Migrating Data Semantic from Relational Database System To NOSQL Systems to Improve Data Quality for Big Data Analytics System

Erraji, Abdelhak; Maizate, Abderrahim; Ouzzif, Mohamed; BATOUTA, Zouhair Ibn

doi:10.1149/10701.19495ecst

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…NoSQLayer is a framework that is integrated by two modules that handle data migration and data mapping (Rocha et al, 2015) Model to migrate relational database instance at a given time (snapshot) and in real time in a parallel way (Namdeo & Suman, 2021). Migration based on transforming the relational database to a NoSQL database and a cleansing process to enhance and improve data quality (Erraji et al, 2022).…”

Section: Relational To Document-basedmentioning

confidence: 99%

An empirical hybrid strategy for NoSQL database distribution

Mendoza-Labastida,

Clavel-Maqueda,

García-Pérez

et al. 2024

Int. Journal of COP and Infor.

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A case study of a pharmaceutical distribution company with a relational database that is migrated to a document-oriented NoSQL database for which a hybrid distribution strategy based on fragmentation and replication is implemented. The legacy relational database schema was thoroughly analyzed and evaluated to determine the collections and documents needed to move all the data and leverage the capabilities of the document-based database. In addition, a hybrid data distribution strategy was designed to effectively balance the performance, scalability, and fault tolerance of the system. Finally, integrated operational tests were performed on the distributed system nodes to verify that they function properly and ensure their availability for the company's operations.

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Section: Relational To Document-basedmentioning

confidence: 99%

An empirical hybrid strategy for NoSQL database distribution

Mendoza-Labastida,

Clavel-Maqueda,

García-Pérez

et al. 2024

Int. Journal of COP and Infor.

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“…Native XML systems show superior query execution speeds in complicated queries, with a 20% advantage. Erraji et al investigated the transfer of data semantics from Relational Database Systems to NoSQL to improve the data quality for BDA (Erraji et al, 2022). The approach incorporates both semantic mapping and transformation methods.…”

Section: Literature Survey and Analysismentioning

confidence: 99%

“…The rank is denoted 𝑅(𝑘), the present rating is expressed 𝑅 𝑖 (𝑘), and the weight deviation is ∆𝑘. All specimens in the linearly distinct training set satisfy a specific scenario, and the system is expressed in Equation (7).…”

Section: 𝜂 = 𝐹 𝑦 (𝑃 𝑛 − 𝑤 𝑥𝑦 (𝑡))mentioning

confidence: 99%

Integrating Novel Machine Learning for Big Data Analytics and IoT Technology in Intelligent Database Management Systems

Clavijo-López,

Navarrete,

Velásquez

et al. 2024

JISIS

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Database Management Systems (DBMS) advancement has been crucial to Information Technology (IT). Traditional DBMS needed help managing large and varied datasets under strict time constraints due to the emergence of Big Data and the widespread use of Internet of Things (IoT) devices. The growing intricacy of data and the need for instantaneous processing presented substantial obstacles. This research suggests a Machine Learning-based Intelligent Database Management Systems (ML-IDMS) technique. This invention combines the skills of Machine Learning with DBMS, improving flexibility and decision-making capacities. The ML-IDMS is specifically developed to tackle current obstacles by providing capabilities such as instantaneous data retrieval, intelligent heat measurement, and effective neural network initialization. The simulation results showcase the effectiveness of ML-IDMS, as shown by impressive metrics such as query execution time (19.27 sec), storage efficiency (83.78%), data accuracy (90%), redundancy reduction (66.42%), network throughput (7.93 Gbps), and end-to-end delay (14.4 ms). The results highlight the efficacy of ML-IDMS in managing various data circumstances. ML-IDMS addresses current obstacles and establishes a standard for future intelligent data management and analytics progress.

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“…Historical project recommendation is to push the university file business data related to historical users to current users. The recommended method is top-N. First, the recommended item set is found by combining user collaborative filtering and user behavior sequence search [30]; Then use the relevant sorting strategy to sort the data in the file business data set according to its sorting weight; Finally select ‫ذ‬business data is pushed to users.…”

Section: Historical Project Recommendationmentioning

confidence: 99%

Design of University Archives Business Data Push System Based on Big Data Mining Technology

Wang,

2023

IJACSA

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Aiming at the problems of low accuracy, recall, coverage and push efficiency of university archives business data, a university archives business data push system based on big data mining technology is designed. Firstly, the overall architecture and topological structure of the university archives business data push system are designed, and then the functional modules of the system are designed. Using big data mining technology to mine user behavior, modeling according to user behavior sequence, and designing a model to predict user behavior sequence based on hidden Markov model theory. Finally, the user behavior sequence is analyzed, and the factors such as user collaboration, similarity of user behavior sequence and data timeliness are comprehensively considered to push university archives business data for users. The experimental results show that the proposed method has high data push accuracy, recall, coverage and push efficiency, and can effectively push the required business data for users.

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Migrating Data Semantic from Relational Database System To NOSQL Systems to Improve Data Quality for Big Data Analytics System

Cited by 3 publications

References 10 publications

An empirical hybrid strategy for NoSQL database distribution

An empirical hybrid strategy for NoSQL database distribution

Integrating Novel Machine Learning for Big Data Analytics and IoT Technology in Intelligent Database Management Systems

Design of University Archives Business Data Push System Based on Big Data Mining Technology

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