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Compression of High-dimensional Data Spaces Using Non-differential Augmented Vector Quantization

ISSN-2223-4985
Journal from gdlhub / 2012-06-22 16:14:17
Oleh : Aderemi A. Atayero , Adeyemi A. Alatishe , Oludayo O. Olugbara, International Journal of Information and Communication Technology Research
Dibuat : 2012-06-22, dengan 1 file

Keyword : Data Compression, High-Dimensional Data Space, Vector Quantization, Database
Url : http://esjournals.org/journaloftechnology/archive/vol1no8/vol1no8_1.pdf
Sumber pengambilan dokumen : Internet

Most data-intensive applications are confronted with the problems of I/O bottleneck, poor query processing times and

space requirements. Database compression alleviates this bottleneck, reduces disk space usage, improves disk access

speed, speeds up query response time, reduces overall retrieval time and increases the effective I/O bandwidth. However,

random access to individual tuples in a compressed database is very difficult to achieve with most of the available

compression techniques. This paper reports a lossless compression technique called non-differential augmented vector

quantization. The technique is applicable to a collection of tuples and especially effective for tuples with numerous low to

medium cardinality fields. In addition, the technique supports standard database operations, permits very fast random

access and atomic decompression of tuples in large collections. The technique maps a database relation into a static bitmap

index cached access structure. Consequently, we were able to achieve substantial savings in space by storing each database

tuple as a bit value in the computer memory. Important distinguishing characteristics of our technique are that tuples can be

compressed and decompressed individually rather than a full page or entire relation at a time. Furthermore, the information

needed for tuple compression and decompression can reside in the memory. Possible application domains of this technique

include decision support systems, statistical and life databases with low cardinality fields and possibly no text fields.

Deskripsi Alternatif :

Most data-intensive applications are confronted with the problems of I/O bottleneck, poor query processing times and

space requirements. Database compression alleviates this bottleneck, reduces disk space usage, improves disk access

speed, speeds up query response time, reduces overall retrieval time and increases the effective I/O bandwidth. However,

random access to individual tuples in a compressed database is very difficult to achieve with most of the available

compression techniques. This paper reports a lossless compression technique called non-differential augmented vector

quantization. The technique is applicable to a collection of tuples and especially effective for tuples with numerous low to

medium cardinality fields. In addition, the technique supports standard database operations, permits very fast random

access and atomic decompression of tuples in large collections. The technique maps a database relation into a static bitmap

index cached access structure. Consequently, we were able to achieve substantial savings in space by storing each database

tuple as a bit value in the computer memory. Important distinguishing characteristics of our technique are that tuples can be

compressed and decompressed individually rather than a full page or entire relation at a time. Furthermore, the information

needed for tuple compression and decompression can reside in the memory. Possible application domains of this technique

include decision support systems, statistical and life databases with low cardinality fields and possibly no text fields.

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