Analytical Review of Data Visualization Methods in Application to Big Data

This paper describes the term Big Data in aspects of data representation and visualization. There are some specific problems in Big Data visualization, so there are definitions for these problems and a set of approaches to avoid them. Also, we make a review of existing methods for data visualization in application to Big Data and taking into account the described problems. Summarizing the result, we have provided a classification of visualization methods in application to Big Data. 1. Introduction The customers need to process secondary data, which is not directly connected to the customers business which has lead to the phenomenon called Big Data. Bellow we will provide the definition of the Big Data term. Big Data, as mentioned by Gubarev Vasiliy Vasil’evich—is a phenomenon, which have no clear borders, and can be presented in unlimited or even infinite data accumulation. And even more, the accumulated data can be presented in various data formats, most of them are not structural data flows. Usually, under the term of Big Data we understand a large data set, with volume growing exponentially. This data set can be too large, too “raw”, or too unstructured for classical data processing methods, used in relational data bases theory. Still, the main concern in that question is not the data volume, but the field of application of that data [1]. It is used to provide the following Big Data properties in different analytical literature sources: large volume of data (Volume), multiformat data presentation (Variety), and high data processing speed (Velocity). It is thought that if the exact data satisfies only two of three described properties, it can be related to the Big Data class [2, 3]. Therefore, nowadays, there are the following Big Data classes: “Volume-Velocity” class, “Volume-Variety” class, “Velocity-Variety” class, and “Volume-Velocity-Variety” class. The Big Data processing is not a trivial task at all, and it requires special methods and approaches. Graphical thinking is a very simple and natural type of data processing for a human being, so, it can be said, that image data representation is an effective method, which allows for easing data understanding and provides enough support for decision making. But, in case of Big Data, most of classical data representation methods become less effective or even not applicable for concrete tasks. Analysis of applicability for one of the concrete classes of Big Data is a topical problem of subject area as there are no such case studies held before. Therefore, there is a purpose for this paper: