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Data & Knowledge Engineering, 1992
Abstract The Visual Data Model VDM and the Visual Data Language VDL are introduced. They provide a unique approach to modelling and manipulating data through spatial arrangement of geometric objects. They are based on the functional data model and the clausal form of the first order logic; however all data and all database activities are represented ...
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Abstract The Visual Data Model VDM and the Visual Data Language VDL are introduced. They provide a unique approach to modelling and manipulating data through spatial arrangement of geometric objects. They are based on the functional data model and the clausal form of the first order logic; however all data and all database activities are represented ...
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The Visualization of Scientific Data
1989Scientific Data Visualization is now a major topic of discussion initiated by the NSF Workshop Report that addressed the need for improved techniques for data visualization. In this paper we discuss how the various “classical” visualization techniques are being interpreted in current and evolving scientific visualization environments.
Georges G. Grinstein, R. Daniel Bergeron
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Computing in Science & Engineering, 1999
The old proverb, "And out of mind as soon as out of sight," attributed to Lord Brooke (1554(1628) generally referred to the passing of evil or harm. In the context of large-scale data analysis and simulation, however, it might foreshadow the loss of important information.
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The old proverb, "And out of mind as soon as out of sight," attributed to Lord Brooke (1554(1628) generally referred to the passing of evil or harm. In the context of large-scale data analysis and simulation, however, it might foreshadow the loss of important information.
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2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), 2015
Big data has many divergent types of sources, from physical (sensor/IoT) to social and cyber (web) types, rendering it messy, imprecise, and incomplete. Due to its quantitative (volume and velocity) and qualitative (variety) challenges, big data to the users resembles something like “the elephant to the blind men”.
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Big data has many divergent types of sources, from physical (sensor/IoT) to social and cyber (web) types, rendering it messy, imprecise, and incomplete. Due to its quantitative (volume and velocity) and qualitative (variety) challenges, big data to the users resembles something like “the elephant to the blind men”.
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Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010
Easy-to-use visual interfaces to data can broadly expand the audience for databases. Domain experts rather than database experts can engage in rapid-fire Q&A sessions with the data. Visual interfaces can provide a medium for story-telling, debate, and conversations about the data.
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Easy-to-use visual interfaces to data can broadly expand the audience for databases. Domain experts rather than database experts can engage in rapid-fire Q&A sessions with the data. Visual interfaces can provide a medium for story-telling, debate, and conversations about the data.
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Proceedings of the 7th annual ACM symposium on User interface software and technology - UIST '94, 1994
Computer sliders are a generic user input mechanism for specifying a numeric value from a range. For data visualization, the effectiveness of sliders may be increased by using the space inside the slider as• an interactive color scale,• a barplot for discrete data, and• a density plot for continuous data.The idea is to show the selected values in ...
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Computer sliders are a generic user input mechanism for specifying a numeric value from a range. For data visualization, the effectiveness of sliders may be increased by using the space inside the slider as• an interactive color scale,• a barplot for discrete data, and• a density plot for continuous data.The idea is to show the selected values in ...
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2018
More than 25 years ago we developed a data visualization system called Vibe. During this same period we developed a system for collaborative authoring – CASCADE – that made heavy use of visualization. These were but a few of many efforts at that time to develop new methods for understanding data, stimulated by improved hardware - faster CPUs, more ...
Kai A. Olsen +2 more
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More than 25 years ago we developed a data visualization system called Vibe. During this same period we developed a system for collaborative authoring – CASCADE – that made heavy use of visualization. These were but a few of many efforts at that time to develop new methods for understanding data, stimulated by improved hardware - faster CPUs, more ...
Kai A. Olsen +2 more
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Visualization of Semantic Data
2015The main goal of the Semantic Web is to direct the current syntactic web on the path to the Semantic Web. The vision of the Semantic Web is to interpret information on the web to be readable and machine-interpretable. Therefore, the article focuses on the creation of an instrument for visualizing semantic data on the basis of the identified advantages ...
Martin Zácek +2 more
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Proceedings of the 2013 workshop on Data driven functional programming, 2013
We present a language-integrated technique that can be applied to enlist web-based data visualization libraries in the type-safe discipline of F#, and to use them with various data access mechanisms to visualize data on the web quickly and effectively using WebSharper[1], an open source web framework for F#[2].
Loïc Denuzière +2 more
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We present a language-integrated technique that can be applied to enlist web-based data visualization libraries in the type-safe discipline of F#, and to use them with various data access mechanisms to visualize data on the web quickly and effectively using WebSharper[1], an open source web framework for F#[2].
Loïc Denuzière +2 more
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2003
1: Introduction. 1.1. Challenges. 1.2. Research Scope. 1.3. State-of-the-Art. 1.4. Outline of Book. 2: Overview Of Visual Information Representation. 2.1. Color. 2.2. Texture. 2.3. Shape. 2.4. Spatial Layout. 2.5. Interest Points. 2.6. Image Segmentation. 2.7. Summary. 3: Edge-based Structural Features. 3.1. Visual Feature Representation. 3.2.
Xiang Sean Zhou +2 more
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1: Introduction. 1.1. Challenges. 1.2. Research Scope. 1.3. State-of-the-Art. 1.4. Outline of Book. 2: Overview Of Visual Information Representation. 2.1. Color. 2.2. Texture. 2.3. Shape. 2.4. Spatial Layout. 2.5. Interest Points. 2.6. Image Segmentation. 2.7. Summary. 3: Edge-based Structural Features. 3.1. Visual Feature Representation. 3.2.
Xiang Sean Zhou +2 more
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