of points within a set of disjoint polygonal regions when neither for points nor for regions preprocessing is allowed. This problem arises in geometric database s...
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We consider the problem of collectively locating a set of points within a set of disjoint polygonal regions when neither for points nor for regions preprocessing is allowed. This problem arises in geometric database systems. More specifically it is equivalent to computing theinside join of geo-relational algebra, a conceptual model for geo-data management. We describe efficient algorithms for solving this problem based on plane-sweep and divide-and-conquer, requiringO(n(logn) +t) andO(n(log2 n) +t) time, respectively, andO(n) space, wheren is the total number of points and edges, and (is the number of reported (point, region) pairs. Since the algorithms are meant to be practically useful we consider as well as the internal versions-running completely in main memory-versions that run internally but use much less than linear space and versions that run externally, that is, require only a constant amount of internal memory regardless of the amount of data to be processed. Comparing plane-sweep and divide-and-conquer, it turns out that divide-and-conquer can be expected to perform much better in the external case even though it has a higher internal asymptotic worst-case complexity.
An interesting theoretical by-product is a new general technique for handling arbitrarily large sets of objects clustered on a singlex-coordinate within a planar divide-and-conquer algorithm and a proof that the resulting “unbalanced” dividing does not lead to a more than logarithmic height of the tree of recursive calls.
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