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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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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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Visualizing oceanographic data
IEEE Computer Graphics and Applications, 1989The use of visualization to study both field-collected and simulated oceanographic data is discussed. An overview is presented of three programs in which visualization has provided important insight or technology. The diverse technology used-animation, computer vision, and synthesis of signal processing, image processing, and display-illustrate the ...
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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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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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2019
As data becomes more accessible, visualization methods are needed to help make sense of the information. Analyzing and visualizing data helps the public to better recognize the patterns and connections between different datasets. By using visual elements such as graphs, charts, and maps, it is easier to see and understand the trends and outliers in ...
Veronica Castro Alvarez, Ching-Yu Huang
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As data becomes more accessible, visualization methods are needed to help make sense of the information. Analyzing and visualizing data helps the public to better recognize the patterns and connections between different datasets. By using visual elements such as graphs, charts, and maps, it is easier to see and understand the trends and outliers in ...
Veronica Castro Alvarez, Ching-Yu Huang
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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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Statistics in Medicine, 2003
AbstractData mining strategies are usually applied to opportunistically collected data and frequently focus on the discovery of structure such as clusters, bumps, trends, periodicities, associations and correlations, quantization and granularity, and other structures for which a visual data analysis is very appropriate and quite likely to yield insight.
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AbstractData mining strategies are usually applied to opportunistically collected data and frequently focus on the discovery of structure such as clusters, bumps, trends, periodicities, associations and correlations, quantization and granularity, and other structures for which a visual data analysis is very appropriate and quite likely to yield insight.
openaire +2 more sources