Open AccessA Recommender for Choosing Data Systems based on Application Profiling and Benchmarking
Author(s) -
Elton Soares,
Renan Souza,
Raphael Melo Thiago,
Marcelo Machado,
Leonardo Guerreiro Azevedo
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbbd.2021.17883
Subject(s) - benchmarking , recommender system , computer science , profiling (computer programming) , benchmark (surveying) , data modeling , data science , architecture , data mining , machine learning , database , art , geodesy , marketing , business , visual arts , geography , operating system
In our data-driven society, there are hundreds of possible data systems in the market with a wide range of configuration parameters, making it very hard for enterprises and users to choose the most suitable data systems. There is a lack of representative empirical evidence to help users make an informed decision. Using benchmark results is a widely adopted practice, but like there are several data systems, there are various benchmarks. This ongoing work presents an architecture and methods of a system that supports the recommendation of the most suitable data system for an application. We also illustrates how the recommendation would work in a fictitious scenario.