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data and knowledge visualization with virtual reality spaces, neural networks and rough sets: Application to cancer and geophysical prospecting data

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第18期39卷 13193-13201页

作者： Valdes, Julio J. Romero, Enrique Barton, Alan J. Univ Politecn Cataluna Dept Llenguatges & Sistemes Informat E-08028 Barcelona Spain

Visual data mining with virtual reality spaces is used for the representation of data and symbolic knowledge. High quality structure-preserving and maximally discriminative visual representations can be obtained using a combination of neural networks (SAMANN and NDA) and rough sets techniques, so that a proper subsequent analysis can be made. The approach is illustrated with two types of data: for gene expression cancer data, an improvement in classification performance with respect to the original spaces was obtained;for geophysical prospecting data for cave detection, a cavity was successfully predicted. Crown (C) 2012 and Elsevier Ltd. All rights reserved.

关键词： data and knowledge visualization Visual data mining Virtual reality data projection Neural networks Rough sets

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visualization SUPPORT FOR USER-CENTERED MODEL SELECTION IN knowledge DISCOVERY AND data MINING

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International Journal on Artificial Intelligence Tools 2001年第4期10卷 691-713页

作者： TUBAO HO TRONGDUNG NGUYEN DUCDUNG NGUYEN SAORI KAWASAKI Japan Advanced Institute of Science and Technology Tatsunokuchi Ishikawa 923-1292 Japan

The problem of model selection in knowledge discovery and data mining—the selection of appropriate discovered patterns/models or algorithms to achieve such patterns/models—is generally a difficult task for the user as it requires meta-knowledge on algorithms/models and model performance metrics. Viewing knowledge discovery as a human-centered process that requires an effective collaboration between the user and the discovery system, our work aims to make model selection in knowledge discovery easier and more effective. For such a collaboration, our solution is to give the user the ability to try easily various alternatives and to compare competing models quantitatively and qualitatively. The basic idea of our solution is to integrate data and knowledge visualization with the knowledge discovery process in order to the support the participation of the user. We introduce the knowledge discovery system D2MS in which several visualization techniques of data and knowledge are developed and integrated into the steps of the knowledge discovery process. The visualizers in D2MS greatly help the user gain better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge in the whole process.

关键词： Model selection knowledge discovery process data and knowledge visualization the user's participation

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Automated Hierarchical Density Shaving: A Robust Automated Clustering and visualization Framework for Large Biological data Sets

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2010年第2期7卷 223-237页

作者： Gupta, Gunjan Liu, Alexander Ghosh, Joydeep Amazon Com Seattle WA 98114 USA Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

A key application of clustering data obtained from sources such as microarrays, protein mass spectroscopy, and phylogenetic profiles is the detection of functionally related genes. Typically, only a small number of functionally related genes cluster into one or more groups, and the rest need to be ignored. For such situations, we present Automated Hierarchical Density Shaving (Auto-HDS), a framework that consists of a fast hierarchical density-based clustering algorithm and an unsupervised model selection strategy. Auto-HDS can automatically select clusters of different densities, present them in a compact hierarchy, and rank individual clusters using an innovative stability criteria. Our framework also provides a simple yet powerful 2D visualization of the hierarchy of clusters that is useful for further interactive exploration. We present results on Gasch and Lee microarray data sets to show the effectiveness of our methods. Additional results on other biological data are included in the supplemental material.

关键词： Mining methods and algorithms data and knowledge visualization clustering bioinformatics

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CoDe Modeling of Graph Composition for data Warehouse Report visualization

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IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2014年第3期26卷 563-576页

作者： Risi, Michele Sessa, Maria I. Tucci, Maurizio Tortora, Genoveffa Univ Salerno Dept Management & Informat Technol I-84084 Salerno Italy

The visualization of information contained in reports is an important aspect of human-computer interaction, for both the accuracy and the complexity of relationships between data must be preserved. A greater attention has been paid to individual report visualization through different types of standard graphs (Histograms, Pies, etc.). However, this kind of representation provides separate information items and gives no support to visualize their relationships which are extremely important for most decision processes. This paper presents a design methodology exploiting the visual language CoDe [1] based on a logic paradigm. CoDe allows to organize the visualization through the CoDe model which graphically represents relationships between information items and can be considered a conceptual map of the view. The proposed design methodology is composed of four phases: the CoDe Modeling and OLAP Operation pattern definition phases define the CoDe model and underlying metadata information, the OLAP Operation phase physically extracts data from a data warehouse and the Report visualization phase generates the final visualization. Moreover, a case study on real data is provided.

关键词： Information visualization cognitive systems OLAP reports data and knowledge visualization user interfaces

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visualization of Real-Time Heterogeneous Smart City data Using Virtual Reality 5

Visualization of Real-Time Heterogeneous Smart City Data Usi...

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5th IEEE Annual International Smart Cities Conference (ISC2)

作者： Vanden Broucke, Steven Deligiannis, Nikos Vrije Univ Brussel Pl Laan 2 B-1050 Brussels Belgium IMEC Kapeldreef 75 B-3001 Leuven Belgium

ISBN: (纸本)9781728108469

As the size and dimensionality of big heterogeneous data increases, finding patterns and anomalies with existing visualization methods and tools poses a significant challenge. The majority of open data platforms that offer smart city data visualizations use browser-based two-dimensional (2D) visualizations as 2D displays are widely adopted. These displays are however ineffective in depicting multi-dimensional heterogeneous data. The recent growth in the virtual reality (VR) consumer market resulted in an affordable alternative for 3D visualizations. In this paper, we propose a VR system capable of visualizing real-time smart city data concerning the city of Brussels. A subset of external data sources that is already visualized in existing web platforms is incorporated in the VR application. A user study is conducted to assess perceived workloads and data immersion parameters for a set of data exploration tasks in three existing web platforms and in the proposed VR system. Results indicate significantly lower levels of perceived frustration and significantly higher levels of data intuitivity, immersion, overview in data, and intuitive interaction. However, no significant difference in total perceived workload is observed.

关键词： data and knowledge visualization visualization Applications Big data Human-computer interaction Artificial augmented, and virtual realities

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Interactive exploration of data traffic with hierarchical network maps

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2006年第6期12卷 1440-1449页

作者： Mansmann, Florian Vinnik, Svetlana Univ Konstanz Dept Comp & Informat Sci D-78457 Constance Germany

Network communication has become indispensable in business, education, and government. With the pervasive role of the Internet as a means of sharing information across networks, its misuse for destructive purposes, such as spreading malicious code, compromising remote hosts, or damaging data through unauthorized access, has grown immensely in the recent years. The classical way of monitoring the operation of large network systems is by analyzing the system logs for detecting anomalies. In this work, we introduce Hierarchical Network Map, an interactive visualization technique for gaining a deeper insight into network flow behavior by means of user-driven visual exploration. Our approach is meant as an enhancement to conventional analysis methods based on statistics or machine learning. We use multidimensional modeling combined with position and display awareness to view source and target data of the hosts in a hierarchical fashion with the ability to interactively change the level of aggregation or apply filtering. The interdisciplinary approach integrating data warehouse technology, information visualization, and decision support, brings about the benefit of efficiently collecting the input data and aggregating over very large data sets, visualizing the results, and providing interactivity to facilitate analytical reasoning.

关键词： data and knowledge visualization information visualization visual analytics network security

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Computing Consistency Between Microarray data and Known Gene Regulation Relationships

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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE 2009年第6期13卷 1075-1082页

作者： Shin, Dong-Guk Kazmi, Saira A. Pei, Baikang Kim, Yoo-Ah Maddox, Jeffrey Nori, Ravi Wong, Alan Krueger, Winfried Rowe, David Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA Univ Connecticut Ctr Hlth Dept Dev Biol Farmington CT 06030 USA

Microarray experiments produce expression patterns for thousands of genes at once. On the other hand, biomedical literature contains large amounts of gene regulation relationship information accumulated over the years. One obvious requirement is an automated way of comparing microarray data with the collection of known gene regulation relationships. Such an automated comparison is imperative because it can help biologists rapidly understand the context of a given microarray experiment. In addition, the consistency measure can be used to either validate or refute the hypothesis being tested using the microarray experiment. In this paper we present a systematic way of examining the consistency between a given set of microarray data and known gene regulation relationships. We first introduce a simple gene regulation network model with two separate algorithms designed to isolate a maximally consistent network. Subsequently, we extend the model to take into account multiple regulating factors for a single gene while highlighting both consistencies and inconsistencies. We illustrate the effectiveness of our approach with two practical examples, one that picks the peroxisome proliferator-activated receptor (PPAR) pathway as highly consistent from multiple pathways of Kyoto encyclopedia of genes and genomes (KEGG), and another that isolates key regulatory relationships involving nfkb1 and others known for macrophage's counter response to inflammation.

关键词： data and knowledge visualization heuristic methods knowledge reuse optimization

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Review of Research on Student-Facing Learning Analytics Dashboards and Educational Recommender Systems

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IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2017年第4期10卷 405-418页

作者： Bodily, Robert Verbert, Katrien Brigham Young Univ Instruct Psychol & Technol Dept 150 MCKB Provo UT 84604 USA Katholieke Univ Leuven Dept Comp Sci Human & Comp Interact Grp Celestijnenlaan 200A B-3001 Heverlee Belgium

This article is a comprehensive literature review of student-facing learning analytics reporting systems that track learning analytics data and report it directly to students. This literature review builds on four previously conducted literature reviews in similar domains. Out of the 945 articles retrieved from databases and journals, 93 articles were included in the analysis. Articles were coded based on the following five categories: functionality, data sources, design analysis, student perceptions, and measured effects. Based on this review, we need research on learning analytics reporting systems that targets the design and development process of reporting systems, not only the final products. This design and development process includes needs analyses, visual design analyses, information selection justifications, and student perception surveys. In addition, experiments to determine the effect of these systems on student behavior, achievement, and skills are needed to add to the small existing body of evidence. Furthermore, experimental studies should include usability tests and methodologies to examine student use of these systems, as these factors may affect experimental findings. Finally, observational study methods, such as propensity score matching, should be used to increase student access to these systems but still rigorously measure experimental effects.

关键词： data mining data and knowledge visualization self-assessment technologies homework support systems adaptive and intelligent educational systems literature review

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DIPKIP: A CONNECTIONIST knowledge MANAGEMENT SYSTEM TO IDENTIFY knowledge DEFICITS IN PRACTICAL CASES

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COMPUTATIONAL INTELLIGENCE 2010年第1期26卷 26-56页

作者： Herrero, Alvaro Corchado, Emilio Saiz, Lourdes Abraham, Ajith Univ Burgos Dept Civil Engn Burgos 09006 Spain Univ Salamanca Dept Informat & Automat E-37008 Salamanca Spain Norwegian Univ Sci & Technol Ctr Quantifiable Qual Serv Commun Syst N-7034 Trondheim Norway

This study presents a novel, multidisciplinary research project entitled DIPKIP (data acquisition, intelligent processing, knowledge identification and proposal), which is a knowledge Management (KM) system that profiles the KM status of a company. Qualitative data is fed into the system that allows it not only to assess the KM situation in the company in a straightforward and intuitive manner, but also to propose corrective actions to improve that situation. DIPKIP is based on four separate steps. An initial "data Acquisition" step, in which key data is captured, is followed by an "Intelligent Processing" step, using neural projection architectures. Subsequently, the "knowledge Identification" step catalogues the company into three categories, which define a set of possible theoretical strategic knowledge situations: knowledge deficit, partial knowledge deficit, and no knowledge deficit. Finally, a "Proposal" step is performed, in which the "knowledge processes"-creation/acquisition, transference/distribution, and putting into practice/updating-are appraised to arrive at a coherent recommendation. The knowledge updating process (increasing the knowledge held and removing obsolete knowledge) is in itself a novel contribution. DIPKIP may be applied as a decision support system, which, under the supervision of a KM expert, can provide useful and practical proposals to senior management for the improvement of KM, leading to flexibility, cost savings, and greater competitiveness. The research also analyses the future for powerful neural projection models in the emerging field of KM by reviewing a variety of robust unsupervised projection architectures, all of which are used to visualize the intrinsic structure of high-dimensional data sets. The main projection architecture in this research, known as Cooperative Maximum-Likelihood Hebbian Learning (CMLHL), manages to capture a degree of KM topological ordering based on the application of cooperative lateral connections.

关键词： data and knowledge visualization connectionism and neural nets knowledge-based systems knowledge management applications discovery-based science

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What Do We Mean When We Say "Insight"? A Formal Synthesis of Existing Theory

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第9期30卷 6075-6088页

作者： Battle, Leilani Ottley, Alvitta Univ Washington Seattle WA 98195 USA Washington Univ St Louis St Louis MO 63130 USA

Researchers have derived many theoretical models for specifying users' insights as they interact with a visualization system. These representations are essential for understanding the insight discovery process, such as when inferring user interaction patterns that lead to insight or assessing the rigor of reported insights. However, theoretical models can be difficult to apply to existing tools and user studies, often due to discrepancies in how insight and its constituent parts are defined. This article calls attention to the consistent structures that recur across the visualization literature and describes how they connect multiple theoretical representations of insight. We synthesize a unified formalism for insights using these structures, enabling a wider audience of researchers and developers to adopt the corresponding models. Through a series of theoretical case studies, we use our formalism to compare and contrast existing theories, revealing interesting research challenges in reasoning about a user's domain knowledge and leveraging synergistic approaches in data mining and data management research.

关键词： data models data mining data and knowledge visualization visualization techniques and methodologies

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