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An enhancement of mespotine run-length encoding applied in columnar databases in a big data environment / Philip P. Cando, and Brent Julian S.Gayola. 6

By: Cando, Philip P. and Gayola, Brent Julian S. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; 201846Edition: Description: 28 cm. 187 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

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Action note: In: Summary: ABSTRACT: With the fast-growing economy of businesses, data storing schemes have been implemented to reduce the cost of maintaining information. One of these schemes is data compression, which can be either lossy or lossless. Mespotine Run-Length Encoding Algorithm, sometimes referred to this study as MRLE, is a variant of Run-Length Encoding formulated by Meo Mespotine in 2015. RLE algorithm work by counting the repetitive consecutive characters or data and storing it in a run-value, run-length format. While the MRLE is proven to be the best RLE algorithm implemented on most of the input data, it is observed that the algorithm still has a room for improvement : (1) Low compression rate against hybrid input data, (2) Slow compression speed of the algorithm and (3) More runs of the same run value in the encoded data means slower fetching of aggregate queries. To address the indicated problems, the researchers fulfilled three objectives , (1) Improving compression rate of the algorithm against hybrid data by at least 15%, (2) Implementing the algorithm in an improved time-complexity and (3) Manipulating the input to lessen the occurrences of runs with the same run value. The first objective was achieved by compressing between runs instead of considering the occurrences of the character in the whole input data. The time-complexity of the algorithm was improved by implementing it in a linear fashion on the first pass over the input data. Lastly, the manipulation of the input data was done by sorting the records in the database before actual compression. The results indicate that the Enhanced MRLE algorithm does not only address the three problems stated but also showed high compression rates in data stored in columnar-database. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Filipiniana Section	Filipiniana-Thesis	T QA76.9.C36.2018 (Browse shelf)	Available		FT6460

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Thesis: (BSCS major in Computer Science)- Pamantasan ng Lungsod ng Maynila, 2018. 56

ABSTRACT: With the fast-growing economy of businesses, data storing schemes have been implemented to reduce the cost of maintaining information. One of these schemes is data compression, which can be either lossy or lossless. Mespotine Run-Length Encoding Algorithm, sometimes referred to this study as MRLE, is a variant of Run-Length Encoding formulated by Meo Mespotine in 2015. RLE algorithm work by counting the repetitive consecutive characters or data and storing it in a run-value, run-length format. While the MRLE is proven to be the best RLE algorithm implemented on most of the input data, it is observed that the algorithm still has a room for improvement : (1) Low compression rate against hybrid input data, (2) Slow compression speed of the algorithm and (3) More runs of the same run value in the encoded data means slower fetching of aggregate queries. To address the indicated problems, the researchers fulfilled three objectives , (1) Improving compression rate of the algorithm against hybrid data by at least 15%, (2) Implementing the algorithm in an improved time-complexity and (3) Manipulating the input to lessen the occurrences of runs with the same run value. The first objective was achieved by compressing between runs instead of considering the occurrences of the character in the whole input data. The time-complexity of the algorithm was improved by implementing it in a linear fashion on the first pass over the input data. Lastly, the manipulation of the input data was done by sorting the records in the database before actual compression. The results indicate that the Enhanced MRLE algorithm does not only address the three problems stated but also showed high compression rates in data stored in columnar-database.

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