Visual‐Interactive Preprocessing of Multivariate Time Series Data

Bernard, Jürgen; Hutter, Marco; Reinemuth, Heiko; Pfeifer, Hendrik; Bors, Christian; Kohlhammer, Jörn

doi:10.1111/cgf.13698

Cited by 38 publications

(44 citation statements)

References 59 publications

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“…While these users are experts in their own domains, they usually have little expertise in data-processing and visualisation, and may be called laymen in this regard. This lack of skills leads to a common pattern where the analytical task is shared by two user roles [28]: In a first step, the domain expert but non data-processing expert (e.g. a humanities scholar or teacher) defines criteria for useful data and requirements for the analysis.…”

Section: Previous Workmentioning

confidence: 99%

Towards a visual guide for communicating uncertainty in Visual Analytics

Seipp

Gutiérrez

Ochôa

et al. 2019

Journal of Computer Languages

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This article presents a first step towards the definition of a visual guide for communicating uncertainty which is to fit into existing visualisation frameworks and toolkits. The first entry in our guide is made by a set of visual variables appropriate for representing areal uncertainty in algorithm mechanics. Such visualisations show users how data points are distributed in the classification space and allow them to understand the "goodness-of-fit" of their data to the algorithm. This is important for Visual Analytics applications, which combine Information Visualisation with information mining techniques in an interactive decision-making process. Model uncertainties stemming from widely spread data points need to be visualised so that the user can make adjustments and improve the analysis. To capitalise on established knowledge and meaning, we explore whether popular visual variables for representing areal uncertainty in the domain of geospatial visualisation may also be effective for representing uncertainty in the visualisation of the mechanics of K-means clustering and Linear Regression algorithms, as both use a spatial distribution of data points. In a study with 500 participants we find that overall the visual means opacity performs best, followed by texture, but that grid and blur may be unsuitable for quantifying uncertainty. The performance of contour lines appears to depend on the algorithm visualisation. Using this study, we extend the validity of a set of domain-specific findings from geospatial visualisation to the visualisation of algorithm mechanics and use these to form the first building blocks of a cross-disciplinary visual guide for representing uncertainty, laying promising foundations for future work.

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Section: Previous Workmentioning

confidence: 99%

Towards a visual guide for communicating uncertainty in Visual Analytics

Seipp

Gutiérrez

Ochôa

et al. 2019

Journal of Computer Languages

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show abstract

“…Methods and workflows for data preprocessing, in particular, resolving data quality issues are proposed in several works [KHP*11, BRG*12, Ber15]. Assuming that data quality issues have been previously resolved, the distinguishing characteristics of data that affect the choice of an abstraction method are dimensionality ( univariate or multivariate ) and whether the data are stationary or streaming .…”

Section: Framework For Characterizing Abstraction Methodsmentioning

confidence: 99%

“…Possible transformations of time series data are not limited to abstraction. It may be necessary to apply some transformations prior to abstraction to improve the quality of data and make them suitable for analysis [KHP*11, BRG*12]. Bernard et al .…”

Section: Related Workmentioning

confidence: 99%

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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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“…This type of data plays an important role in many domains, such as environmental monitoring, monitoring of natural phenomena, financial markets, population statistics and others (BERNARD et al, 2012). Data analysis in multiple domains require the ability to thoroughly explore the variables, with the objective of identifying patterns, analyzing their behavior (STEED et al, 2017) and making sense of long multivariate time series (BERNARD et al, 2016).…”

Section: Contextmentioning

confidence: 99%

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

Vargas¹

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This work is dedicated to my husband Oscar Cuadros Linares, to my father Raul Soriano Filinich, and to my mother, Dora Vargas Núñez de Soriano. Foremost, I wish to give my heartfelt thanks to a very special person, my husband Oscar Cuadros. Thanks for his unconditional love, his unflagging encouragement, patience, and continued support that made the culmination of this thesis possible. I greatly value his insightful comments, the stimulating discussions, that helped me to keep things in perspective, the sleepless nights we were working together before deadlines and for all the fun we have had on this journey. I would like to express my special appreciation and my sincere gratitude to my advisor Prof. Maria Cristina Ferreira de Oliveira. Thanks (i) for the continuous support and patience during all my Ph.D. study and research, especially in the most critical periods, (ii) for her helpful and priceless advice, and (iii) for her insightful comments. Also, I would like to thank Prof. Bernd Hamann for receiving me as a visiting research scholar at the University of California, Davis. Thanks for his guidance, for supporting my research ideas, and for his insightful discussions about the research in the last months. Both of them, Prof. Cristina and Prof. Bernd, were and remain my best role models for a scientist and mentor. ¡Muchas gracias! I would like to extend my sincerest gratitude to the people who mean a lot to me, my parents Raul Soriano Filinich and Dora Vargas Núñez de Soriano. Thanks for their unconditional love, their emotional support, for all of the sacrifices that you have made on my behalf. Also, I express my thanks to my sister Laura, my little heart Fabio and my grandaunt Marlene Núñez (Pipito). I would also like to dedicate this thesis to the memory of my grandmother Mamá Nelly Núñez. It was hard to say goodbye from afar. Even though the Alzheimer's demons attacked her, she treasured the memory of her granddaughter Aurea. Many times she talked with me about her granddaughter(me) with so much love, and joy because she(I) would visit her, not knowing that it was me. My mind and my heart treasure the great memories we had. Rest in peace. I would like to thank all my friends from Brazil, Spain, United States and Peru, especially the Reginas girls, the Pipocas girls, and the Chelas guys, who always support me, give me good advice and make my life colorful. Finally, I acknowledge the financial support provided by the State of São Paulo Research Foundation (FAPESP) grants 12/24537-0 and 15/12831-0. "It will hurt. It will take time. It will require dedication. It will require willpower. You will need to make healthy decisions. It requires sacrifice. You will need to push your body to its max. There will be temptation. But, I promise you, when you reach your goal, it's worth it. " (Unknown)

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Visual‐Interactive Preprocessing of Multivariate Time Series Data

Cited by 38 publications

References 59 publications

Towards a visual guide for communicating uncertainty in Visual Analytics

Towards a visual guide for communicating uncertainty in Visual Analytics

Data Abstraction for Visualizing Large Time Series

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

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