VisInfo: a digital library system for time series research data based on exploratory search—a user-centered design approach

Bernard, Jürgen; Daberkow, Debora; Fellner, Dieter W.; Fischer, Katrin; Koepler, Oliver; Kohlhammer, Jörn; Runnwerth, Mila; Ruppert, Tobias; Schreck, Tobias; Sens, Irina

doi:10.1007/s00799-014-0134-y

Cited by 28 publications

(17 citation statements)

References 55 publications

(75 reference statements)

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“…For being useful in a web‐based data visualization context an effective data reduction strategy is necessary. This usage scenario reproduces a real‐world use case that was conducted 2013 [BDF∗15], without having VA support. Overall, the creation, validation, and optimization of the PP had taken about two years.…”

Section: Usage Scenariosmentioning

confidence: 99%

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Bernard

Hutter

Reinemuth

et al. 2019

Computer Graphics Forum

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Pre‐processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre‐processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre‐processing pipelines, human‐in‐the‐loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in‐depth research in visual analytics. We present a visual‐interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre‐processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty‐aware pre‐processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre‐processing in general and for uncertainty‐aware pre‐processing of multivariate time series in particular.

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Section: Usage Scenariosmentioning

confidence: 99%

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Bernard

Hutter

Reinemuth

et al. 2019

Computer Graphics Forum

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“…Also, the widely-used line chart can be regarded to define trajectories. In [7], query-by-example and queryby-sketch search are proposed for exploration of patterns in time series databases.…”

Section: Interactive Trajectory Search For Sport Analysismentioning

confidence: 99%

Tackling Similarity Search for Soccer Match Analysis: Multimodal Distance Measure and Interactive Query Definition

Stein

Janetzko

Schreck

et al. 2019

IEEE Comput. Grap. Appl.

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Figure 1: Soccer movement trajectories are complex data. Existing trajectory similarity measures are typically based on spatiotemporal features, but lacking support for richer context. The trajectory on the left illustrates a trajectory of a soccer move consisting exclusively of x-and y-coordinates of the ball. The annotated trajectory on the right reveals the crucial movement context. Essential context data are, among others, the movement of the involved players of the ball possessing team as well as the movement of the players of the opposing team. Trajectory event data provide additional context information. We here study how to jointly take into account these data perspectives for similarity search as an important basis for soccer data exploration.

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“…Bernard et al . [BDF*15] apply user‐centred design to create a system for visual search and exploration in a large set of time series in which abstraction in the data space (by means of time series clustering) is applied to facilitate the exploration.…”

Section: Related Workmentioning

confidence: 99%

Data Abstraction for Visualizing Large Time Series

Shurkhovetskyy

Andrienko

Fuchs

2017

Computer Graphics Forum

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Numeric time series is a class of data consisting of chronologically ordered observations represented by numeric values. Much of the data in various domains, such as financial, medical and scientific, are represented in the form of time series. To cope with the increasing sizes of datasets, numerous approaches for abstracting large temporal data are developed in the area of data mining. Many of them proved to be useful for time series visualization. However, despite the existence of numerous surveys on time series mining and visualization, there is no comprehensive classification of the existing methods based on the needs of visualization designers. We propose a classification framework that defines essential criteria for selecting an abstraction method with an eye to subsequent visualization and support of users' analysis tasks. We show that approaches developed in the data mining field are capable of creating representations that are useful for visualizing time series data. We evaluate these methods in terms of the defined criteria and provide a summary table that can be easily used for selecting suitable abstraction methods depending on data properties, desirable form of representation, behaviour features to be studied, required accuracy and level of detail, and the necessity of efficient search and querying. We also indicate directions for possible extension of the proposed classification framework.

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VisInfo: a digital library system for time series research data based on exploratory search—a user-centered design approach

Cited by 28 publications

References 55 publications

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Tackling Similarity Search for Soccer Match Analysis: Multimodal Distance Measure and Interactive Query Definition

Data Abstraction for Visualizing Large Time Series

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