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Fast Algorithms for Computing the Constrained LCS of RunLength Encoded Strings
"... Abstract — In the constrained longest common subsequence (CLCS) problem, we are given two sequences X, Y and the constrained sequence P in runlength encoded (RLE) format, where X  = n, Y  = m and P  = r and the numbers of runs in RLE format are N, M and R, respectively. In this paper, we s ..."
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Abstract — In the constrained longest common subsequence (CLCS) problem, we are given two sequences X, Y and the constrained sequence P in runlength encoded (RLE) format, where X  = n, Y  = m and P  = r and the numbers of runs in RLE format are N, M and R, respectively. In this paper, we show that after the sequences are encoded, the CLCS problem can be solved in O(NMr+ r × min{q1, q2} + q3) time, where q1 and q2 denote the numbers of elements in the bottom and right boundaries of the partially matched blocks on the first layer, and q3 denotes the number of elements of whole boundaries of all fully matched cuboids in the DP lattice. If the compression ratio is good, our work obviously outperforms the previously known DP algorithm and the HuntandSzymanskilike algorithm. 1.
Efficient indexing algorithms for onedimensional discretelyscaled strings
 INFORMATION PROCESSING LETTERS
, 2010
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A Database Server for NextGeneration Scientific Data Management
"... Abstract — The growth of scientific information and the increasing automation of data collection have made databases integral to many scientific disciplines including life sciences, physics, meteorology, earth and atmospheric sciences, and chemistry. These sciences pose new data management challeng ..."
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Abstract — The growth of scientific information and the increasing automation of data collection have made databases integral to many scientific disciplines including life sciences, physics, meteorology, earth and atmospheric sciences, and chemistry. These sciences pose new data management challenges to current database system technologies. The thesis work presented in this paper proposes a database server for nextgeneration scientific data management. The proposed sever realizes two core requirements in scientific databases, mainly, (1) Annotation management, and (2) Complex dependencies involving human actions. In the paper, we discuss the challenges involved in each of these requirements and present the key contributions and main results in each of the two fronts. I.
Optimization of outofcore data preparation methods identifying
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Reordering Columns for Smaller Indexes
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"... Columnoriented indexes—such as projection or bitmap indexes—are compressed by runlength encoding to reduce storage and increase speed. Sorting the tables improves compression. On realistic data sets, permuting the columns in the right order before sorting can reduce the number of runs by a factor ..."
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Columnoriented indexes—such as projection or bitmap indexes—are compressed by runlength encoding to reduce storage and increase speed. Sorting the tables improves compression. On realistic data sets, permuting the columns in the right order before sorting can reduce the number of runs by a factor of two or more. For many cases, we prove that the number of runs in table columns is minimized if we sort columns by increasing cardinality. Yet—maybe surprisingly—we must sometimes maximize the number of runs to minimize the index size. Experimentally, sorting based on Hilbert spacefilling curves is poor at minimizing the number of runs. Key words:
Benchmarking Coding Algorithms for the Rtree Compression⋆
"... Abstract. Multidimensional data structures have been widely applied in many data management fields. Spatial data indexing is their natural application, however there are many applications in different domain fields. When a compression of these data structures is considered, we follow two objective ..."
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Abstract. Multidimensional data structures have been widely applied in many data management fields. Spatial data indexing is their natural application, however there are many applications in different domain fields. When a compression of these data structures is considered, we follow two objectives. The first objective is a smaller index file, the second one is a reduction of the query processing time. In this paper, we apply a compression scheme to fit these objectives. This compression scheme handles compressed nodes in a secondary storage. If a page must be retrieved then this page is decompressed into the tree cache. Since this compression scheme is transparent from the tree operations point of view, we can apply various compression algorithms to pages of a tree. Obviously, there are compression algorithms suitable for various data collections, therefore, this issue is very important. In our paper, we compare the performance of Golomb, Eliasdelta and Eliasgamma coding with the previously introduced Fast Fibonacci algorithm. Keywords:multidimensional data structures, Rtree, compression scheme, Golomb, Eliasdelta, and Eliasgamma coding, Fast Fibonacci algorithm 1
Reordering Columns for Smaller Indexes
"... Columnoriented indexes—such as projection or bitmap indexes—are compressed by runlength encoding to reduce storage and increase speed. Sorting the tables improves compression. On realistic data sets, permuting the columns in the right order before sorting can reduce the number of runs by a factor ..."
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Columnoriented indexes—such as projection or bitmap indexes—are compressed by runlength encoding to reduce storage and increase speed. Sorting the tables improves compression. On realistic data sets, permuting the columns in the right order before sorting can reduce the number of runs by a factor of two or more. Unfortunately, determining the best column order is NPhard. For many cases, we prove that the number of runs in table columns is minimized if we sort columns by increasing cardinality. Experimentally, sorting based on Hilbert spacefilling curves is poor at minimizing the number of runs.
A Database Server for NextGeneration Scientific Data Management
"... A database server for nextgeneration scientific data management ..."
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