37-Issue 1
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Browsing 37-Issue 1 by Subject "Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics:] Picture/Image Generation—Line and curve generation"
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Item Data Abstraction for Visualizing Large Time Series(© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Shurkhovetskyy, G.; Andrienko, N.; Andrienko, G.; Fuchs, G.; Chen, Min and Benes, BedrichNumeric 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.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.Item Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization(© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Dasgupta, Aritra; Arendt, Dustin L.; Franklin, Lyndsey R.; Wong, Pak Chung; Cook, Kristin A.; Chen, Min and Benes, BedrichReal‐world systems change continuously. In domains such as traffic monitoring or cyber security, such changes occur within short time scales. This results in a streaming data problem and leads to unique challenges for the human in the loop, as analysts have to ingest and make sense of dynamic patterns in real time. While visualizations are being increasingly used by analysts to derive insights from streaming data, we lack a thorough characterization of the human‐centred design problems and a critical analysis of the state‐of‐the‐art solutions that exist for addressing these problems. In this paper, our goal is to fill this gap by studying how the state of the art in streaming data visualization handles the challenges and reflect on the gaps and opportunities. To this end, we have three contributions in this paper: (i) problem characterization for identifying domain‐specific goals and challenges for handling streaming data, (ii) a survey and analysis of the state of the art in streaming data visualization research with a focus on how visualization design meets challenges specific to change perception and (iii) reflections on the design trade‐offs, and an outline of potential research directions for addressing the gaps in the state of the art.Real‐world systems change continuously. In domains such as traffic monitoring or cyber security, such changes occur within short time scales. This results in a streaming data problem and leads to unique challenges for the human in the loop, as analysts have to ingest and make sense of dynamic patterns in real time. While visualizations are being increasingly used by analysts to derive insights from streaming data, we lack a thorough characterization of the human‐centred design problems and a critical analysis of the state‐of‐the‐art solutions that exist for addressing these problems.