dc.creatorMonjaras, Alvaro
dc.creatorBcndezu, Enrique
dc.creatorRaymundo, Carlos
dc.date.accessioned2021-06-07T17:13:33Z
dc.date.accessioned2024-05-07T02:09:46Z
dc.date.available2021-06-07T17:13:33Z
dc.date.available2024-05-07T02:09:46Z
dc.date.created2021-06-07T17:13:33Z
dc.date.issued2019-05-09
dc.identifier10.1109/ICITM.2019.8710696
dc.identifierhttp://hdl.handle.net/10757/656346
dc.identifierProceedings of 2019 8th International Conference on Industrial Technology and Management, ICITM 2019
dc.identifier2-s2.0-85066632248
dc.identifierSCOPUS_ID:85066632248
dc.identifier0000 0001 2196 144X
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9325341
dc.description.abstractThere are currently several types of databases that have different ways of manipulating data that affects the performance of transactions when dealing with the information stored. And it is very important for companies to manage information fast, so they do not lose any operation because of a bad performance of a database, in the same way, they need to operate fast while keeping the integrity of the information. Likewise, every database category's purpose is to serve a specific or specifics use cases to perform fast to manage the information when needed, so in this paper, we study and analyze the SQL, NoSQL and In Memory databases to understand their fit uses cases and make performance tests to build a decision tree that can help to take the decision to choose what database category to use to maintain a good performance. The precision of the tests of relational databases was 96.26% in NoSQL databases was 91.83% and finally in IMDBS was 93.87%.
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relationhttps://ieeexplore.ieee.org/document/8710696
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.sourceUniversidad Peruana de Ciencias Aplicadas (UPC)
dc.sourceRepositorio Académico - UPC
dc.sourceProceedings of 2019 8th International Conference on Industrial Technology and Management, ICITM 2019
dc.source353
dc.source357
dc.subjectcomponent database
dc.subjectdecision tree
dc.subjectin memory database
dc.subjectnosql
dc.subjectperformance
dc.subjectSQL
dc.titleDecision Tree Model to Support the Successful Selection of a Database Engine for Novice Database Administrators
dc.typeinfo:eu-repo/semantics/article


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