TimeSpiral, an enhanced interactive visual system for time series data

Zhang, Di; Zhu, Liehuang; Wang, Chengcheng; Zhang, Lei

doi:10.1109/infoman.2016.7477546

Cited by 2 publications

(4 citation statements)

References 26 publications

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“…Similarly to the solution introduced by Liu et al, 18 we adopt a small multiples visualization and a consistent placement strategy to display groups of related variables. Moreover, we consider arbitrary time periods and temporal scales by means of user-defined observation periods and data aggregations, 20 which allow visual data explorations at multiple levels of detail.…”

Section: Exploratory Visualization Of Time-varying Multivariate Datamentioning

confidence: 99%

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

Soriano-Vargas

Hamann

Oliveira

2019

Information Visualization

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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.

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Section: Exploratory Visualization Of Time-varying Multivariate Datamentioning

confidence: 99%

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

Soriano-Vargas

Hamann

Oliveira

2019

Information Visualization

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“…It has been applied to univariate data from three different domains, namely stock market trends, census demographics and climate modeling, in order to assess changes over the years and identify common trends and similar years. TimeSpiral [26] is a visualization system combining multiple views to assist users in analyzing and exploring periodic trends and correlations in multivariate time-series data. It supports data aggregation at multiple levels, e.g., changing a time interval, time span or time granularity.…”

Section: Visualization Of Time-varying Multivariate Datamentioning

confidence: 99%

“…We also present a domain-specific solution for scintillation data that includes several strategies available in existing systems. For example, similarly to known solutions [25,30] we adopt a small multiples visualization and a consistent placement strategy to display groups of related variables; and we consider arbitrary time periods of different granularity levels by means of user-defined data aggregations [26] . Our solution, however, supports the simultaneous investigation of many variables and satisfies specific requirements of scintillation experts.…”

Section: Visualization Of Time-varying Multivariate Datamentioning

confidence: 99%

“…The affected satellites at a particular GPS station will change according to the spatial dynamics of both their fixed orbital periods and the ionospheric irregularities. Time matrix views of four satellites (4,8,10,26) depicting over 28% missing data are highlighted (cyan borders). Occurrence of missing data may be related to scintillation susceptibility of a certain satellite because strong scintillation may cause loss of lock and, as a consequence, inability to track some variables.…”

Section: Comparing the Behavior Of Observations From Multiple Satellimentioning

confidence: 99%

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Visual analytics of time-varying multivariate ionospheric scintillation data

Soriano-Vargas

Vani

Shimabukuro

et al. 2017

Computers & Graphics

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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.

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TimeSpiral, an enhanced interactive visual system for time series data

Cited by 2 publications

References 26 publications

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

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

Visual analytics of time-varying multivariate ionospheric scintillation data

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