Visual Segmentation and Labeling of Multivariate Time Series

多元时间序列的视觉分割和标记

• 批准号: 310545869
• 负责人: Professor Dr.-Ing. Dieter W. Fellner
• 金额: --
• 依托单位: Institute of Information Systems Engineering (ISE)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/310545869?language=en
• 关键词: Visual; Segmentation; Labeling; Multivariate; Time

A highly relevant task in many domains is to find meaningful segments and labels in multivariate time series data to allow humans to generate hypotheses and draw conclusions, for instance to find events or activities in human motion data or electrocardiograph data. Going beyond current segmentation and labeling approaches, this project aims for an interconnected and visual-interactive approach to combine the algorithm selection for segmentation and labeling, the parametrization of these algorithms, and the visualization and exploration of diverse types of uncertainty about and in the results. Current approaches consider each of these problems separately. However, the tight interconnection of these aspects in our combined visual analytics approach will definitely lead to better results as well as to a deeper understanding of the data and the data generating process; in our case with regard to algorithm selection, parametrization, and involved uncertainty in the segmentation and labelling of multivariate time series.A joint system setup, shared sets of data, and task abstractions define a common ground and ensure collaboration throughout the project. We will investigate in the individual aspects with regard to the envisaged interconnections. (a) To open the black-box of algorithm selection, we provide visual analytics techniques to explore the selection of adequate segmentation and labeling algorithms, to steer these algorithms, and to guide the users to detect the most adequate algorithms for a particular data set. (b) We ease the parametrization of these algorithms by developing visual analytics techniques for a systematic analysis of the parameter space (many parameters and large value ranges). (c) For exploring and communicating diverse types of uncertainty, we facilitate appropriate visual encoding, develop visual analytics techniques to assess these types of uncertainty, and allow investigation of uncertainties of alternative algorithms and parametrizations (aggregated uncertainties as well as causes and effects). Such a novel strategy requires a comprehensive evaluation. We plan a horizontal as well as a vertical evaluation strategy. With the horizontal evaluation, we will test single visualization and interaction designs that will be developed during the project. With the vertical evaluation, we will provide a summative evaluation of our combined visual analytics approach.The project team of this German-Austrian collaboration is comprised of experts who have long-standing experience in the three research fields. The University of Rostock (lead Heidrun Schumann) has worked extensively in the field of parametrization; the TU Wien (lead Silvia Miksch) includes experts for time series analysis and uncertainty visualization; and the TU Darmstadt (lead Dieter Fellner) has created several approaches for the data-driven selection of algorithms and the analysis of large sets of time series.

许多领域中一项高度相关的任务是在多元时间序列数据中找到有意义的片段和标签，以允许人类生成假设并得出结论，例如在人体运动数据或心电图数据中查找事件或活动。超越当前的分割和标记方法，该项目旨在采用一种互连和视觉交互的方法，将分割和标记的算法选择、这些算法的参数化以及结果中各种类型的不确定性的可视化和探索结合起来。当前的方法分别考虑每个问题。然而，在我们的组合可视化分析方法中，这些方面的紧密互连肯定会带来更好的结果，并加深对数据和数据生成过程的理解；在我们的案例中，涉及算法选择、参数化以及多元时间序列分割和标记中涉及的不确定性。联合系统设置、共享数据集和任务抽象定义了共同点并确保整个项目的协作。我们将对设想的互连的各个方面进行调查。 (a) 为了打开算法选择的黑匣子，我们提供可视化分析技术来探索适当的分割和标记算法的选择，引导这些算法，并指导用户检测针对特定数据集的最适当的算法。 （b）我们通过开发可视化分析技术来对参数空间（许多参数和大值范围）进行系统分析，从而简化这些算法的参数化。 (c) 为了探索和交流不同类型的不确定性，我们促进适当的视觉编码，开发视觉分析技术来评估这些类型的不确定性，并允许调查替代算法和参数化的不确定性（汇总的不确定性以及原因和影响）。如此新颖的策略需要进行全面评估。我们计划横向和纵向评估策略。通过横向评估，我们将测试项目期间开发的单一可视化和交互设计。通过垂直评估，我们将对我们的组合可视化分析方法进行总结性评估。这个德国-奥地利合作的项目团队由在这三个研究领域拥有长期经验的专家组成。罗斯托克大学（领导者 Heidrun Schumann）在参数化领域进行了广泛的研究； TU Wien（领导者 Silvia Miksch）包括时间序列分析和不确定性可视化方面的专家；达姆施塔特工业大学（Dieter Fellner 领导）创建了多种数据驱动算法选择和大型时间序列分析的方法。

项目成果

Quantifying Uncertainty in Multivariate Time Series Pre-Processing

• DOI: 10.2312/eurova.20191121
• 发表时间: 2019
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: C. Bors;J. Bernard;M. Bögl;T. Gschwandtner;J. Kohlhammer;S. Miksch

Sketching Temporal Uncertainty - An Exploratory User Study

描绘时间不确定性 - 一项探索性用户研究

• DOI: 10.2312/eurovisshort.20181080
• 发表时间: 2018
• 作者: Schwarzinger;Roschal;Gschwandtner
• 通讯作者: Gschwandtner

Interactive Lenses for Visualization: An Extended Survey

• DOI: 10.1111/cgf.12871
• 发表时间: 2017-09
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: C. Tominski;Stefan Gladisch;Ulrike Kister;Raimund Dachselt;H. Schumann

Visual-Interactive Semi-Supervised Labeling of Human Motion Capture Data

• DOI: 10.2352/issn.2470-1173.2017.1.vda-387
• 发表时间: 2017-01
• 作者: J. Bernard;Eduard Dobermann;Anna Vögele;Björn Krüger;J. Kohlhammer;D. Fellner

Making Parameter Dependencies of Time‐Series Segmentation Visually Understandable

使时间序列分割的参数依赖性直观易懂

• DOI: 10.1111/cgf.13894
• 发表时间: 2020
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: Eichner;Schumann;Tominski
• 通讯作者: Tominski