Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

DBMS and Oracle Datamining

Version 1 : Received: 24 March 2021 / Approved: 25 March 2021 / Online: 25 March 2021 (16:05:52 CET)

How to cite: Bernal, J.N.; Rodriguez, J.P.; Portella, J. DBMS and Oracle Datamining. Preprints 2021, 2021030640 (doi: 10.20944/preprints202103.0640.v1). Bernal, J.N.; Rodriguez, J.P.; Portella, J. DBMS and Oracle Datamining. Preprints 2021, 2021030640 (doi: 10.20944/preprints202103.0640.v1).

Abstract

Databases are by far the most valuable asset of companies. Since the need was seen not only to count but also to have some type of record of elements such as crops, animals, money, properties and that this record could be consulted and modified according to the situation, that is where the first database was born. , and after that, these databases cannot be disorganized, they also need to be managed and administered under established standards that facilitate their understanding and management not only by their creators but by the other people who subsequently administer them. Databases and database management systems have an interesting evolutionary history that deserves to be analyzed and this is the objective of this document, where it is sought to understand. Along with databases and their management systems, data mining or Data mining arises that in order not to extend ourselves so much, it is the job of finding common patterns in various data sources and in what way they can be used to predict situations or results of various circumstances; We also focus on the other topic that we will present, Oracle data mining, which roughly is to merge data mining with Oracle, which makes it a powerful tool for obtaining information and predicting results based on statistics.In this article we will study and analyze the ideas, concepts and basic examples that make up SGBD and Data Mining and, we will try to go deeper into this topic, the use of decision techniques such as advanced statistical algorithms. We also present a fictitious example of the application of these techniques: predicting which products can be sold based on their relationship with others. we will give a brief explanation of association rules, data mining cycle and the types of learning and the evolution that data mining has had.

Keywords

Databases; database administration; database management systems; counting; storage; structure; search; No SQL; SQL; Oracle; relational databases; non-relational databases; magnetic tapes; punched tapes; relational model; Datamining; BigData; Datawarehouse

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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