期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：10
DOI：10.14569/IJACSA.2016.071003
出版社：Science and Information Society (SAI)
摘要：Regardless of the recent growth in the use of “Big Data” and “Business Intelligence” (BI) tools, little research has been undertaken about the implications involved. Analytical tools affect the development and sustainability of a company, as evaluating clientele needs to advance in the competitive market is critical. With the advancement of the population, processing large amounts of data has become too cumbersome for companies. At some stage in a company’s lifecycle, all companies need to create new and better data processing systems that improve their decision-making processes. Companies use BI Results to collect data that is drawn from interpretations grouped from cues in the data set BI information system that helps organisations with activities that give them the advantage in a competitive market. However, many organizations establish such systems, without conducting a preliminary analysis of the needs and wants of a company, or without determining the benefits and targets that they aim to achieve with the implementation. They rarely measure the large costs associated with the implementation blowout of such applications, which results in these impulsive solutions that are unfinished or too complex and unfeasible, in other words unsustainable even if implemented. BI open source tools are specific tools that solve this issue for organizations in need, with data storage and management. This paper compares two of the best positioned BI open source tools in the market: Pentaho and Jaspersoft, processing big data through six different sized databases, especially focussing on their Extract Transform and Load (ETL) and Reporting processes by measuring their performances using Computer Algebra Systems (CAS). The ETL experimental analysis results clearly show that Jaspersoft BI has an increment of CPU time in the process of data over Pentaho BI, which is represented by an average of 42.28% in performance metrics over the six databases. Meanwhile, Pentaho BI had a marked increment of the CPU time in the process of data over Jaspersoft evidenced by the reporting analysis outcomes with an average of 43.12% over six databases that prove the point of this study. This study is a guiding reference for many researchers and those IT professionals who support the conveniences of Big Data processing, and the implementation of BI open source tool based on their needs.
关键词：thesai; IJACSA Volume 7 Issue 10; Big Data; BI; Business Intelligence; CAS; Computer Algebra System; ETL; Data Mining; OLAP
