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TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

INFORMATION VISUALIZATION 2020年第1期19卷 3-23页

作者： Soriano-Vargas, Aurea Hamann, Bernd de Oliveira, Maria Cristina F. Univ Estadual Campinas Inst Comp BR-13083872 Campinas SP Brazil Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Univ Sao Paulo Inst Math & Comp Sci Sao Carlos SP Brazil

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.

关键词： Visual analytics time-varying multivariate data visual feature selection data visualization data analytics

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Visual exploration to support the identification of relevant attributes in time-varying multivariate data

Visual exploration to support the identification of relevant...

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作者： Aurea Rossy Soriano Vargas UNIVERSIDADE DE SAO PAULO

学位级别：博士

Ionospheric scintillation is a rapid variation in the amplitude and/or phase of radio signals traveling through the ionosphere. This spatial and time-varying phenomenon is of interest because its occurrence may affect the reception quality of satellite signals. Specialized receivers at strategic regions can track multiple variables related to the phenomenon, generating a database of historical observations on the regional behavior of ionospheric scintillation. The analysis of such data is very challenging, since it consists of time-varying measurements of many variables which are heterogeneous in nature and with possibly many missing values, recorded over extensive time periods. There is a need to introduce alternative intuitive strategies that contribute to experts acquiring further knowledge from the ionospheric scintillation data. Such challenges motivated a study on the applicability of visualization techniques to support tasks of identification of relevant attributes in the study of the behavior of phenomena described by multiple time-varying variables, of which the ionospheric scintillation is a good example. In particular, this thesis introduces a visual analytics framework, named TV-MV Analytics, that supports exploratory tasks on time-varying multivariate data and was developed following the requirements of experts on ionospheric scintillation from the Faculty of Science and Technology of UNESP at Presidente Prudente, Brazil. TV-MV Analytics provides an interactive visual explo- ration loop to analysts inspecting the behavior of multiple variables at different temporal scales, through temporal representations associated with clustering and multidimensional projection techniques. Analysts can also assess how different feature sub-spaces contribute to character- izing a certain behavior, where they may direct the analysis process and include their domain knowledge in the exploratory analysis. We also illustrate the application of TV-MV Analytics on multivaria

关键词： Visual Analytics time-varying multivariate data Visual Feature Selection Feature Space Analysis Exploratory data Visualization

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Visual analytics of time-varying multivariate ionospheric scintillation data

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COMPUTERS & GRAPHICS-UK 2017年 68卷 96-107页

作者： Soriano-Vargas, Aurea Vani, Bruno C. Shimabukuro, Milton H. Monico, Joao F. G. Oliveira, Maria Cristina F. Hamann, Bernd Univ Sao Paulo ICMC BR-13566590 Sao Carlos SP Brazil Sao Paulo State Univ UNESP FCT BR-19060900 Presidente Prudente SP Brazil Univ Calif Davis Dept Comp Sci Davis CA 95616 USA

We present a clustering-based interactive approach to multivariate data analysis, motivated by the specific needs of scintillation data. Ionospheric scintillation is a rapid variation in the amplitude and/or phase of radio signals traveling through the ionosphere. This spatial and time-varying phenomenon is of great interest since it affects the reception quality of satellite signals. Specialized receivers at strategic regions can track multiple variables related to this phenomenon, generating a database of observations of regional ionospheric scintillation. We introduce a visual analytics solution to support analysis of such data, keeping in mind the general applicability of our approach to similar multivariate data analysis situations. Taking into account typical user questions, we combine visualization and data mining algorithms that satisfy these goals: (i) derive a representation of the variables monitored that conveys their behavior in detail, at multiple user-defined aggregation levels;(ii) provide overviews of multiple variables regarding their behavioral similarity over selected time periods;(iii) support users when identifying representative variables for characterizing scintillation behavior. We illustrate the capabilities of our proposed framework by presenting case studies driven directly by questions formulated by collaborating domain experts. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Visual analytics time-varying multivariate data Visual feature selection data visualization Ionospheric scintillation

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A Synthetic Visual Analysis System for time-varying Network

A Synthetic Visual Analysis System for Time-Varying Network

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IEEE International Conference on Computer and Communication Engineering Technology (CCET)

作者： Huang, Jian Zhang, Xiushan Naval Univ Engn Dept Comp Sci Wuhan Hubei Peoples R China

ISBN: (纸本)9781538674376

In recent years, with the rapid development of computer science and information technology, the world is filled and propelled of time-varying and multivariate data. The enormous growth of data in the last decades led to big data challenge in the network security field. Traditional visual analysis method for the time-varying network is inadequate. Efficient methods for visual clutter reduction, network structure exploration and network behavior detection are needed. In this paper, we propose two methods: A triangular based algorithm for time-varying behavior presentation and an improved brush PCP aims to assist the visual analysis task in time-varying network pattern detection. A synthetic visual analysis system is designed and implemented on the basis of these two novel methods. Our system is capable of visually analyzing vast amounts of network data. To better describe and demonstrate the usefulness and performance of our system, we utilize the ChinaVis2015 Challenge dataset as a case study.

关键词： component visual analysis time-varying multivariate data network security threat pattern recognition

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