A Scheme for Data Deduplication Using Advance Machine Learning Architecture in Distributed Systems

Tarun, Sashi; Batth, Ranbir Singh; Kaur, Sukhpreet

doi:10.1109/iccs54944.2021.00019

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Furthermore, machine learning techniques can also be applied to identify and remove duplicates in data, improving the overall uniqueness and consistency of the data [6,19,26].…”

Section: Data Quality Assessment and Enhancementmentioning

confidence: 99%

Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective

Boukhers

Lange

Beyan

2023

Companion Proceedings of the ACM Web Conference 2023

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Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning. The use of data spaces, where data is exchanged among members in a self-regulated environment, is becoming increasingly popular. However, the current manual practices of managing metadata and vocabularies in these spaces are time-consuming, prone to errors, and may not meet the needs of all stakeholders. By leveraging the power of machine learning, we believe that semantic interoperability in data spaces can be significantly improved. This involves automatically generating and updating metadata, which results in a more flexible vocabulary that can accommodate the diverse terminologies used by different sub-communities. Our vision for the future of data spaces addresses the limitations of conventional data exchange and makes data more accessible and valuable for all members of the community. CCS CONCEPTS• Information systems → Data exchange; Data access methods; Semantic web description languages.

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“…Furthermore, machine learning techniques can also be applied to identify and remove duplicates in data, improving the overall uniqueness and consistency of the data [6,19,26].…”

Section: Data Quality Assessment and Enhancementmentioning

confidence: 99%

Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective

Boukhers

Lange

Beyan

2023

Companion Proceedings of the ACM Web Conference 2023

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A Comparative Study of Secure and Efficient Data Duplication Mechanisms for Cloud-Based IoT Applications

Baligodugula,

Amsaad,

Tadepalli

et al. 2023

Lecture Notes in Networks and Systems

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Bulut Bilişimin Gelişmesi ile Büyük Verinin Tekilleştirilmesinde Yapay Zekâ Öneminin İncelenmesi

KESKİN

Işık

2023

International Journal of Engineering and Innovative Research

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With the start of Industry 4.0 in 2011, new concepts and technologies have entered the IT literature. Some of these technologies are virtualization, modularity, big data and deduplication. Big data can be defined as data of a magnitude that exceeds the ability of traditional database systems to collect, store, manage and analyze data. Today, data is diverse, large and rapidly changing. This situation cannot be solved with the traditional database structure. With the emergence of big data, it has become difficult to process data with the algorithms used for data processing. Therefore, new algorithms and technologies have been developed. The most important of these technologies is data deduplication. Deduplication backs up data by dividing it into variable or fixed sizes. In this way, it aims to save storage space by storing only one copy of many repeated data. Today, "deduplication and compression" is an indispensable feature for data storage in both server-storge and hyper-converged architecture systems. Recently, artificial intelligence technologies are advancing very rapidly and their application areas are expanding. Therefore, Artificial Intelligence is a technology that will be very important for the industry and our lives in the future. The purpose of this paper is to give an idea about the relationship between deduplication technology and artificial intelligence by examining various deduplication systems and algorithms. Studies in the literature show that deduplication provides significant savings in storage space, the importance of data security, and the use of artificial intelligence and deduplication as a whole.

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A Scheme for Data Deduplication Using Advance Machine Learning Architecture in Distributed Systems

Cited by 3 publications

References 12 publications

Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective

Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective

A Comparative Study of Secure and Efficient Data Duplication Mechanisms for Cloud-Based IoT Applications

Bulut Bilişimin Gelişmesi ile Büyük Verinin Tekilleştirilmesinde Yapay Zekâ Öneminin İncelenmesi

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