Data analyses provide a profound insight into the internal structure of data. Algorithms from the field of machine learning are used to derive knowledge from existing data, which can then be used to create additional value in a certain context.
Prior to the analysis, it is often not clear which algorithms will lead to valuable results of a data analysis. Therefore, different algorithms with different parameters are often executed in several iterations and then the most promising result is chosen. However, this procedure encounters - especially in today's world - considerable problems: Due to the ever increasing amount of data, the execution time of each iteration requires more time.
Within the INTERACT project, selected aspects are investigated which reduce the execution time and at the same time lead to valuable results. In particular, the aspects (1) of the mining algorithms to be executed, (2) of the strategies to reduce the amount of data, and (3) of the execution environments for an analysis process are examined more closely.
The micro project INTERACT is performed within the Software Campus.