TimeBench: A Data Model and Software Library for Visual Analytics of Time-Oriented Data

Rind, Alexander; Lammarsch, Tim; Aigner, Wolfgang; Alsallakh, Bilal; Miksch, Silvia

doi:10.1109/tvcg.2013.206

Cited by 23 publications

(14 citation statements)

References 33 publications

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“…There have been several visual analytics systems developed in recent years that support data analysis and exploration processes, and provide extensive data modeling and hypothesis generation tools (e.g., [28,35]). More recently however, researchers have also started progressing toward creating visual analytics systems that incorporate predictive analytics in them.…”

Section: Predictive Visual Analyticsmentioning

confidence: 99%

Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement

Malik

Maciejewski

Towers

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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Abstract-In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets.

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Section: Predictive Visual Analyticsmentioning

confidence: 99%

Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement

Malik

Maciejewski

Towers

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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“…It is also a space-efficient representation. The Horizon graph is not commonly known by laypeople and is sparsely used in the visualization community [7,[10][11][12][13][14][15], however, this representation is also space-efficient for presenting large data. This gives a range from "familiar" to "unfamiliar" visual representation to be evaluated.…”

Section: Introductionmentioning

confidence: 99%

A Study on Visual Representations for Active Plant Wall Data Analysis

Hassan

Liu

Besançon

et al. 2019

Data

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The indoor climate is closely related to human health, well-being, and comfort. Thus, an understanding of the indoor climate is vital. One way to improve the indoor climates is to place an aesthetically pleasing active plant wall in the environment. By collecting data using sensors placed in and around the plant wall both the indoor climate and the status of the plant wall can be monitored and analyzed. This manuscript presents a user study with domain experts in this field with a focus on the representation of such data. The experts explored this data with a Line graph, a Horizon graph, and a Stacked area graph to better understand the status of the active plant wall and the indoor climate. Qualitative measures were collected with Think-aloud protocol and semi-structured interviews. The study resulted in four categories of analysis tasks: Overview, Detail, Perception, and Complexity. The Line graph was found to be preferred for use in providing an overview, and the Horizon graph for detailed analysis, revealing patterns and showing discernible trends, while the Stacked area graph was generally not preferred. Based on these findings, directions for future research are discussed and formulated. The results and future directions of this research can facilitate the analysis of multivariate temporal data, both for domain users and visualization researchers.

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“…However, the subjective quality evaluation method is time consuming, labor intensive and difficult to be inte-grated into the teaching processing system. Therefore, the design of objective teaching quality evaluation method [2], so that the quality of teaching can be evaluated by computer automatically and accurately is of great significance in the field of teaching processing.…”

Section: Introductionmentioning

confidence: 99%

“…The existing objective teaching quality evaluation methods can be divided into two categories: the reference teaching quality evaluation method [2][3] and the physical education multimedia teaching evaluation method [4]. Between them, the former requires all or part of the initial teaching information to evaluate and test the teaching quality by comparing the difference between the test teaching and the initial teaching information.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Physical Education Multimedia Teaching for Data Assimilation

Lifu¹

2018

Int. J. Onl. Eng.

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Abstract-The evaluation of physical education (PE) multimedia teaching refers to the prediction of physical education multimedia teaching quality in the absence of initial multimedia teaching information. Therefore, the evaluation method of PE multimedia teaching based on unsupervised feature learning has achieved good performance. But, its quality prediction accuracy decreases significantly with the reduction of the feature dimension. In order to overcome this defect, the author combines the active learning strategy with the unsupervised feature learning and proposes a kind of data assimilation framework to improve the discriminability of the representation of teaching features. The results show that the proposed method can enhance the accuracy of teaching quality prediction by 8%. Experiments show that, when feature dimension is relatively low, the proposed method can improve the teaching quality prediction accuracy by 8% compared with the method based on unsupervised feature learning. At the same time, the performance of the proposed method is superior to that of the other physical education multimedia teaching evaluation methods at present.Keywords-active learning; dictionary learning; feature learning; teaching presentation; teaching quality evaluation IntroductionWith the popularization of teaching acquisition equipment such as mobile phones and cameras and the rapid development of social networks, teaching plays an increasingly important role in our daily life. However, due to the limitation of the performance of the teaching processing system, various kinds of distortion will be introduced in the process of teaching access, transmission and processing. The introduction of distortion will reduce the aesthetics of teaching, which, at the same time, will impeding people from obtaining information from the teaching. Therefore, it is necessary to study the teaching quality evaluation methods, so as to apply them to the monitoring of the teaching acquisition equipment performance, the optimal selection of teaching processing system and the optimization of teaching methods.As people are the ultimate recipients of teaching, the subjective evaluation of the quality of teaching by people is the most reliable method [1]. However, the subjective quality evaluation method is time consuming, labor intensive and difficult to be inte- 30

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TimeBench: A Data Model and Software Library for Visual Analytics of Time-Oriented Data

Cited by 23 publications

References 33 publications

Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement

Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement

A Study on Visual Representations for Active Plant Wall Data Analysis

Evaluation of Physical Education Multimedia Teaching for Data Assimilation

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