Conclusion

In practice, there are a lot of challenges for Big Data processing and analysis. As all the data is currently visualized by computers, it leads to difficulties in the extraction of data, followed by its perception and cognition. Those tasks are time-consuming and do not always provide correct or acceptable results.

In this paper we have obtained relevant Big Data Visualization methods classification and have suggested the modern tendency towards visualization-based tools for business support and other significant fields. Past and current states of data visualization were described and supported by analysis of advantages and disadvantages. The approach of utilizing VR, AR and MR for Big Data Visualization is presented and the advantages, disadvantages and possible optimization strategies of those are discussed.

For visualization problems discussed in this work, it is critical to understand the issues related to human perception and limited cognition. Only after that, the field of design can provide more efficient and useful ways to utilize Big Data. It can be concluded that data visualization methodology may be improved by considering fundamental cognitive psychological principles and by implementing most natural interaction with visualized virtual objects. Moreover, extending it with functions to exclude blind spots and decreased vision sectors would highly improve recognition time for people with such a disease. Furthermore, a step towards wireless solutions would extend device battery life in addition to computation and quality improvements.