First of all, we try to automate data collection from various distributed sources including big data sets. Data volumes are now outgrowing the capacity to process them, so nowadays managing data volumes can become a nontrivial task.

The next step involves transforming, processing and cleansing data using highly efficient intellectual algorithms, followed by modeling, analysis and applied computing tasks.  An important role is given to interactive 2D and 3D data visualization by different parameters. This is an excellent tool for initial analysis that provides us the opportunity to reveal common tendencies and potential problems in data sets.

The most intellectual tasks are related to machine learning, in particular classification and clustering. We construct complex classification models and then use a set of examples to discover common tendencies and make rules applicable to new examples. After multiple modeling and selecting efficient models we can solve forecasting, recognition, image processing and other tasks