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semantic visualization of 3D urban environments

MULTIMEDIA TOOLS AND APPLICATIONS 2012年第2期59卷 505-521页

作者： Luis Pina, Jose Cerezo, Eva Seron, Francisco Univ Zaragoza Adv Comp Graph Grp GIGA Dept Comp Sci Engn Res Inst Aragon I3A Zaragoza Spain

The purpose of this work is the semantic visualization of complex 3D city models containing numerous dynamic entities, as well as performing interactive semantic walkthroughs and flights without predefined paths. This is achieved by using a 3D multilayer scene graph that integrates geometric and semantic information as well as by the performance of efficient geometric and what we call semantic view culling. The proposed semantic-geometric scene graph is a 3D structure composed of several layers which is suitable for visualizing geometric data with semantic meaning while the user is navigating inside the 3D city model. BqR-Tree is the data structure specially developed for the geometric layer for the purpose of speeding up rendering time in urban scenes. It is an improved R-Tree data structure based on a quadtree spatial partitioning which improves the rendering speed of the usual R-trees when view culling is implemented in urban scenes. The BqR-Tree is defined by considering the city block as the basic and logical unit. The advantage of the block as opposed to the traditional unit, the building, is that it is easily identified regardless of the data source format, and allows inclusion of mobile and semantic elements in a natural way. The usefulness of the 3D scene graph has been tested with low structured data, which makes its application appropriate to almost all city data containing not only static but dynamic elements as well.

关键词： semantic visualization 3D Scenegraphs Dynamic urban scenes Virtual flights and walkthroughs Data structures View culling

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semantic visualization of Wireless Sensor Networks for Elderly Monitoring

Semantic Visualization of Wireless Sensor Networks for Elder...

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1st International Joint Conference on Ambient Intelligence

作者： Stockloew, Carsten Kamieth, Felix Fraunhofer Inst Comp Graph Res D-64283 Darmstadt Germany

ISBN: (纸本)9783642169168

In the area of Ambient Intelligence, Wireless Sensor Networks are commonly used for user monitoring purposes like health monitoring and user localization. Existing work on visualization of wireless sensor networks focuses mainly on displaying individual nodes and logical, graph-based topologies. This way, the relation to the real-world deployment is lost. This paper presents a novel approach for visualization of wireless sensor networks and interaction with complex services on the nodes. The environment is realized as a 3D model, and multiple nodes, that are worn by a single individual, are grouped together to allow an intuitive interface for end users. We describe application examples and show that our approach allows easier access to network information and functionality by comparing it with existing solutions.

关键词： Wireless Sensor Network semantic visualization Elderly Care

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semantic visualization for Spherical Representation 14

Semantic Visualization for Spherical Representation

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20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Le, Tuan M., V Lauw, Hady W. Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (纸本)9781450329569

visualization of high-dimensional data such as text documents is widely applicable. The traditional means is to find an appropriate embedding of the high-dimensional representation in a low-dimensional visualizable space. As topic modeling is a useful form of dimensionality reduction that preserves the semantics in documents, recent approaches aim for a visualization that is consistent with both the original word space, as well as the semantic topic space. In this paper, we address the semantic visualization problem. Given a corpus of documents, the objective is to simultaneously learn the topic distributions as well as the visualization coordinates of documents. We propose to develop a semantic visualization model that approximates L-2-normalized data directly. The key is to associate each document with three representations: a coordinate in the visualization space, a multinomial distribution in the topic space, and a directional vector in a high-dimensional unit hypersphere in the word space. We join these representations in a unified generative model, and describe its parameter estimation through variational inference. Comprehensive experiments on real-life text datasets show that the proposed method outperforms the existing baselines on objective evaluation metrics for visualization quality and topic interpretability.

关键词： semantic visualization topic model generative model spherical space spherical semantic embedding dimensionality reduction L2-normalized vector

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SemVis: semantic visualization for Interactive Topical Analysis 17

SemVis: Semantic Visualization for Interactive Topical Analy...

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ACM Conference on Information and Knowledge Management (CIKM)

作者： Le, Tuan M. V. Lauw, Hady W. Singapore Management Univ 80 Stamford Rd Singapore 178902 Singapore

ISBN: (纸本)9781450349185

Exploratory analysis of a text corpus is an important task that can be aided by informative visualization. One spatially-oriented form of document visualization is a scatterplot, whereby every document is associated with a coordinate, and relationships among documents can be perceived through their spatial distances. semantic visualization further infuses the visualization space with latent semantics, by incorporating a topic model that has a representation in the visualization space, allowing users to also perceive relationships between documents and topics spatially. We illustrate how a semantic visualization system called SemVis could be used to navigate a text corpus interactively and topically via browsing and searching.

关键词： semantic visualization topic model interactive topical analysis

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Semi-supervised semantic visualization for Networked Documents 1

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21st Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Zhang, Delvin Ce Lauw, Hady W. Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore

ISBN: (数字)9783030865238

ISBN: (纸本)9783030865238;9783030865221

semantic interpretability and visual expressivity are important objectives in exploratory analysis of text. On the one hand, while some documents may have explicit categories, we could develop a better understanding of a corpus by studying its finer-grained structures, which may be latent. By inferring latent topics and discovering keywords associated with each topic, one obtains a semantic interpretation of the corpus. One the other hand, by visualizing documents, latent topics, and category labels on the same plot, one gains a bird's eye view of the relationships among documents, topics, and various categories. semantic visualization is a class of methods that unify both topic modeling and visualization. In this paper, we propose a novel semantic visualization model for networked documents that incorporates partial labels. We introduce coordinate-based label distribution and label-dependent topic distribution to visualize documents, topics, and labels in a semi-supervised way. We further derive three variants for singly-labeled, multi-labeled, and hierarchically-labeled documents. The focus on semi-supervision that employs variants of labeling structures is particularly novel. Experiments verify the efficacy of our model against baselines.

关键词： semantic visualization Topic modeling Dimensionality reduction Generative models

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semantic Evolutionary visualization 8th

Semantic Evolutionary Visualization

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8th International Conference on Swarm Intelligence (ICSI)

作者： Keshk, Marwa Univ New South Wales Canberra Canberra ACT Australia

ISBN: (纸本)9783319618333;9783319618326

The Evolutionary optimization (EO) field has become an active area of research for handling complex optimization problems. However, EO techniques need tuning to obtain better results. visualization is one approach used by EA researchers to identify early stagnation, loss of diversity, and other indicators that can help them to guide evolutionary search to better areas. In this paper, a semantic Evolutionary visualization framework (SEV) is proposed for analysing and exploring the potential EA dynamics. Empirical results have shown that the SEV can help to reveal and monitor information on evolutionary dynamics;thus, it can assist researchers in adapting the evolutionary parameters to obtain better performance.

关键词： semantic visualization Fitness landscape analysis EA

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Engineering User Centered Interaction Systems for semantic visualizations

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5th International Conference on Universal Access in Human-Computer Interaction held at the HCI International 2009

作者： Nazemi, Kawa Ullmann, Thomas Daniel Hornung, Christoph Fraunhofer Inst Comp Graph Res D-64283 Darmstadt Germany

ISBN: (纸本)9783642027062

For intuitive interaction with semantic visualizations, gesture-based interaction seems a promising way. However, the development Of Such ensembles is costly. To cut down the engineering effort, we propose a development model for interaction systems with semantic visualizations. In addition, we provide a set of evaluation tools to support the interaction developer engineer evaluating the engineering process.

关键词： semantic visualization gesture interaction universal access

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A taxonomy generation tool for semantic visual analysis of large corpus of documents

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第23期78卷 32919-32937页

作者： Carrion, Belen Onorati, Teresa Diaz, Paloma Triga, Vasiliki Univ Carlos III Madrid Dept Comp Sci Leganes Spain Cyprus Univ Technol Dept Commun & Internet Studies Limassol Cyprus

Taxonomies are semantic resources that help to categorize and add meaning to data. In a hyperconnected world where information is generated at a rate that exceeds human capacities to process and make sense of it, such semantic resources can help to access relevant information more efficiently by extracting knowledge from large and unstructured data sets. Taxonomies are related to specific domains of knowledge in which they identify relevant topics. However, they have to be validated by experts to guarantee that its terms and relations are meaningful. In this paper, we introduce a semiautomatic taxonomy generation tool for supporting domain experts in building taxonomies that are then used to automatically create semantic visualizations of data. Our proposal combines automatic techniques to extract, sort and categorize terms, and empowers domain experts to take part at any stage of the process by providing a visual edition tool. We tested the tool's usability in two use cases from different domains and languages. Results show that all the functionalities are easy to use and interact with. Lessons learned from this experience will guide the design of a utility evaluation involving domain experts interested in data analysis and knowledge modeling.

关键词： Knowledge modelling semantic visualization Taxonomy development process Big data

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semantics-Space-Time Cube: A Conceptual Framework for Systematic Analysis of Texts in Space and Time

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2020年第4期26卷 1789-1806页

作者： Li, Jie Chen, Siming Chen, Wei Andrienko, Gennady Andrienko, Natalia Tianjin Univ Coll Intelligence & Comp Tianjin 300072 Peoples R China Fraunhofer Inst IAIS D-53757 St Augustin Germany Univ Bonn D-53012 Bonn Germany Zhejiang Univ State Key Lab Cad&CG Hangzhou 310058 Peoples R China City Univ London London EC1V 0HB England

We propose an approach to analyzing data in which texts are associated with spatial and temporal references with the aim to understand how the text semantics vary over space and time. To represent the semantics, we apply probabilistic topic modeling. After extracting a set of topics and representing the texts by vectors of topic weights, we aggregate the data into a data cube with the dimensions corresponding to the set of topics, the set of spatial locations (e.g., regions), and the time divided into suitable intervals according to the scale of the planned analysis. Each cube cell corresponds to a combination (topic, location, time interval) and contains aggregate measures characterizing the subset of the texts concerning this topic and having the spatial and temporal references within these location and interval. Based on this structure, we systematically describe the space of analysis tasks on exploring the interrelationships among the three heterogeneous information facets, semantics, space, and time. We introduce the operations of projecting and slicing the cube, which are used to decompose complex tasks into simpler subtasks. We then present a design of a visual analytics system intended to support these subtasks. To reduce the complexity of the user interface, we apply the principles of structural, visual, and operational uniformity while respecting the specific properties of each facet. The aggregated data are represented in three parallel views corresponding to the three facets and providing different complementary perspectives on the data. The views have similar look-and-feel to the extent allowed by the facet specifics. Uniform interactive operations applicable to any view support establishing links between the facets. The uniformity principle is also applied in supporting the projecting and slicing operations on the data cube. We evaluate the feasibility and utility of the approach by applying it in two analysis scenarios using geolocated social media da

关键词： Task analysis semantics visualization Time measurement Data visualization Complexity theory Aggregates Spatiotemporal visualization semantic visualization data cube interactive exploration visual analytics

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Visualizing risk factors of dementia from scholarly literature using knowledge maps and next-generation data models

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 165-182页

作者： Kiran Fahd Sitalakshmi Venkatraman Department of Information Technology Melbourne Polytechnic3181 PrahranVICAustralia

Scholarly communication of knowledge is predominantly document-based in digital repositories,and researchers find it tedious to automatically capture and process the semantics among related *** the present digital era of big data,there is a lack of visual representations of the knowledge present in scholarly articles,and a time-saving approach for a literature search and visual navigation is *** majority of knowledge display tools cannot cope with current big data trends and pose limitations in meeting the requirements of automatic knowledge representation,storage,and dynamic *** address this limitation,the main aim of this paper is to model the visualization of unstructured data and explore the feasibility of achieving visual navigation for researchers to gain insight into the knowledge hidden in scientific articles of digital *** topics of research and practice,including modifiable risk factors leading to a dramatic increase in Alzheimer’s disease and other forms of dementia,warrant deeper insight into the evidence-based knowledge available in the *** goal is to provide researchers with a visual-based easy traversal through a digital repository of research *** paper takes the first step in proposing a novel integrated model using knowledge maps and next-generation graph datastores to achieve a semantic visualization with domain-specific knowledge,such as dementia risk *** model facilitates a deep conceptual understanding of the literature by automatically establishing visual relationships among the extracted knowledge from the big data resources of research *** also serves as an automated tool for a visual navigation through the knowledge repository for faster identification of dementia risk factors reported in scholarly ***,it facilitates a semantic visualization and domain-specific knowledge discovery from a large digital repository and their associations.

关键词： Big data Data visualization Knowledge maps Dementia Non-relational database Graph database Neo4j semantic visualization

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