Visualizing Big Data with augmented and virtual reality: challenges and research agenda

Olshannikova, Ekaterina; Ometov, Aleksandr; Koucheryavy, Yevgeni; Olsson, Thomas

doi:10.1186/s40537-015-0031-2

Cited by 250 publications

(137 citation statements)

References 126 publications

(88 reference statements)

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“…VR for visual analytics still sits in a very niche area of applications, with just a few limited examples outside our own developments existing in the literature (Coffey et al 2011;Donalek et al 2014) and industry. 21 This is reflective of the challenges that exist in this area that include: balancing the availability of cost effective and efficient computing resource; combining methods for big data processing and analysis; exploiting the VR environment for representation of multidimensional data; and overcoming the limitations of human perception and cognition to facilitate human interaction with virtual objects (Olshannikova et al 2015).…”

Section: Datashield Applications For Data Visualisationmentioning

confidence: 99%

DataSHIELD – New Directions and Dimensions

Wilson

Butters

Avraam

et al. 2017

Data Science Journal

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In disciplines such as biomedicine and social sciences, sharing and combining sensitive individual-level data is often prohibited by ethical-legal or governance constraints and other barriers such as the control of intellectual property or the huge sample sizes. DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonised Individual-levEL Databases) is a distributed approach that allows the analysis of sensitive individual-level data from one study, and the co-analysis of such data from several studies simultaneously without physically pooling them or disclosing any data.Following initial proof of principle, a stable DataSHIELD platform has now been implemented in a number of epidemiological consortia. This paper reports three new applications of DataSHIELD including application to post-publication sensitive data analysis, text data analysis and privacy protected data visualisation. Expansion of DataSHIELD analytic functionality and application to additional data types demonstrate the broad applications of the software beyond biomedical sciences.

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Section: Datashield Applications For Data Visualisationmentioning

confidence: 99%

DataSHIELD – New Directions and Dimensions

Wilson

Butters

Avraam

et al. 2017

Data Science Journal

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“…AR has been applied to many domains, including "hands-free instruction and training, language translation, obstacle avoidance, advertising, gaming, museum tours, and much more" [4], maintenance and repair [17], or Big Data visualization [18], where AR "might solve many issues from narrow visual angle, navigation, scaling, etc. ".…”

Section: Augmented Reality For Learning Gamesmentioning

confidence: 99%

Design Patterns for Augmented Reality Learning Games

Emmerich

Klemke

Hummes

2017

Lecture Notes in Computer Science

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Abstract. Augmented Reality (AR) is expected to receive a major uptake with the recent availability of high quality wearable AR devices such as Microsoft's Hololens. However, the design of interaction with AR applications and games is still a field of experimentation and upcoming innovations in sensor technology provide new ways. With this paper, we aim to provide a step towards the structured use of design patterns for sensor-based AR games, which can also inform general application development in the field of AR.

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“…Since the organizations are investing big amount in processing the data, the returns in terms of accurate information has to be done. [6] Selecting the right visualization tool for the specific type of data is the challenge. [8] If the accurate tool is not selected, the visualization may present the improper information which may result into incorrect business decisions that are based on the information displayed by these tools.…”

Section: Challenges Of Digital Data Visualizationmentioning

confidence: 99%

Significance of Digital Data Visualization Tools in Big Data Analysis for Business Decisions

Mahajan¹,

Gokhale²

2017

IJCA

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Big Data is the large volume of data collected through various resources which integrates variety of structured, semi structured and unstructured data making it complex to analyze by using conventional methods and tools. Computer based visualization is the way of communicating the analyzed information to the user for the purpose of taking data driven business decisions. Visualization of the data is the way of presenting the large data graphically. It is a technique of displaying the information in such a graphical format that the information becomes easy to understand to the user. This interaction between the computer and the user is called graphical user interface. The entire process involves storing, processing and presenting the huge data for which different tools are being used. The tools used for storing and processing the data are considered as backend tools while visualization tools are called frontend tools. Visualization aims to improve the precision and manifestation of the information discovered. There are many conventional methods used for visualization of data. However they miss the potential of the Big Data. Images present the content in varied formats including graphs, charts, moving images, animation. Appropriate visualization of big data can have a major impact on the business in discovering hidden insights, improving decisions and automating business processes. Therefore specific framework has to be designed which will give precise idea to the user about the entire process. This paper shows the panorama of the significance of visualization of big data, the protocols to be followed while selecting the visualization tool and the classification of these tools based on various factors.

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Visualizing Big Data with augmented and virtual reality: challenges and research agenda

Cited by 250 publications

References 126 publications

DataSHIELD – New Directions and Dimensions

DataSHIELD – New Directions and Dimensions

Design Patterns for Augmented Reality Learning Games

Significance of Digital Data Visualization Tools in Big Data Analysis for Business Decisions

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