International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 7, No. 6, 8 Nov. 2017
Cover page and Table of Contents: PDF (size: 297KB)
DataViz Model: A Novel Approach towards Big Data Analytics and Visualization
Full Text (PDF, 297KB), PP.43-49
Views: 0 Downloads: 0
Author(s)
Rohit More
1
R H Goudar
1
Index Terms
Big Data, Big Data analytics, Data Visualization, DataViz
Abstract
Big Data is the collection of large data sets which contains large amount of data. There are different areas which are generating huge data, this data may be present in the form of semi-structured or unstructured and to get useful information from such raw data there is need of data analysis. Due to Big Data’s excessive volume, variety, and velocity it is very difficult to store and process huge data. The process of extracting the information from such raw data is called Big Data Analytics. Big data Analytics processes data gives result in the form of structured data. Again this data is huge size and very difficult to understand since it is present in the form of CSV or excel or simple text files. So for effective decision making and to understand the information quickly the data need to be visualized as human mind understands images and graphs better and faster than text data. In this paper a model called Data Visualization (Viz) is designed which integrates big data analytics and the data visualization. This model first takes the data from various sources and then processes it and converts it into structured form, if want this data can be stored to RDBMS. Finally the text result can be visualized with the help of Visualization module of the DataViz. Here text result is represented in the form of charts and graphs.
Cite This Paper
Rohit More, R H Goudar,"DataViz Model: A Novel Approach towards Big Data Analytics and Visualization", International Journal of Engineering and Manufacturing(IJEM), Vol.7, No.6, pp.43-49, 2017. DOI: 10.5815/ijem.2017.06.04
Reference
[1]Ishan Sharma, Rajeev Tiwari and Abhineet Anand. “Open Source Big Data Analytics Technique”, International Conference on Data Engineering and Communication Technology Volume 468 of the seriesAdvances in Intelligent Systems and Computing, pp 593-602, 2016.
[2]Daniel A. Keim, Florian Mansmann, J¨orn Schneidewind, Jim Thomas, and Hartmut Ziegler “Visual Analytics: Scope and Challenges”, Visual Data Mining, pp 76-90, 2008.
[3]Pravin Chopade, Justin Zhan, Kaushik Roy, Kenneth Flurchick. “Real-Time Large-Scale Big Data Networks Analytics and Visualization Architecture”, 2015 12th International Conference & Expo on Emerging Technologies for a Smarter World (CEWIT), pp 1-6, 2015.
[4]Yingjian Qi, Xinyan Yu, Guoliang Shi, Ying Li. “Visualization In Media Big Data Analysis”, 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS), pp 571-574, 2015.
[5]Qunchao Fu, Wanheng Liu, Tengfei Xue, Heng Gu, Siyue Zhang, Cong Wang. “A Big Data Processing Methods for Visualization”, 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems, pp 571-575, 2014.
[6]Zipeng Liu, Zhenhuang Wang, Siming Chen, Zuchao Wang, Zhengjie Miao, Xiaoru Yuan. “A Platform for Collaborative Visual Analysis on Streaming Messages”, 2014 IEEE Conference on Visual Analytics Science and Technology (VAST), pp 375-376, 2014.
[7]Mark Shackleton, Fadi El-Moussa, Robert Rowlingson, Alex Healing, John Crowther, Joshua Daniel, Theo Dimitrakos, and Ali Sajjad. “Deploying Visual Analytics Through a Multi-cloud Service Store with Encrypted Big Data”, On the Move to Meaningful Internet Systems: OTM 2016 Conferences Volume 10033 of the series Lecture Notes in Computer Science , pp 883-889, 2016.
[8]James Davey, Florian Mansmann, J¨orn Kohlhammer, and Daniel Keim “Visual Analytics: Towards Intelligent Interactive Internet and Security Solutions”, The Future Internet Assembly FIA 2012, pp 93-104, 2012.
[9]Divanshu Gupta, Avinash Sharma, Narayanan Unny, and Geetha Manjunath. “Graphical Analysis and Visualization of Big Data in Business Domains”, Big Data Analytics Volume 8883 of the series Lecture Notes in Computer Science, pp 53-56, 2014.
[10]Nascif A. Abousalh-Neto, Sumeyye Kazgan “Big data exploration through visual analytics”, 2012 IEEE Conference on visual Analytics Science and Technology(VAST), pp 285 – 286, 2012.
[11]Sachchidanand singh, Nirmal Singh “ Big Data analytics”, 2012 International Conference on Communication, information & computing Technology(ICCICT), pp 1 – 4, 2012.
[12]David Jonker, scott Langevin, peterSchretlen, Casey Canfield “Agile visual analytics for banking cyber big data”, 2012 IEEE Conference on visual Analytics Science and Technology (VAST), pp 299 – 300, 2012.
[13]Shinnosuke Takeda, Aimi kobayashi, Hiroaki Kobayashi, Saori Okubo, Kazuo Misue “Irregular Trend Finder: Visualization tool for analyzing time-series big data”, 2012 IEEE Conference on visual Analytics Science and Technology(VAST) , 305 – 306, 2012.
[14]Daniel Keim, Huamin Qu, Kwan-Liu Ma “Big Data Visualization” IEEE computer Graphics and Application, volume 33, pp 20 – 21, 2013.
[15]Bapin Bihari Jayasingh, M. R. Patra, D Bhanu Mahesh “Security issues and challenges of big data analytics and visualization” 2016 2nd International Conference on Contemporary Computing and Inforatics(IC3I), pp 204 – 208, 2016.