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DOI: 10.14569/IJACSA.2020.0110472
PDF

Enhanced Data Lake Clustering Design based on K-means Algorithm

Author 1: Jabrane Kachaoui
Author 2: Abdessamad Belangour

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 4, 2020.

  • Abstract and Keywords
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Abstract: In recent years, Big Data requirements have evolved. Organizations are trying more than ever to accent their efforts on industrial development of all data at their disposal and move further away from underpinning technologies. After investing around Data Lake concept, organizations must now overhaul their data architecture to face IoT (Internet of Things) and AI (Artificial Intelligence) expansion. Efficient and effective data mapping treatments could serve in understanding the importance of data being transformed and used for decision-making process endorsement. As current relational databases are not able to manage large amounts of data, organizations headed towards NoSQL (Not only Structured Query Language) databases. One such known NoSQL database is MongoDB, which has a high scalability. This article mainly put forward a new data model able to extract, classify, and then map data for the purpose of generating new more structured data that meet organizational needs. This can be carried out by calculating various metadata attributes weights, which are considered as important information. It also processed on data clustering stored into MongoDB. This categorization based on data mining clustering algorithm named K-Means.

Keywords: Big data; Data Lake; NoSQL; MongoDB; K-means; metadata

Jabrane Kachaoui and Abdessamad Belangour, “Enhanced Data Lake Clustering Design based on K-means Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 11(4), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110472

@article{Kachaoui2020,
title = {Enhanced Data Lake Clustering Design based on K-means Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110472},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110472},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {4},
author = {Jabrane Kachaoui and Abdessamad Belangour}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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