Open Access
Big Data Visualization Tools
Nikos Bikakis
- 01 Jan 2018
TL;DR: Data visualization is the presentation of data in a pictorial or graphical format, and a data visualization tool is the software that generates this presentation as discussed by the authors, providing users with intuitive means to interactively explore and analyze data, enabling them to effectively identify interesting patterns, infer correlations and causalities.
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Abstract: Data visualization is the presentation of data in a pictorial or graphical format, and a data visualization tool is the software that generates this presentation. Data visualization provides users with intuitive means to interactively explore and analyze data, enabling them to effectively identify interesting patterns, infer correlations and causalities, and supports sense-making activities.
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Citations
Making data visualization more efficient and effective: a survey
Xuedi Qin,Yuyu Luo,Nan Tang,Guoliang Li +3 more
- 01 Jan 2020
TL;DR: Techniques that make data visualization more efficient and effective are surveyed, which include auto-complete an incomplete specification, to discover more interesting visualizations based on a reference visualization.
209
•Book
Linked Data Visualization: Techniques, Tools, and Big Data
Laura Po,Nikos Bikakis,Federico Desimoni,George Papastefanatos +3 more
- 20 Mar 2020
TL;DR: This paper presents a meta-modelling architecture that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and cataloging individual pieces of data to create a graph of their contents.
Empirical Evaluation of Linked Data Visualization Tools
Federico Desimoni,Laura Po +1 more
TL;DR: This paper deeply analyzes the state of the art of tools for LD visualization and defines some suggestions for LD consumers in order for them to be able to select the most appropriate tools based on the type of analysis they wish to perform.
29
An exploratory teaching program in big data analysis for undergraduate students
TL;DR: According to students’ feedback, the exploratory teaching program is useful for learning how to analyze large datasets and identify patterns that will improve any company’s and organization decision-making process.
26
Big Data Analysis and Services: Visualization on Smart Data to Support Healthcare Analytics
Carson K. Leung,Yibin Zhang,Calvin S. H. Hoi,Joglas Souza,Bryan H. Wodi +4 more
- 14 Jul 2019
TL;DR: This paper presents a system for visualizing smart data–as well as their related information and knowledge–from health data, which in turn supports healthcare analytics.
18
References
The eyes have it: a task by data type taxonomy for information visualizations
Ben Shneiderman
- 03 Sep 1996
TL;DR: A task by data type taxonomy with seven data types and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts) is offered.
Graph visualization and navigation in information visualization: A survey
TL;DR: This is a survey on graph visualization and navigation techniques, as used in information visualization, which approaches the results of traditional graph drawing from a different perspective.
Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data
TL;DR: This paper presents a new method for visualizing compound graphs based on visually bundling the adjacency edges, i.e., non-hierarchical edges, together and discusses the results based on an informal evaluation provided by potential users of such visualizations.
1.2K
•Book
The Visualization Handbook
Charles Hansen,Chris R. Johnson +1 more
- 01 Jan 2005
TL;DR: The Visualization Handbook focuses on Scalar Field Visualization, which addresses the challenges of large-scale data visualization, and its applications in the SCIRun Problem Solving Environment.
1.1K
BlinkDB: queries with bounded errors and bounded response times on very large data
Sameer Agarwal,Barzan Mozafari,Aurojit Panda,Henry Milner,Samuel Madden,Ion Stoica +5 more
- 15 Apr 2013
TL;DR: BlinkDB allows users to trade-off query accuracy for response time, enabling interactive queries over massive data by running queries on data samples and presenting results annotated with meaningful error bars.
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