GraDVis: A Visualization Tool for a Visual Data Management System
Authors: 
Jarrett Zapata, Syed Ferhan Haider, Ragaad Al-Tarawneh, 
Shah Rukh HumayounAuthors Info & Claims
Jarrett Zapata, Syed Ferhan Haider, Ragaad Al-Tarawneh, Shah Rukh HumayounAuthors Info & ClaimsPages 89 - 91
Abstract
Nowadays, visual data (image, videos, and feature vectors) is generated in many applications, such as surveillance or automated retail stores. The fast rate of accumulating such visual data makes the task of analyzing it or getting insights a tedious job for analysts. Intel Labs’ Visual Data Management System (VDMS) uses machine learning and data analytics pipelines to store and query visual data to enable faster access to large visual data. In this work, we present the design and implementation of a graph database visualization tool, called GradVis, to provide an interactive visualization interface to VDMS to enable the users to get insights about the stored graph data efficiently and interactively.
References
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Ragaad AlTarawneh, Christina Strong, Luis Remis, Pablo Munoz, Addicam Sanjay, and Srikanth Kambhatla. 2019. Navigating the Visual Fog: Analyzing and Managing Visual Data from Edge to Cloud. In 2nd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 19). USENIX Association, Renton, WA. https://www.usenix.org/conference/hotedge19/presentation/altarawneh
[2]
Niels de Jong. April 13, 2021. 15 Tools for Visualizing Your Neo4j Graph Database. In Neo4j, Inc.
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Alexandros Panagiotidis, Michael Burch, Oliver Deussen, Daniel Weiskopf, and Thomas Ertl. 2014. Graph Exploration by Multiple Linked Metric Views. In 2014 18th International Conference on Information Visualisation. 19–26. https://doi.org/10.1109/IV.2014.51
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Robert Pienta, James Abello, Minsuk Kahng, and Duen Horng Chau. 2015. Scalable graph exploration and visualization: Sensemaking challenges and opportunities. In 2015 International Conference on Big Data and Smart Computing (BIGCOMP). 271–278. https://doi.org/10.1109/35021BIGCOMP.2015.7072812
[5]
Luis Remis, Vishakha Gupta-Cledat, Christina Strong, and Ragaad Altarawneh. 2018. VDMS: Efficient Big-Visual-Data Access for Machine Learning Workloads. arxiv:1810.11832 [cs.DB]
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Pak Chung Wong, David Haglin, David Gillen, Daniel Chavarria, Vito Castellana, Cliff Joslyn, Alan Chappell, and Song Zhang. 2015. A visual analytics paradigm enabling trillion-edge graph exploration. In 2015 IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV). 57–64. https://doi.org/10.1109/LDAV.2015.7348072
Index Terms
- GraDVis: A Visualization Tool for a Visual Data Management System
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Published In
June 2024
129 pages
ISBN:9798400706516
DOI:10.1145/3660515
Copyright © 2024 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Published: 24 June 2024
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EICS '24
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EICS '24: The 16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems
June 24 - 28, 2024
Cagliari, Italy
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Overall Acceptance Rate 73 of 299 submissions, 24%
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