Preface

Thwel, Tin Thein; Sinha, G.R.

doi:10.1016/b978-0-12-823395-5.00021-5

Cited by 2 publications

(1 citation statement)

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“…In this paper, we created a deduplication storage system and assessed how well the suggested algorithms performed. In order to compare the behavior of the proposed system, the test outcomes are evaluated using the following performance metrics 1-Equation 5 is used to express the Deduplication Ratio (DR), which assesses the efficiency of the deduplication procedure [27] [28]. DR= (Total Input Data Size Before Deduplication) /(Total Input Data Size After Deduplication) (5) 2-Calculate the Average Chunk Size (AVG) by dividing the total data size by the number of segments overall [29] [30].…”

Section: Resultsmentioning

confidence: 99%

Deduplication using Modified Dynamic File Chunking for Big Data Mining

Taha Ahmed

2024

IJCDS

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The unpredictability of data growth necessitates data management to make optimum use of storage capacity. An innovative strategy for data deduplication is suggested in this study. The file is split into blocks of a predefined size by the predefined-size DeDuplication algorithm. The primary problem with this strategy is that the preceding sections will be relocated from their original placements if additional sections are inserted into the forefront or center of a file. As a result, the generated chunks will have a new hash value, resulting in a lower DeDuplication ratio. To overcome this drawback, this study suggests multiple characters as content-defined chunking breakpoints, which mostly depend on file internal representation and have variable chunk sizes. The experimental result shows significant improvement in the redundancy removal ratio of the Linux dataset. So, a comparison is made between the proposed fixed and dynamic deduplication stating that dynamic chunking has less average chunk size and can gain a much higher deduplication ratio.

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Section: Resultsmentioning

confidence: 99%

Deduplication using Modified Dynamic File Chunking for Big Data Mining

Taha Ahmed

2024

IJCDS

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Using Network Analysis Theory to Extract Critical Data from a Construction Project

Kao

Chen

Ho³

2023

Buildings

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Construction projects are inherently complex and entail extensive information processing. Thus, they require effective information management, which, in turn, requires the preservation of critical construction data (CD). Although BIM and blockchain methodology use the “change type of query and storage for data management” to improve the service quality of data, data redundancy still causes inefficient retrieval. Moreover, project managers face various source limitations, which prevent the contents of the database from being managed efficiently. This study uses network analysis theory to design an information network (IN). Critical CD were extracted, and an IN structure was built using data from construction practices (network nodes) and data relation (network links). Three metrics were used for performance evaluation of the data references and data delivery. The refurbishment of heritage buildings in Kinmen, Taiwan, was used as a case study to extract critical CD such as the “inspection record checklist” and “architect design plan drawing”. Lastly, CD can be applied as the elementary item of a backstage database for BIM and blockchain applications of DM. The combined system of critical DM can play an important role in obtaining comprehensive information for a construction project. Customized metrics of IN analysis can be developed as an integrated composite to decide the priority of CD.

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Preface

Cited by 2 publications

References 0 publications

Deduplication using Modified Dynamic File Chunking for Big Data Mining

Deduplication using Modified Dynamic File Chunking for Big Data Mining

Using Network Analysis Theory to Extract Critical Data from a Construction Project

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