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Visualizing Big Data with Augmented and Virtual Reality

Introduction

The whole history of humanity is an enormous accumulation of data. Information has been stored for thousands of years. Data has become an integral part of history, politics, science, economics and business structures, and now even social lives. This trend is clearly visible in social networks such as Facebook, Twitter and Instagram where users produce an enormous stream of different types of information daily (music, pictures, text, etc.). Now, government, scientific and technical laboratory data as well as space research information are available not only for review, but also for public use. For instance, there is the 1000 Genomes Project, which provide 260 terabytes of human genome data. More than 20 terabytes of data are publicly available at Internet Archive, ClueWeb09, among others.

Lately, Big Data processing has become more affordable for companies from resource and cost points of view. Simply put, revenues generated from it are higher than the costs, so Big Data processing is becoming more and more widely used in industry and business. According to International Data Corporation (IDC), data trading is forming a separate market. Indeed, 70 % of large organizations already purchase external data, and it is expected to reach 100 % by the beginning of 2019.

Simultaneously, Big Data characteristics such as volume, velocity, variety, value and veracity require quick decisions in implementation, as the information may become less up to date and can lose value fast. According to IDC, data volumes have grown exponentially, and by 2020 the number of digital bits will be comparable to the number of stars in the universe. As the size of bits geminates every two years, for the period from 2013 to 2020 worldwide data will increase from 4.4 to 44 zettabytes. Such fast data expansion may result in challenges related to human ability to manage the data, extract information and gain knowledge from it.

The complexity of Big Data analysis presents an undeniable challenge: visualization techniques and methods need to be improved. Many companies and open-source projects see the future of Big Data Analytics via Visualization, and are establishing new interactive platforms and supporting research in this area. Husain et al. in their paper provide a wide list of contemporary and recently developed visualization platforms. There are commercial Big Data platforms such as International Business Machines (IBM) Software, Microsoft, Amazon and Google. There exists an open-source project, Socrata, which deals with dynamic data from public, government and private organizations. Another platform is a JavaScript library D3 for dynamic data visualizations. This list can be extended with Cytoscape, Tableau, Data Wrangler and others. Intel and Statistical Analysis System (SAS) are performing research in data visualization as well but more from a business perspective.

Organizations and social media generate enormous amounts of data every day and, traditionally, represent it in a format consistent with the poorly structured databases: web blogs, text documents, or machine code, such as geospatial data that may be collected in various stores even outside of a company/organization. On the other hand, information stored in a multitude repository and the use of cloud storage or data centers is also widely common. Furthermore, companies have the necessary tools to establish the relationship between data segments in addition to the process of making the basis for meaningful conclusions. As data processing rates are growing continuously, a situation may appear when traditional analytical methods would not be able to stay up to date, especially with the growing amount of constantly updated data, which ultimately opens the way for Big Data technologies.

This paper provides information about various types of existing data to which certain techniques are useful for the analysis. Recently, many visualization methods have been developed for a quick representation of data that is already preprocessed. There has been a step away from planar images towards multi-dimensional volumetric visualizations. However, Big Data visualization evolution cannot be considered as finished, inasmuch as new techniques generate new research challenges and solutions that will be discussed in the following paper.

Current activity in the field of Big Data visualization is focused on the invention of tools that allow a person to produce quick and effective results working with large amounts of data. Moreover, it would be possible to assess the analysis of the visualized information from all the angles in novel, scalable ways. Based on Big Data related literature, we identify the main visualization challenges and propose a novel technical approach to visualize Big Data based on the understandings of human perception and new Mixed Reality (MR) technologies. From our perspective, one of the more promising methods for improving upon current Big Data visualization techniques is in its correlation with Augmented Reality (AR) and Virtual Reality (VR) that are suitable for the limited perception capabilities of humans. We identify important steps for the research agenda to implement this approach.

This paper covers various issues and topics, but there are three main directions of this survey:

  • Human cognitive limitations in terms of Big Data Visualization.
  • Applying Augmented and Virtual reality opportunities towards Big Data Visualization.
  • Challenges and benefits of the proposed visualization approach.

The rest of paper is organized as follows: The first section provides a definition of Big Data and looks at currently used methods for Big Data processing and their specifications. Also it indicates the main challenges and issues in Big Data analysis. Next, in the section Visualization methods, the historical background of this field is given, modern visualization techniques for massive amounts of information are presented and the evolution of visualization methods is discussed. Further in the last section, Integration with Augmented and Virtual Reality, the history of AR and VR is detailed with respect to its influence on Big Data. These developmental processes are supported by the proposed oncoming Big Data visualization extension for VR and AR, which can solve actual perception and cognition challenges. Finally, important data visualization challenges and future research agenda are discussed.