Data Abstraction for Visualizing Large Time Series
| dc.contributor.author | Shurkhovetskyy, G. | en_US |
| dc.contributor.author | Andrienko, N. | en_US |
| dc.contributor.author | Andrienko, G. | en_US |
| dc.contributor.author | Fuchs, G. | en_US |
| dc.contributor.editor | Chen, Min and Benes, Bedrich | en_US |
| dc.date.accessioned | 2018-04-05T12:48:37Z | |
| dc.date.available | 2018-04-05T12:48:37Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | 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.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. | en_US |
| dc.description.number | 1 | |
| dc.description.sectionheaders | Articles | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 37 | |
| dc.identifier.doi | 10.1111/cgf.13237 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 125-144 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13237 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13237 | |
| dc.publisher | © 2018 The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | data visualization | |
| dc.subject | visual analytics | |
| dc.subject | data abstraction | |
| dc.subject | time series | |
| dc.subject | visualization pipeline | |
| dc.subject | Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics:] Picture/Image Generation—Line and curve generation | |
| dc.title | Data Abstraction for Visualizing Large Time Series | en_US |