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Strategy planner: Graphical definition of soccer set-plays

data & knowledge ENGINEERING 2014年第PartA期94卷 110-131页

作者： Cravo, Joao Almeida, Fernando Abreu, Pedro Henriques Reis, Luis Paulo Lau, Nuno Mota, Luis Univ Porto Fac Engn Dept Informat Engn P-4200465 Oporto Portugal Univ Coimbra Fac Sci & Technol Dept Informat Engn P-3030790 Coimbra Portugal Univ Coimbra Ctr Informat & Syst P-3030290 Coimbra Portugal Univ Porto Artificial Intelligence & Comp Sci Lab P-4200465 Oporto Portugal Univ Aveiro P-3810193 Aveiro Portugal Inst Elect & Telemat Engn Aveiro P-3810193 Aveiro Portugal Polytech Inst Viseu Dept Informat P-3504510 Viseu Portugal Univ Inst Lisboa ISCTE IUL P-1649026 Lisbon Portugal

One of the research topics on multi-agent systems focuses on the development of mechanisms such as plans to empower a team of agents to cooperate in order to perform complex tasks. In many cases, the definition of these plans are based on a specific and rather complex grammar and stored in structured text files. In the context of the 2D simulated Robotic Soccer domain, a set-play language was proposed to coordinate the execution of teammates' behaviors to improve a team's overall performance. The process of manually writing set-play definitions is hazardous and can benefit from the use of a graphical tool to reach new users and allow typical users to become more productive. This work presents such a tool for which several experiments were run to measure its usability with forty two users by having them perform a set of tasks for which their execution time, number errors and satisfaction were recorded. The tool reduced the previous average time required to completely define a set-play by 90% and enabled even non-expert users to use it Moreover, users were on average satisfied with SPlanner having ranked it with a score of 77 (out of 100) using a System Usability Scale questionnaire. (C) 2014 Elsevier B.V. All rights reserved.

关键词： data and knowledge visualization Methodologies and tools Graphical planning Set-play Robotic soccer

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A Space-Filling visualization Technique for Multivariate Small-World Graphs

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2012年第5期18卷 797-809页

作者： Wong, Pak Chung Foote, Harlan Mackey, Patrick Chin, George Huang, Zhenyu Thomas, Jim Pacific NW Natl Lab Richland WA 99352 USA

We introduce an information visualization technique, known as GreenCurve, for large multivariate sparse graphs that exhibit small-world properties. Our fractal-based design approach uses spatial cues to approximate the node connections and thus eliminates the links between the nodes in the visualization. The paper describes a robust algorithm to order the neighboring nodes of a large sparse graph by solving the Fiedler vector of its graph Laplacian, and then fold the graph nodes into a space-filling fractal curve based on the Fiedler vector. The result is a highly compact visualization that gives a succinct overview of the graph with guaranteed visibility of every graph node. GreenCurve is designed with the power grid infrastructure in mind. It is intended for use in conjunction with other visualization techniques to support electric power grid operations. The research and development of GreenCurve was conducted in collaboration with domain experts who understand the challenges and possibilities intrinsic to the power grid infrastructure. The paper reports a case study on applying GreenCurve to a power grid problem and presents a usability study to evaluate the design claims that we set forth.

关键词： data and knowledge visualization information visualization visualization techniques and methodologies

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Cardinality constraints on qualitatively uncertain data

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data & knowledge ENGINEERING 2015年 99卷 126-150页

作者： Hall, Neil Koehler, Henning Link, Sebastian Prade, Henri Zhou, Xiaofang Univ Auckland Dept Comp Sci Auckland 1 New Zealand Massey Univ Sch Engn & Adv Technol Palmerston North New Zealand Univ Toulouse 3 CNRS IRIT F-31062 Toulouse France Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia Soochow Univ Suzhou Peoples R China

Modern applications require advanced techniques and tools to process large volumes of uncertain data. For that purpose we introduce cardinality constraints as a principled tool to control the occurrences of uncertain data. Uncertainty is modeled qualitatively by assigning to each object a degree of possibility by which the object occurs in an uncertain instance. Cardinality constraints are assigned a degree of certainty that stipulates on which objects they hold. Our framework empowers users to model uncertainty in an intuitive way, without the requirement to put a precise value on it. Our class of cardinality constraints enjoys a natural possible world semantics, which is exploited to establish several tools to reason about them. We characterize the associated implication problem axiomatically and algorithmically in linear input time. Furthermore, we show how to visualize any given set of our cardinality constraints in the form of an Armstrong sketch. Even though the problem of finding an Armstrong sketch is precisely exponential, our algorithm computes a sketch with conservative use of time and space. data engineers may therefore compute Armstrong sketches that they can jointly inspect with domain experts in order to consolidate the set of cardinality constraints meaningful for a given application domain. (C) 2015 Elsevier B.V. All rights reserved.

关键词： data and knowledge visualization data models database semantics Management of integrity constraints Requirements engineering

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Mapping and Visualizing Deep-Learning Urban Beautification

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2018年第5期38卷 70-83页

作者： Kauer, Tobias Joglekar, Sagar Redi, Miriam Aiello, Luca Maria Quercia, Daniele Univ Appl Sci Potsdam Potsdam Germany Kings Coll London Dept Informat London England Nokia Bell Labs Cambridge Cambridge England Nokia Bell Labs Cambridge Social Dynam Cambridge England

Information visualization has great potential to make sense of the increasing amount of data generated by complex machine-learning algorithms. We design a set of visualizations for a new deep-learning algorithm called FaceLift (***/facelift). This algorithm is able to generate a beautified version of a given urban image (such as from Google Street View), and our visualizations compare pairs of original and beautified images. With those visualizations, we aim at helping practitioners understand what happened during the algorithmic beautification without requiring them to be machine-learning experts. We evaluate the effectiveness of our visualizations to do just that with a survey among practitioners. From the survey results, we derive general design guidelines on how information visualization makes complex machine-learning algorithms more understandable to a general audience.

关键词： computer graphics data and knowledge visualization deep learning information visualization urban informatics

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A Novel visualization Technique for Electric Power Grid Analytics

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2009年第3期15卷 410-423页

作者： Wong, Pak Chung Schneider, Kevin Mackey, Patrick Foote, Harlan Chin, George, Jr. Guttromson, Ross Thomas, Jim Pacific NW Natl Lab Richland WA 99352 USA

The application of information visualization holds tremendous promise for the electric power industry, but its potential has so far not been sufficiently exploited by the visualization community. Prior work on visualizing electric power systems has been limited to depicting raw or processed information on top of a geographic layout. Little effort has been devoted to visualizing the physics of the power grids, which ultimately determines the condition and stability of the electricity infrastructure. Based on this assessment, we developed a novel visualization system prototype, GreenGrid, to explore the planning and monitoring of the North American Electricity Infrastructure. This paper discusses the rationale underlying the GreenGrid design, describes its implementation and performance details, and assesses its strengths and weaknesses against the current geographic-based power grid visualization. We also present a case study using GreenGrid to analyze the information collected moments before the last major electric blackout in the Western United States and Canada and a usability study to evaluate the practical significance of our design in simulated real life situations. Our result indicates that many of the disturbance characteristics can be readily identified with the proper form of visualization.

关键词： data and knowledge visualization information visualization visualization techniques and methodologies applications

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Interpretation of Structural Preservation in Low-Dimensional Embeddings

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IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2022年第5期34卷 2227-2240页

作者： Ghosh, Aindrila Nashaat, Mona Miller, James Quader, Shaikh Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada IBM Toronto Software Lab Toronto ON Canada

Despite being commonly used in big-data analytics;the outcome of dimensionality reduction remains a black-box to most of its users. Understanding the quality of a low-dimensional embedding is important as not only it enables trust in the transformed data, but it can also help to select the most appropriate dimensionality reduction algorithm in a given scenario. As existing research primarily focuses on the visual exploration of embeddings, there is still a need for enhancing interpretability of such algorithms. To bridge this gap, we propose two novel interactive explanation techniques for low-dimensional embeddings obtained from any dimensionality reduction algorithm. The first technique LAPS produces a local approximation of the neighborhood structure to generate interpretable explanations on the preserved locality for a single instance. The second method GAPS explains the retained global structure of a high-dimensional dataset in its embedding, by combining non-redundant local-approximations from a coarse discretization of the projection space. We demonstrate the applicability of the proposed techniques using 16 real-life tabular, text, image, and audio datasets. Our extensive experimental evaluation shows the utility of the proposed techniques in interpreting the quality of low-dimensional embeddings, as well as with selecting the most suitable dimensionality reduction algorithm for any given dataset.

关键词： Dimensionality reduction visualization Approximation algorithms Optimization Manifolds Bridges data visualization Interactive data exploration and discovery algorithms for data and knowledge management data and knowledge visualization

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Visual Role Mining: A Picture Is Worth a Thousand Roles

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IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2012年第6期24卷 1120-1133页

作者： Colantonio, Alessandro Di Pietro, Roberto Ocello, Alberto Verde, Nino Vincenzo Bay31 GmbH CH-6300 Zug Switzerland Univ Roma Tre I-00146 Rome Italy Engiweb Secur I-00185 Rome Italy

This paper offers a new role engineering approach to Role-Based Access Control (RBAC), referred to as visual role mining. The key idea is to graphically represent user-permission assignments to enable quick analysis and elicitation of meaningful roles. First, we formally define the problem by introducing a metric for the quality of the visualization. Then, we prove that finding the best representation according to the defined metric is a NP-hard problem. In turn, we propose two algorithms: ADVISER and EXTRACT. The former is a heuristic used to best represent the user-permission assignments of a given set of roles. The latter is a fast probabilistic algorithm that, when used in conjunction with ADVISER, allows for a visual elicitation of roles even in absence of predefined roles. Besides being rooted in sound theory, our proposal is supported by extensive simulations run over real data. Results confirm the quality of the proposal and demonstrate its viability in supporting role engineering decisions.

关键词： Access controls data and knowledge visualization mining methods and algorithms

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Design and evaluation of a semantic enrichment process for bibliographic databases

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data & knowledge ENGINEERING 2013年 88卷 94-107页

作者： Lacasta, Javier Nogueras-Iso, Javier Falquet, Gilles Teller, Jacques Javier Zarazaga-Soria, F. Univ Zaragoza Comp Sci & Syst Engn Dept E-50009 Zaragoza Spain Univ Geneva Ctr Univ Informat CH-1211 Geneva 4 Switzerland Univ Liege LEMA B-4000 Liege Belgium

The limited semantics of thesauri and similar knowledge models hinder the searching and browsing possibilities of the bibliographic databases classified with this type of resource. This work proposes an automatic process to convert a knowledge model into a domain ontology through the alignment with DOLCE, an upper level ontology. This process is facilitated by an intermediary alignment with WordNet, a lexical model. The process has been tested with the thesauri and bibliographic databases of Urbamet and the European Urban knowledge Network. The Urbamet model has been used to create an atlas of urban related resources with advanced search capabilities. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Ontologies Digital libraries Semantic Web data and knowledge visualization

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BD2K Training Coordinating Center's ERuDIte: The Educational Resource Discovery Index for data Science

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2021年第1期9卷 316-328页

作者： Ambite, Jose Luis Fierro, Lily Gordon, Jonathan Burns, Gully A. P. C. Geigl, Florian Lerman, Kristina Van Horn, John D. Univ Southern Calif Informat Sci Inst ISI Marina Del Rey CA 90292 USA IBS Austria A-8833 Teufenbach Austria Vassar Coll Dept Comp Sci Poughkeepsie NY 12604 USA Univ Southern Calif Stevens Neuroimaging & Informat Inst Los Angeles CA 90033 USA

data science is a field that has developed to enable efficient integration and analysis of increasingly large data sets in many domains. In particular, big data in genetics, neuroimaging, mobile health, and other subfields of biomedical science, promises new insights, but also poses challenges. To address these challenges, the National Institutes of Health launched the Big data to knowledge (BD2K) initiative, including a Training Coordinating Center (TCC) tasked with developing a resource for personalized data science training for biomedical researchers. The BD2K TCC web portal is powered by ERuDIte, the Educational Resource Discovery Index, which collects training resources for data science, including online courses, videos of tutorials and research talks, textbooks, and other web-based materials. While the availability of so many potential learning resources is exciting, they are highly heterogeneous in quality, difficulty, format, and topic, making the field intimidating to enter and difficult to navigate. Moreover, data science is rapidly evolving, so there is a constant influx of new materials and concepts. We leverage data science techniques to build ERuDIte itself, using data extraction, data integration, machine learning, information retrieval, and natural language processing to automatically collect, integrate, describe, and organize existing online resources for learning data science.

关键词： Videos data science Training Metadata YouTube Big data Standards Machine learning education database design modeling and management data and knowledge visualization ontology design

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Generating graphs for visual analytics through interactive sketching

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2006年第6期12卷 1386-1398页

作者： Wong, Pak Chung Foote, Harlan Mackey, Patrick Perrine, Ken Chin, George, Jr. Pacific NW Natl Lab Richland WA 99352 USA

We introduce an interactive graph generator, GreenSketch, designed to facilitate the creation of descriptive graphs required for different visual analytics tasks. The human-centric design approach of GreenSketch enables users to master the creation process without specific training or prior knowledge of graph model theory. The customized user interface encourages users to gain insight into the connection between the compact matrix representation and the topology of a graph layout when they sketch their graphs. Both the human-enforced and machine-generated randomnesses supported by GreenSketch provide the flexibility needed to address the uncertainty factor in many analytical tasks. This paper describes more than two dozen examples that cover a wide variety of graph creations from a single line of nodes to a real-life small-world network that describes a snapshot of telephone connections. While the discussion focuses mainly on the design of GreenSketch, we include a case study that applies the technology in a visual analytics environment and a usability study that evaluates the strengths and weaknesses of our design approach.

关键词： data and knowledge visualization information visualization visualization techniques and methodologies graphs and networks

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