This article describes the design, comparison and evaluation of predictive models from the area of monitoring data on the basis of CRISP-DM and RadpidMiner technology, for the purpose of improving IT processes in the company. These models have been created on a sample of real data from the monitoring of IT systems in one of the largest companies in Slovakia. Article defines the detailed description and evaluation of created models, which represents only one phase of this research. This means that the other phases are only mentioned - understanding and editing of data, visualization and statistical analysis, along with modeling process itself. Predictive models generated by linear regression and ARIMA models are described in detail in this article. These models achieved during research are a huge benefit for companies, since they can predict the future value of individual transactions, and thus take the necessary measures to make the right decisions in order to improve quality of services.
[1]
Alexander Linden,et al.
Magic Quadrant for Advanced Analytics Platforms
,
2014
.
[2]
Ekonomická Fakulta.
VOĽNE DOSTUPNÉ NÁSTROJE PRE DATA MINING BAKALÁRSKA PRÁCA ac237019-eca3-4791-8da1-e9ad842ecb99
,
2011
.
[3]
Peter Michalik,et al.
Concept definition for Big Data architecture in the education system
,
2014,
2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI).
[4]
Manuel Filipe Santos,et al.
KDD, SEMMA and CRISP-DM: a parallel overview
,
2008,
IADIS European Conf. Data Mining.
[5]
Jaroslav Čipkala.
Hĺbková analýza údajov v banke
,
2011
.
[6]
Pavel Važan,et al.
Prediction of Selected Production Goals by Classification Methods
,
2014
.