Pub Date : 2016-12-22DOI: 10.1109/BDVA.2016.7787046
H. Nim, Mengyang Wang, Yujie Zhu, B. Sommer, F. Schreiber, S. Boyd, Stephen Jia Wang
For many years, the Great Barrier Reef (GBR) in Australia has been under serious threat of rapid decline due to a number of factors including heat stress events and the crown-of-thorn sea star. Human behaviour can directly and indirectly contribute to these factors, for example, through increased water and carbon footprints. In this paper, we illustrate the potential benefit of a large-scale mixed reality visualisation methodology in communicating complex GBR data, including exploring the impact of individual factors on the coral reef ecosystem. We present an immersive interactive visualisation, combining tiled displays (PerceptuWall) and head-mounted displays (Oculus DK2, Google Cardboard), that dynamically presents individualised coral damage information based on viewers' footprint inputs. The immersive tour provides a use-case for promoting understanding of how human behaviour impacts on GBR health by linking individual or regional actions to global outcomes, with the additional advantage of capturing the public's attention for immersive technologies.
多年来，由于热应激事件和棘冠海星等一系列因素，澳大利亚的大堡礁(GBR)一直面临着迅速下降的严重威胁。人类行为可以直接或间接地促成这些因素，例如，通过增加水和碳足迹。在本文中，我们说明了大规模混合现实可视化方法在交流复杂GBR数据方面的潜在好处，包括探索个体因素对珊瑚礁生态系统的影响。我们展示了一种身临其境的交互式可视化，结合了平纹显示器(PerceptuWall)和头戴式显示器(Oculus DK2, Google Cardboard)，根据观众的足迹输入动态呈现个性化的珊瑚损害信息。沉浸式参观提供了一个用例，通过将个人或区域行动与全球成果联系起来，促进对人类行为如何影响GBR健康的理解，并具有吸引公众注意沉浸式技术的额外优势。
{"title":"Communicating the Effect of Human Behaviour on the Great Barrier Reef via Mixed Reality Visualisation","authors":"H. Nim, Mengyang Wang, Yujie Zhu, B. Sommer, F. Schreiber, S. Boyd, Stephen Jia Wang","doi":"10.1109/BDVA.2016.7787046","DOIUrl":"https://doi.org/10.1109/BDVA.2016.7787046","url":null,"abstract":"For many years, the Great Barrier Reef (GBR) in Australia has been under serious threat of rapid decline due to a number of factors including heat stress events and the crown-of-thorn sea star. Human behaviour can directly and indirectly contribute to these factors, for example, through increased water and carbon footprints. In this paper, we illustrate the potential benefit of a large-scale mixed reality visualisation methodology in communicating complex GBR data, including exploring the impact of individual factors on the coral reef ecosystem. We present an immersive interactive visualisation, combining tiled displays (PerceptuWall) and head-mounted displays (Oculus DK2, Google Cardboard), that dynamically presents individualised coral damage information based on viewers' footprint inputs. The immersive tour provides a use-case for promoting understanding of how human behaviour impacts on GBR health by linking individual or regional actions to global outcomes, with the additional advantage of capturing the public's attention for immersive technologies.","PeriodicalId":201664,"journal":{"name":"2016 Big Data Visual Analytics (BDVA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131726522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-11-22DOI: 10.1109/BDVA.2016.7787051
M. Sankupellay, Tshering Dema, S. Tarar, M. Towsey, A. Truskinger, M. Brereton, P. Roe
Audio recording is a convenient and important method for large-scale terrestrial environmental monitoring. However, it is impossible to listen and make sense of all the data collected. Attempts to generalise automated analysis tasks have not been successful due to the unconstrained nature of long-term environmental recording. Our approach to this big-data challenge is to facilitate visualisation of long-term audio recording, to keep ecologists in the loop. The content of long-duration audio recordings are visualised by calculating acoustic indices. Our interface facilitates the customised visualisation and navigation of long-term audio recording by ecologists. Two case studies, one in Australia and one in Bhutan, are presented as examples.
{"title":"Visual Analytics of Eco-Acoustic Recordings: The Use of Acoustic Indices to Visualise 24-Hour Recordings","authors":"M. Sankupellay, Tshering Dema, S. Tarar, M. Towsey, A. Truskinger, M. Brereton, P. Roe","doi":"10.1109/BDVA.2016.7787051","DOIUrl":"https://doi.org/10.1109/BDVA.2016.7787051","url":null,"abstract":"Audio recording is a convenient and important method for large-scale terrestrial environmental monitoring. However, it is impossible to listen and make sense of all the data collected. Attempts to generalise automated analysis tasks have not been successful due to the unconstrained nature of long-term environmental recording. Our approach to this big-data challenge is to facilitate visualisation of long-term audio recording, to keep ecologists in the loop. The content of long-duration audio recordings are visualised by calculating acoustic indices. Our interface facilitates the customised visualisation and navigation of long-term audio recording by ecologists. Two case studies, one in Australia and one in Bhutan, are presented as examples.","PeriodicalId":201664,"journal":{"name":"2016 Big Data Visual Analytics (BDVA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130412257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-11-01DOI: 10.1109/BDVA.2016.7787047
Matthias Klapperstück, Tobias Czauderna, Cagatay Goncu, Jaroslaw Glowacki, Tim Dwyer, F. Schreiber, K. Marriott
The emerging field of Immersive Analytics investigates how novel display and interaction technologies can enable people to visualise and analyse data and complex information. In this paper, we present ContextuWall, a system for interactive local and remote collaboration using touch and mobile devices as well as displays of various sizes. The system enables groups of users located on different sites to share content to a jointly used virtual desktop which is accessible over a secured network. This virtual desktop can be shown on different large displays simultaneously, taking advantage of their high resolution. To enable users to intuitively share, arrange as well as annotate image content, a purpose-built client software has been built and can easily be adapted with plug-ins for existing data analytics software. We show exemplary use cases and describe the system architecture and its implementation.
{"title":"ContextuWall: Peer Collaboration Using (Large) Displays","authors":"Matthias Klapperstück, Tobias Czauderna, Cagatay Goncu, Jaroslaw Glowacki, Tim Dwyer, F. Schreiber, K. Marriott","doi":"10.1109/BDVA.2016.7787047","DOIUrl":"https://doi.org/10.1109/BDVA.2016.7787047","url":null,"abstract":"The emerging field of Immersive Analytics investigates how novel display and interaction technologies can enable people to visualise and analyse data and complex information. In this paper, we present ContextuWall, a system for interactive local and remote collaboration using touch and mobile devices as well as displays of various sizes. The system enables groups of users located on different sites to share content to a jointly used virtual desktop which is accessible over a secured network. This virtual desktop can be shown on different large displays simultaneously, taking advantage of their high resolution. To enable users to intuitively share, arrange as well as annotate image content, a purpose-built client software has been built and can easily be adapted with plug-ins for existing data analytics software. We show exemplary use cases and describe the system architecture and its implementation.","PeriodicalId":201664,"journal":{"name":"2016 Big Data Visual Analytics (BDVA)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125396909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-11-01DOI: 10.1109/BDVA.2016.7787050
J. A. Walsh, J. Zucco, Ross T. Smith, B. Thomas
Given the diverse set of pervasive tracking technologies available, temporal-geospatial data is being collected at an unprecedented rate. However, the effective visualization and interpretation of this data remains elusive. Visualizations have focused on showing an object's location, however more complex inter-entity queries also need to be supported, e.g. "did X and Y meet, and if so, where and when?". We present Cooperative Visual Analysis, a combination of two novel visualizations, the Parallel Schedule View and the Braille Plot, working in synergy with a traditional 2D map. The Parallel Schedule View focuses on showing colocation (simultaneous or time separated), with the Braille Plot used to resolve position ambiguity and identify patterns and trends within a data trace (in addition to colocation). We present descriptions of each, and a user study showing support for these approaches. The study compared Cooperative Visual Analysis with a current approach, the Space Time Cube, and found the Cooperative Visual Analysis is an effective means for visualizing temporal-geospatial relationships in a data set, performing at or above the Space Time Cube, whilst being preferred by users.
{"title":"Temporal-Geospatial Cooperative Visual Analysis","authors":"J. A. Walsh, J. Zucco, Ross T. Smith, B. Thomas","doi":"10.1109/BDVA.2016.7787050","DOIUrl":"https://doi.org/10.1109/BDVA.2016.7787050","url":null,"abstract":"Given the diverse set of pervasive tracking technologies available, temporal-geospatial data is being collected at an unprecedented rate. However, the effective visualization and interpretation of this data remains elusive. Visualizations have focused on showing an object's location, however more complex inter-entity queries also need to be supported, e.g. \"did X and Y meet, and if so, where and when?\". We present Cooperative Visual Analysis, a combination of two novel visualizations, the Parallel Schedule View and the Braille Plot, working in synergy with a traditional 2D map. The Parallel Schedule View focuses on showing colocation (simultaneous or time separated), with the Braille Plot used to resolve position ambiguity and identify patterns and trends within a data trace (in addition to colocation). We present descriptions of each, and a user study showing support for these approaches. The study compared Cooperative Visual Analysis with a current approach, the Space Time Cube, and found the Cooperative Visual Analysis is an effective means for visualizing temporal-geospatial relationships in a data set, performing at or above the Space Time Cube, whilst being preferred by users.","PeriodicalId":201664,"journal":{"name":"2016 Big Data Visual Analytics (BDVA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126090547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-11-01DOI: 10.1109/BDVA.2016.7787044
Thi Thuong Huyen Nguyen, Peter Marendy, U. Engelke
Recent trends in computing environments indicate that the future infrastructure for visual analytics will be distributed and collaborative. Collaborative frameworks create value for scientists, analysts, industrial partners, domain experts, and other end-users to meet, communicate, interact with others, and coordinate their activities in a globally shared network. This paper focuses on collaborative framework design for immersive analytics facilitating the integration of multimodal immersive interfaces. The framework design takes into account visualisation and interaction techniques for multiple users and especially decision support tools for scientific visual analytics experts. An overview of several important aspects of collaborative platforms for immersive analytics is presented and different modules of our proposed platform (including data management, analytics, visualisation, querying, and user interface design) will be detailed to highlight their importance in a full visual analytics pipeline.
{"title":"Collaborative Framework Design for Immersive Analytics","authors":"Thi Thuong Huyen Nguyen, Peter Marendy, U. Engelke","doi":"10.1109/BDVA.2016.7787044","DOIUrl":"https://doi.org/10.1109/BDVA.2016.7787044","url":null,"abstract":"Recent trends in computing environments indicate that the future infrastructure for visual analytics will be distributed and collaborative. Collaborative frameworks create value for scientists, analysts, industrial partners, domain experts, and other end-users to meet, communicate, interact with others, and coordinate their activities in a globally shared network. This paper focuses on collaborative framework design for immersive analytics facilitating the integration of multimodal immersive interfaces. The framework design takes into account visualisation and interaction techniques for multiple users and especially decision support tools for scientific visual analytics experts. An overview of several important aspects of collaborative platforms for immersive analytics is presented and different modules of our proposed platform (including data management, analytics, visualisation, querying, and user interface design) will be detailed to highlight their importance in a full visual analytics pipeline.","PeriodicalId":201664,"journal":{"name":"2016 Big Data Visual Analytics (BDVA)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129130405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-11-01DOI: 10.1109/BDVA.2016.7787042
Dirk Zeckzer, Daniel Gerighausen, Lydia Müller
Epigenetics data is very important for understanding the differentiation of cells into different cell types. Moreover, the amount of epigenetic data available was and still is considerably increasing. To cope with this big amount of data, statistical or visual analysis is used. Usually, biologists analyze epigenetic data using statistical methods like correlations on a high level. However, this does not allow to analyze the fate of histone modifications in detail during cell specification or to compare histone modifications in different cell lines. Tiled binned scatter plot matrices proved to be very useful for this type of analysis showing binary relationships. We adapted the idea of tiling and binning scatter plots from 2D to 3D, such that ternary relationships can be depicted. Comparing tiled binned 3D scatter plots--the new method--to tiled binned 2D scatter plot matrices showed, that many relations that are difficult or impossible to find using tiled binned 2D scatter plot matrices can easily be observed using the new approach. We found that using our approach, changes in the distribution of the marks over time (different cell types) or differences between different replicates of the same cell sample are easy to detect. Tiled binned 3D scatter plots proved superior compared to the previously used method due to the reduced amount of overplotting leading to less interaction necessary for gaining similar insights.
{"title":"Analyzing Histone Modifications in iPS Cells Using Tiled Binned 3D Scatter Plots","authors":"Dirk Zeckzer, Daniel Gerighausen, Lydia Müller","doi":"10.1109/BDVA.2016.7787042","DOIUrl":"https://doi.org/10.1109/BDVA.2016.7787042","url":null,"abstract":"Epigenetics data is very important for understanding the differentiation of cells into different cell types. Moreover, the amount of epigenetic data available was and still is considerably increasing. To cope with this big amount of data, statistical or visual analysis is used. Usually, biologists analyze epigenetic data using statistical methods like correlations on a high level. However, this does not allow to analyze the fate of histone modifications in detail during cell specification or to compare histone modifications in different cell lines. Tiled binned scatter plot matrices proved to be very useful for this type of analysis showing binary relationships. We adapted the idea of tiling and binning scatter plots from 2D to 3D, such that ternary relationships can be depicted. Comparing tiled binned 3D scatter plots--the new method--to tiled binned 2D scatter plot matrices showed, that many relations that are difficult or impossible to find using tiled binned 2D scatter plot matrices can easily be observed using the new approach. We found that using our approach, changes in the distribution of the marks over time (different cell types) or differences between different replicates of the same cell sample are easy to detect. Tiled binned 3D scatter plots proved superior compared to the previously used method due to the reduced amount of overplotting leading to less interaction necessary for gaining similar insights.","PeriodicalId":201664,"journal":{"name":"2016 Big Data Visual Analytics (BDVA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126995623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-11-01DOI: 10.1109/BDVA.2016.7787041
Jamie Waese, W. Stuerzlinger, N. Provart
Accurately identifying images with subtly varying features from a large set of similar images can be a challenging task. To succeed, viewers must perceive subtle differences between multiple nearly identical images and react appropriately. The Rapid Serial Visual Presentation (RSVP) display technique has the potential to improve performance as it exploits our ability to preattentively recognize differences between images when they are flashed on a screen in a rapid and serial manner. We compared the speed and accuracy of three RSVP interface methods ("Hover", "Slide Show" and "Velocity") against a traditional "Point & Click" non-RSVP interface to test whether an RSVP display improves performance in visual search tasks. In a follow-up study we compared "Hover" and "Velocity" RSVP interface methods against a "Small Multiples" non-RSVP interface to explore the interaction of interface type and target size on visual search tasks. We found the "Hover" RSVP interface to significantly reduce the time it takes to perform visual search tasks with no reduction in accuracy, regardless of the size of the search targets. Beyond the gene identification task tested here, these experiments inform the design of user interfaces for many other visual search tasks.
{"title":"An Evaluation of Interaction Methods for Controlling RSVP Displays in Visual Search Tasks","authors":"Jamie Waese, W. Stuerzlinger, N. Provart","doi":"10.1109/BDVA.2016.7787041","DOIUrl":"https://doi.org/10.1109/BDVA.2016.7787041","url":null,"abstract":"Accurately identifying images with subtly varying features from a large set of similar images can be a challenging task. To succeed, viewers must perceive subtle differences between multiple nearly identical images and react appropriately. The Rapid Serial Visual Presentation (RSVP) display technique has the potential to improve performance as it exploits our ability to preattentively recognize differences between images when they are flashed on a screen in a rapid and serial manner. We compared the speed and accuracy of three RSVP interface methods (\"Hover\", \"Slide Show\" and \"Velocity\") against a traditional \"Point & Click\" non-RSVP interface to test whether an RSVP display improves performance in visual search tasks. In a follow-up study we compared \"Hover\" and \"Velocity\" RSVP interface methods against a \"Small Multiples\" non-RSVP interface to explore the interaction of interface type and target size on visual search tasks. We found the \"Hover\" RSVP interface to significantly reduce the time it takes to perform visual search tasks with no reduction in accuracy, regardless of the size of the search targets. Beyond the gene identification task tested here, these experiments inform the design of user interfaces for many other visual search tasks.","PeriodicalId":201664,"journal":{"name":"2016 Big Data Visual Analytics (BDVA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131125884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-11-01DOI: 10.1109/BDVA.2016.7787048
Jenny Vuong, C. Stolte, Sandeep Kaur, S. O’Donoghue
High-resolution mass spectrometry can now track all temporal changes in the phosphoproteomes of cells. The resulting time-series datasets pose a challenge ripe for the visual analytics community: how to effectively visualise - in a single graph-time-profiles for many thousands of proteins and protein complexes. To address this challenge we recently proposed a novel graph layout strategy Minardo that uses 'tracks' instead of nodes to communicate cell signalling pathways, displaying all events simultaneously, ordered in clockwise progression. Here, we summarize the key visual concepts used in Minardo to address the complexity of cell signalling data. We also discuss ongoing work on Minardo to allow interactive and collaborative approaches to managing large proteomics time-series datasets.
{"title":"Developing a Visual Analytics Tool for Large-Scale Proteomics Time-Series Data","authors":"Jenny Vuong, C. Stolte, Sandeep Kaur, S. O’Donoghue","doi":"10.1109/BDVA.2016.7787048","DOIUrl":"https://doi.org/10.1109/BDVA.2016.7787048","url":null,"abstract":"High-resolution mass spectrometry can now track all temporal changes in the phosphoproteomes of cells. The resulting time-series datasets pose a challenge ripe for the visual analytics community: how to effectively visualise - in a single graph-time-profiles for many thousands of proteins and protein complexes. To address this challenge we recently proposed a novel graph layout strategy Minardo that uses 'tracks' instead of nodes to communicate cell signalling pathways, displaying all events simultaneously, ordered in clockwise progression. Here, we summarize the key visual concepts used in Minardo to address the complexity of cell signalling data. We also discuss ongoing work on Minardo to allow interactive and collaborative approaches to managing large proteomics time-series datasets.","PeriodicalId":201664,"journal":{"name":"2016 Big Data Visual Analytics (BDVA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122174824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-11-01DOI: 10.1109/BDVA.2016.7787045
Jessie Roberts, Phillip Gough
Communicating statistical uncertainty to non-expert users is essential to translating data driven insights to create impact in the 'real world'. Embedding uncertainty in data visualizations however, can be a significant design challenge due when communicating to non-expert decision makers, and has been avoided in the past due to fear of overwhelming or confusing the audience. This research aims to explore interactive disease mapping features that enable the user to explore the data and reveal the uncertainty within the information presented. Understanding uncertainty enables the user to be aware of the limitations of data driven insights, and leads to more informed decision making processes.
{"title":"Communicating Statistical Uncertainty to Non-Expert Audiences: Interactive Disease Mapping","authors":"Jessie Roberts, Phillip Gough","doi":"10.1109/BDVA.2016.7787045","DOIUrl":"https://doi.org/10.1109/BDVA.2016.7787045","url":null,"abstract":"Communicating statistical uncertainty to non-expert users is essential to translating data driven insights to create impact in the 'real world'. Embedding uncertainty in data visualizations however, can be a significant design challenge due when communicating to non-expert decision makers, and has been avoided in the past due to fear of overwhelming or confusing the audience. This research aims to explore interactive disease mapping features that enable the user to explore the data and reveal the uncertainty within the information presented. Understanding uncertainty enables the user to be aware of the limitations of data driven insights, and leads to more informed decision making processes.","PeriodicalId":201664,"journal":{"name":"2016 Big Data Visual Analytics (BDVA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129289574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-11-01DOI: 10.1109/BDVA.2016.7787043
Neven A. M. ElSayed, Ross T. Smith, K. Marriott, B. Thomas
This paper presents a context aware model for situated analytics, supporting a blended user interface. Our approach is a state-based model, allowing seamless transition between the physical space and information space during use. We designed the model to allow common user interface controls to work in tandem with the printed information on a physical object by adapting the operation and presentation based on a semantic matrix. We demonstrate the use of the model with a set of blended controls including; pinch zoom, menus, and details-on-demand. We analyze each control to highlight how the physical and virtual information spaces work in tandem to provide a rich interaction environment in augmented reality.
{"title":"Blended UI Controls for Situated Analytics","authors":"Neven A. M. ElSayed, Ross T. Smith, K. Marriott, B. Thomas","doi":"10.1109/BDVA.2016.7787043","DOIUrl":"https://doi.org/10.1109/BDVA.2016.7787043","url":null,"abstract":"This paper presents a context aware model for situated analytics, supporting a blended user interface. Our approach is a state-based model, allowing seamless transition between the physical space and information space during use. We designed the model to allow common user interface controls to work in tandem with the printed information on a physical object by adapting the operation and presentation based on a semantic matrix. We demonstrate the use of the model with a set of blended controls including; pinch zoom, menus, and details-on-demand. We analyze each control to highlight how the physical and virtual information spaces work in tandem to provide a rich interaction environment in augmented reality.","PeriodicalId":201664,"journal":{"name":"2016 Big Data Visual Analytics (BDVA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116060271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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