International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Studying the right field has great importance in human life and perspective. Rather than affecting the greatest employer’s ability, some studies see higher education as one of the leading factors that directly affects the style of life that we do. Therefore, today’s demands have increased significantly for skilled people, and prepared in complex areas, and a consolidation between market demands and university curricula is needed. This paper examines Data mining techniques which are used in order to create an automated model which makes comparisons between market demands and university curricula. We also present how proposed model is able to give recommendations, based on the comparison between market demands and university curricula.

Big data is large volume, heterogeneous, distributed data. Big data applications where data collection has grown continuously, it is expensive to manage, capture or extract and process data using existing software tools. With increasing size of data in data warehouse it is expensive to perform data analysis. In recent years, numbers of computation and data intensive scientific data analyses are established. To perform the large scale data mining analyses so as to meet the scalability and performance requirements of big data, several efficient parallel and concurrent algorithms got applied. For data processing, Big data processing framework relay on cluster computers and parallel execution framework provided by MapReduce. MapReduce is a parallel programming model and an associated implementation for processing and generating large data sets. In this paper, we are going to work around MapReduce, use a MapReduce solution for handling large data efficiently, its advantages, disadvantages and how it can be used in integration with other technology.

Diagnostic system is very important part of a system for fault prediction and it is very helpful tool to improve efficiency of industry production. If we want to attain an optimalisation of production, have shortest lead times, low costs of production we need accurate information from maximal number of criteria collected from production machines, to setup best process parameters focused on reaching acceptable life time of machine parts. For this reason, we need improve the power of diagnostic systems with implementation of new methods with new possibilities.

Business Intelligence is set of techniques and tools for acquisition and transformation of raw data into meaningful and useful informations. Multicriterial diagnostic is approach to obtain real status of machining proces just in time and produce a big group of raw data. We want to prepare application business intelligence tools in multicriterial diagnostic process to obtain results of diagnostic process and find "hiddenly" influence the result.