Real Time Database Compression Optimization Using Iterative Length Compression Algorithm

Murugesan, Muthukumar; Ravichandran, T.

doi:10.5121/csit.2013.3610

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…If the codebook is properly designed, the average difference between a tuple and its representative tuple will be small enough that it takes fewer bits to encode than the original tuple. Run‐length encoding is very effective in databases where there are long sequences of repeated zeros [25]. Simply, run‐length encoding replaces sequences of identical values by a count field, followed by an identifier for the repeated value.…”

Section: Proposed Methodsmentioning

confidence: 99%

Improving semantic compression specification in large relational database

Darwish

2016

IET softw.

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The large-scale relational databases normally have a large size and a high degree of sparsity. This has made database compression very important to improve the performance and save storage space. Using standard compression techniques (syntactic) such as Gzip or Zip does not take advantage of the relational properties, as these techniques do not look at the nature of the data. Since semantic compression accounts for and exploits both the meanings and dynamic ranges of error for individual attributes (lossy compression); and existing data dependencies and correlations between attributes in the table (lossless compression), it is very effective for table-data compression. Inspired by semantic compression, this study proposes a novel independent lossless compression system through utilising data-mining model to find the frequent pattern with maximum gain (representative row) in order to draw attribute semantics, besides a modified version of an augmented vector quantisation coder to increase total throughput of the database compression. This algorithm enables more granular and suitable for every kind of massive data tables after synthetically considering compression ratio, space, and speed. The experimentation with several very large real-life datasets indicates the superiority of the system with respect to previously known lossless semantic techniques.

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Section: Proposed Methodsmentioning

confidence: 99%

Improving semantic compression specification in large relational database

Darwish

2016

IET softw.

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“…If the codebook is properly designed, the average difference between a tuple and its representative tuple will be small enough that it takes fewer bits to encode than the original tuple. Run-length encoding is very effective in databases where there are long sequences of repeated zeros or missing values [20]. Simply, run-length encoding replaces sequences of identical values by a count field, followed by an identifier for the repeated value.…”

Section: Block Encodingmentioning

confidence: 99%

Semantic Database Compression System Based on Augmented Vector Quantization

Darwish¹,

El-Kaffas²,

Abdulateef³

2015

JSW

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Abstract:In the last years, that amount of data stored in databases has increased extremely with the widespread use of databases and the rapid adoption of information systems and data warehouse technologies. It is a challenge to store and recover this increased data in an efficient method. This challenge will potentially appeal in database systems for two causes: storage cost reduction and performance improvement. Lossy compression in databases can return better compression ratios than lossless compression in general, but is rarely used due to the concern of losing data. For relational databases, using standard compression techniques like Gzip or Zip don't take advantage of the relational properties; since these techniques don't look at the nature of the data. In this paper, we propose a database compression system that takes advantage of attributes semantics and data-mining models to find frequent attribute pattern with maximum gain to perform compression of massive table's data. Furthermore, the suggested system relies on augmented vector quantization (AVQ) algorithm to achieve lossless compression version without losing any information. Extensive experiments were conducted and the results indicate the superiority of the system with respect to previously known techniques.

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Oltp Performance Improvement Using File-Systems Layer Compression

Suharjito

Kurnadi

2017

Jurnal Teknologi

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Database for Online Transaction Processing (OLTP) application is used by almost every corporations that has adopted computerisation to support their operational day to day business. Compression in the storage or file-systems layer has not been widely adopted for OLTP database because of the concern that it might decrease database performance. OLTP compression in the database layer is available commercially but it has a significant licence cost that reduces the cost saving of compression. In this research, transparent file-system compression with LZ4, LZJB and ZLE algorithm have been tested to improve performance of OLTP application. Using Swing-bench as the benchmark tool and Oracle database 12c, The result indicated that on OLTP workload, LZJB was the most optimal compression algorithm with performance improvement up to 49% and consistent reduction of maximum response time and CPU utilisation overhead, while LZ4 was the compression with the highest compression ratio and ZLE was the compression with the lowest CPU utilisation overhead. In terms of compression ratio, LZ4 can deliver the highest compression ratio which is 5.32, followed by LZJB, 4.92; and ZLE, 1.76. Furthermore, it is found that there is indeed a risk of reduced performance and/or an increase of maximum response time.

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Real Time Database Compression Optimization Using Iterative Length Compression Algorithm

Abstract: ABSTRACT

Cited by 4 publications

References 9 publications

Improving semantic compression specification in large relational database

Improving semantic compression specification in large relational database

Semantic Database Compression System Based on Augmented Vector Quantization

Oltp Performance Improvement Using File-Systems Layer Compression

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