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Human-Centric Situational Awareness and Big Data Visualization

10 pagesPublished: September 27, 2019

Abstract

Human-centric situational awareness and visualization are needed for analyzing the big data in an efficient way. One of the challenges is to create an algorithm to analyze the given data without any help of other data analyzing tools. This research effort aims to identify how graphical objects (such as data-shapes) developed in accordance with an analyst's mental model can enhance analyst's situation awareness. Our approach for improved big data visualization is two-fold, focusing on both visualization and interaction. This paper presents the developed data and graph technique based on force- directed model graph in 3D. It is developed using Unity 3D gaming engine. Pilot testing was done with different data sets for checking the efficiency of the system in immersive environment and non-immersive environment. The application is able to handle the data successfully for the given data sets in data visualization. The currently graph can render around 200 to 300 linked nodes in real-time.

Keyphrases: Big Data, data visualization, Human-Centric Approach, Immersive Environment, Situational Awareness, Virtual Reality

In: Frederick C. Harris Jr, Sergiu Dascalu, Sharad Sharma and Rui Wu (editors). Proceedings of 28th International Conference on Software Engineering and Data Engineering, vol 64, pages 51--60

Links:
BibTeX entry
@inproceedings{SEDE2019:Human_Centric_Situational_Awareness_and,
  author    = {Sri Teja Bodempudi and Sharad Sharma and Atma Sahu and Rajeev Agrawal},
  title     = {Human-Centric Situational Awareness and Big Data Visualization},
  booktitle = {Proceedings of 28th International Conference on Software Engineering and Data Engineering},
  editor    = {Frederick Harris and Sergiu Dascalu and Sharad Sharma and Rui Wu},
  series    = {EPiC Series in Computing},
  volume    = {64},
  pages     = {51--60},
  year      = {2019},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/kwnj},
  doi       = {10.29007/mq54}}
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