Compression of High-dimensional Data Spaces Using Non-differential Augmented Vector Quantization
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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.
Keywords
TK Electrical engineering. Electronics Nuclear engineering