Sizing the horizon

Heer, Jeffrey; Kong, Nicholas; Agrawala, Maneesh

doi:10.1145/1518701.1518897

Cited by 228 publications

(39 citation statements)

References 22 publications

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“…Following previous graphical perception studies [4,10,14,16,21], we used synthetic time series data in order to have greater control on the data values and their corresponding visual representation. We generated 96 distinct time series datasets, one for each experiment condition.…”

Section: Time Series Datamentioning

confidence: 99%

“…The aforementioned trend of focusing more on visual representation than on interaction is also predominant in graphical perception of time series visualisations, which is a well-studied area in visual analytics. A common practice in quantitative graphical perception studies of time series visualisations [4,10,14,16,21] is to conduct them in a static setting, i.e., not allowing the users to interact with the time series visualisations, thereby limiting our knowledge of the user experience. In addition, the overall coverage of different visual representations of time series data is lacking.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the overall coverage of different visual representations of time series data is lacking. Several previous studies [4,10,14,16,21] have compared the effectiveness of time series visualisations within and across the categories of positional (e.g., Figure 1(a&b)) and colour visual encodings (e.g., Figure 1(c&d)). However, to the best of our knowledge, there has been no study that compares the effectiveness of time series visualisations that use area visual encoding (e.g., Figure 1(e&f)) with their positional and colour counterparts.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Investigating Time Series Visualisations to Improve the User Experience

Adnan

Just

Baillie

2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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Research on graphical perception of time series visualisations has focused on visual representation, and not on interaction. Even for visual representation, there has been limited study of the impact on users of visual encodings and the strengths and weaknesses of Cartesian and Polar coordinate systems. In order to address this research gap, we performed a comprehensive graphical perception study that measured the effectiveness of time series visualisations with different interactions, visual encodings and coordinate systems for several tasks. Our results show that, while positional and colour visual encodings were better for most tasks, area visual encoding performed better for data comparison. Most importantly, we identified that introducing interactivity within time series visualisations considerably enhances the user experience, without any loss of efficiency or accuracy. We believe that our findings can greatly improve the development of visual analytics tools using time series visualisations in a variety of domains.

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Section: Time Series Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating Time Series Visualisations to Improve the User Experience

Adnan

Just

Baillie

2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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“…The StateFinder algorithm can be applied several times to build a multi-level state representation. The raw data is displayed using the horizon graph [7].…”

Section: A Segmentationmentioning

confidence: 99%

Online unsupervised state recognition in sensor data

Eberle

Wijaya

Aberer

2015

2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Abstract-Smart sensors, such as smart meters or smart phones, are nowadays ubiquitous. To be "smart", however, they need to process their input data with limited storage and computational resources. In this paper, we convert the stream of sensor data into a stream of symbols, and further, to higher level symbols in such a way that common analytical tasks such as anomaly detection, forecasting or state recognition, can still be carried out on the transformed data with almost no loss of accuracy, and using far fewer resources. We identify states of a monitored system and convert them into symbols (thus, reducing data size), while keeping "interesting" events, such as anomalies or transition between states, as it is. Our algorithm is able to find states of various length in an online and unsupervised way, which is crucial since behavior of the system is not known beforehand. We show the effectiveness of our approach using real-world datasets and various application scenarios.

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“…Lam et al [LMK07] compared the effectiveness of using multiple line graphs and heatmaps for analyzing overviews over large datasets and found that heatmap was more efficient for finding the maximum values and comparison, but less efficient for finding the graph with the maximum number of peaks. Horizon graphs [HKA09], which are more space-efficient than time series, could be also used in place of the heatmap. It is not clear, though, how well they would support changing the zoom level.…”

Section: Design Considerationsmentioning

confidence: 99%

Flowstrates: An Approach for Visual Exploration of Temporal Origin‐Destination Data

Boyandin

Bertini

Bak

et al. 2011

Computer Graphics Forum

118

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Many origin-destination datasets have become available in the recent years, e.g. flows of people, animals, money, material, or network traffic between pairs of locations, but appropriate techniques for their exploration still have to be developed. Especially, supporting the analysis of datasets with a temporal dimension remains a significant challenge. Many techniques for the exploration of spatio-temporal data have been developed, but they prove to be only of limited use when applied to temporal origin-destination datasets. We present Flowstrates, a new interactive visualization approach in which the origins and the destinations of the flows are displayed in two separate maps, and the changes over time of the flow magnitudes are represented in a separate heatmap view in the middle. This allows the users to perform spatial visual queries, focusing on different regions of interest for the origins and destinations, and to analyze the changes over time provided with the means of flow ordering, filtering and aggregation in the heatmap. In this paper, we discuss the challenges associated with the visualization of temporal origin-destination data, introduce our solution, and present several usage scenarios showing how the tool we have developed supports them.

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Sizing the horizon

Cited by 228 publications

References 22 publications

Investigating Time Series Visualisations to Improve the User Experience

Investigating Time Series Visualisations to Improve the User Experience

Online unsupervised state recognition in sensor data

Flowstrates: An Approach for Visual Exploration of Temporal Origin‐Destination Data

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