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Wiz: A Web-Based Tool for Interactive Visualization of Big Data

Balzer, Christopher and Oktavian, Rama and Zandi, Mohammad and Fairen-Jimenez, David and Moghadam, Peyman Z. (2020) Wiz: A Web-Based Tool for Interactive Visualization of Big Data. Patterns, 1 (8). Art. No. 100107. ISSN 2666-3899. https://resolver.caltech.edu/CaltechAUTHORS:20200925-135426116

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Abstract

In an age of information, visualizing and discerning meaning from data is as important as its collection. Interactive data visualization addresses both fronts by allowing researchers to explore data beyond what static images can offer. Here, we present Wiz, a web-based application for handling and visualizing large amounts of data. Wiz does not require programming or downloadable software for its use and allows scientists and non-scientists to unravel the complexity of data by splitting their relationships through 5D visual analytics, performing multivariate data analysis, such as principal component and linear discriminant analyses, all in vivid, publication-ready figures. With the explosion of high-throughput practices for materials discovery, information streaming capabilities, and the emphasis on industrial digitalization and artificial intelligence, we expect Wiz to serve as an invaluable tool to have a broad impact in our world of big data.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.patter.2020.100107DOIArticle
https://wiz.shef.ac.uk/Related ItemWiz website
https://github.com/peymanzmoghadam/WizRelated Itempublic version of code
ORCID:
AuthorORCID
Balzer, Christopher0000-0002-9767-8437
Oktavian, Rama0000-0003-0701-4213
Zandi, Mohammad0000-0003-0017-1080
Moghadam, Peyman Z.0000-0002-1592-0139
Additional Information:© 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Received 16 May 2020, Revised 14 July 2020, Accepted 25 August 2020, Available online 23 September 2020. P.Z.M. thanks the Corporate Information and Computing Services (CiCS) and Partnerships and Regional Engagement at the University of Sheffield for providing partial funds for the project. D.F.-J. thanks the Royal Society for funding through University Research Fellowships. P.Z.M. also thanks John Dale from the University of Sheffield, and Yosof Badr and David Moss from Siemens for useful discussions. M.Z. acknowledges the Knowledge Exchange funding (X/013145) from the University of Sheffield. R.O. acknowledges Indonesia Endowment Fund for Education (LPDP) for funding his doctoral study and also acknowledges Muhammad Rifaldi from Brawijaya University for assisting in the design of front cover for this paper. The authors also thank the University of Sheffield for providing infrastructure to host Wiz. Author Contributions. D.F.-J. and P.Z.M. conceptualized the study. C.B. created the Wiz app and all documentation for Wiz under supervision of D.F.-J. and P.Z.M. P.Z.M. wrote all scripts to host Wiz and authored the initial draft of the manuscript. R.O. and M.Z. contributed to design and testing of Wiz. All authors contributed to manuscript review. Resource Availability. Lead Contact. Peyman Z. Moghadam is the lead contact of this study and can be reached by email: p.moghadam@sheffield.ac.uk. Materials Availability. This study did not generate new materials. Data and Code Availability. The Wiz website is hosted by the University of Sheffield and can be freely accessed at https://wiz.shef.ac.uk/. All data uploaded to Wiz are only stored during the user session via cache and removed after the session is ended. The public version of Wiz is available in a Github repository https://github.com/peymanzmoghadam/Wiz. Declaration of Interests. P.Z.M. has financial interest through Monoclinic Ltd. D.F.-J. has financial interest through Immaterial Ltd. The other authors declare no competing interests.
Funders:
Funding AgencyGrant Number
University of SheffieldX/013145
Royal SocietyUNSPECIFIED
Indonesia Endowment Fund for EducationUNSPECIFIED
Subject Keywords:data visualization; web app; big data; interactive plots; data visualisation for Industry 4.0; multi-dimensional data analytics; principle component analysis; digitalization
Issue or Number:8
Record Number:CaltechAUTHORS:20200925-135426116
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200925-135426116
Official Citation:Christopher Balzer, Rama Oktavian, Mohammad Zandi, David Fairen-Jimenez, Peyman Z. Moghadam, Wiz: A Web-Based Tool for Interactive Visualization of Big Data, Patterns, Volume 1, Issue 8, 2020, 100107, ISSN 2666-3899, https://doi.org/10.1016/j.patter.2020.100107. (http://www.sciencedirect.com/science/article/pii/S2666389920301410)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105571
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:28 Sep 2020 14:12
Last Modified:02 Dec 2020 16:48

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