2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)最新文献
Pub Date : 2018-10-01DOI: 10.1109/BDVA.2018.8534020
Raphael Sahann, Torsten Möller
We conducted a design study to do an in-depth analysis of the problem of operational planning at universities and designed a decision support tool for that problem, called Operational Curricular Planning (OCP). Based on our observations we abstracted the planning process into separate tasks. Focusing on a subset of tasks that we characterized, we present the OCP tool for visually supporting decision making in the process of planning teaching resources. We show the steps leading to the final design of our visual decision support system and discuss the design decisions made while building the tool. Finally, we present an evaluation with four domain experts in a real- world scenario and talk about lessons learned from building the OCP tool, including the issue of integration and adoption of the system.
{"title":"OCP - Operational Curricular Planning: A Visual Decision Support System for Planning Teaching Resources at Universities","authors":"Raphael Sahann, Torsten Möller","doi":"10.1109/BDVA.2018.8534020","DOIUrl":"https://doi.org/10.1109/BDVA.2018.8534020","url":null,"abstract":"We conducted a design study to do an in-depth analysis of the problem of operational planning at universities and designed a decision support tool for that problem, called Operational Curricular Planning (OCP). Based on our observations we abstracted the planning process into separate tasks. Focusing on a subset of tasks that we characterized, we present the OCP tool for visually supporting decision making in the process of planning teaching resources. We show the steps leading to the final design of our visual decision support system and discuss the design decisions made while building the tool. Finally, we present an evaluation with four domain experts in a real- world scenario and talk about lessons learned from building the OCP tool, including the issue of integration and adoption of the system.","PeriodicalId":92742,"journal":{"name":"2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)","volume":"71 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84512919","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 : 2018-10-01DOI: 10.1109/bdva.2018.8533886
{"title":"Copyright","authors":"","doi":"10.1109/bdva.2018.8533886","DOIUrl":"https://doi.org/10.1109/bdva.2018.8533886","url":null,"abstract":"","PeriodicalId":92742,"journal":{"name":"2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75064751","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 : 2018-10-01DOI: 10.1109/BDVA.2018.8534019
Maha El Meseery, Yuyao Wu, W. Stuerzlinger
Exploratory visual analysis is an iterative process, where analysts often start from an overview of the data. Subsequently, they often pursue different hypotheses through multiple rounds of interaction and analysis. Commercial visualization packages support mostly a model with a single analysis path, where the system view represents only the final state of the users' current analysis. In this paper, we investigate the benefit of using multiple workspaces to support alternative analyses, enabling users to create different workspaces to pursue multiple analysis paths at the same time. We implemented a prototype for multiple workspaces using a multi-tab design in a visual analytics system. The results of our user studies show that multiple workspaces: enable analysts to work on concurrent tasks, work well for organizing an analysis, and make it easy to revisit previous parts of their work.
{"title":"Multiple Workspaces in Visual Analytics","authors":"Maha El Meseery, Yuyao Wu, W. Stuerzlinger","doi":"10.1109/BDVA.2018.8534019","DOIUrl":"https://doi.org/10.1109/BDVA.2018.8534019","url":null,"abstract":"Exploratory visual analysis is an iterative process, where analysts often start from an overview of the data. Subsequently, they often pursue different hypotheses through multiple rounds of interaction and analysis. Commercial visualization packages support mostly a model with a single analysis path, where the system view represents only the final state of the users' current analysis. In this paper, we investigate the benefit of using multiple workspaces to support alternative analyses, enabling users to create different workspaces to pursue multiple analysis paths at the same time. We implemented a prototype for multiple workspaces using a multi-tab design in a visual analytics system. The results of our user studies show that multiple workspaces: enable analysts to work on concurrent tasks, work well for organizing an analysis, and make it easy to revisit previous parts of their work.","PeriodicalId":92742,"journal":{"name":"2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)","volume":"47 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80711216","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 : 2018-10-01DOI: 10.1109/BDVA.2018.8534026
Arnaud Prouzeau, B. DharshiniM., Manivannan Balasubramaniam, Joshua Henry, Ngoc Hoang, Tim Dwyer
Building management systems (BMS) provide monitoring and control of most large-building assets (heating, ventilation, air conditioning, lighting, security systems, and so on). With the recent advancement of the Internet of Things and data management systems, BMS must gather and manage increasingly detailed data coming from a greater number and diversity of sources. The availability of such data should help building managers optimise the energy consumption of buildings. However, current BMS don't allow efficient visualisation of such data, which means that even if the data is available, it is not used to its full potential. In this paper, we describe a prototype BMS interface providing interactive visualisations of traditional building data (temperature, energy consumption), as well as more novel data (comfort feedback from occupants and live occupancy). We evaluate this prototype by first showing how it could be used to plan a long- term energy saving strategy, and then in a feedback session involving facility managers at a university.
{"title":"Visual Analytics for Energy Monitoring in the Context of Building Management","authors":"Arnaud Prouzeau, B. DharshiniM., Manivannan Balasubramaniam, Joshua Henry, Ngoc Hoang, Tim Dwyer","doi":"10.1109/BDVA.2018.8534026","DOIUrl":"https://doi.org/10.1109/BDVA.2018.8534026","url":null,"abstract":"Building management systems (BMS) provide monitoring and control of most large-building assets (heating, ventilation, air conditioning, lighting, security systems, and so on). With the recent advancement of the Internet of Things and data management systems, BMS must gather and manage increasingly detailed data coming from a greater number and diversity of sources. The availability of such data should help building managers optimise the energy consumption of buildings. However, current BMS don't allow efficient visualisation of such data, which means that even if the data is available, it is not used to its full potential. In this paper, we describe a prototype BMS interface providing interactive visualisations of traditional building data (temperature, energy consumption), as well as more novel data (comfort feedback from occupants and live occupancy). We evaluate this prototype by first showing how it could be used to plan a long- term energy saving strategy, and then in a feedback session involving facility managers at a university.","PeriodicalId":92742,"journal":{"name":"2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)","volume":"34 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75081587","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 : 2018-10-01DOI: 10.1109/bdva.2018.8533885
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Pub Date : 2018-07-16DOI: 10.1109/BDVA.2018.8534025
Mohammed Ali, Mark W. Jones, Xianghua Xie, Mark Williams
We address the problem of visualizing and interacting with large multi-dimensional time- series data. We propose a visual analytics system and approach which aims to visualize, analyze, present and enable exploration of large temporal datasets. Our approach consists of three main stages which are preprocessing, dimensionality reduction, and visual exploration. It assists with finding the interesting features in the data which are often obscured in the line chart because of the visual compression that is required to render the large dataset to screen. Our approach helps to obtain an overview of the entire dataset and track changes over time. It enables the user to detect clusters and outliers and observe the transitions between data. The juxtaposed views are used to visualize and interact both with raw time series data and projected data. Different time series datasets are deployed on our system, and we demonstrate the utility and evaluate the results using a case study with two different datasets which show the effectiveness of our system.
{"title":"Towards Visual Exploration of Large Temporal Datasets","authors":"Mohammed Ali, Mark W. Jones, Xianghua Xie, Mark Williams","doi":"10.1109/BDVA.2018.8534025","DOIUrl":"https://doi.org/10.1109/BDVA.2018.8534025","url":null,"abstract":"We address the problem of visualizing and interacting with large multi-dimensional time- series data. We propose a visual analytics system and approach which aims to visualize, analyze, present and enable exploration of large temporal datasets. Our approach consists of three main stages which are preprocessing, dimensionality reduction, and visual exploration. It assists with finding the interesting features in the data which are often obscured in the line chart because of the visual compression that is required to render the large dataset to screen. Our approach helps to obtain an overview of the entire dataset and track changes over time. It enables the user to detect clusters and outliers and observe the transitions between data. The juxtaposed views are used to visualize and interact both with raw time series data and projected data. Different time series datasets are deployed on our system, and we demonstrate the utility and evaluate the results using a case study with two different datasets which show the effectiveness of our system.","PeriodicalId":92742,"journal":{"name":"2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)","volume":"82 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2018-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85407090","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 : 2018-03-28DOI: 10.1109/BDVA.2018.8534027
Gokhan Cetin, W. Stuerzlinger, J. Dill
Large, high-resolution displays (LHRDs) have been shown to enable increased productivity over conventional monitors. Previous work has identified the benefits of LHRDs for Visual Analytics tasks, where the user is analyzing complex data sets. However, LHRDs are fundamentally different from desktop and mobile computing environments, presenting some unique usability challenges and opportunities, and need to be better understood. There is thus a need for additional studies to analyze the impact of LHRD size and display resolution on content spatialization strategies and Visual Analytics task performance. We present the results of two studies of the effects of physical display size and resolution on analytical task successes and also analyze how participants spatially cluster visual content in different display conditions. Overall, we found that navigation technique preferences differ significantly among users, that the wide range of observed spatialization types suggest several different analysis techniques are adopted, and that display size affects clustering task performance whereas display resolution does not.
{"title":"Visual Analytics on Large Displays: Exploring User Spatialization and How Size and Resolution Affect Task Performance","authors":"Gokhan Cetin, W. Stuerzlinger, J. Dill","doi":"10.1109/BDVA.2018.8534027","DOIUrl":"https://doi.org/10.1109/BDVA.2018.8534027","url":null,"abstract":"Large, high-resolution displays (LHRDs) have been shown to enable increased productivity over conventional monitors. Previous work has identified the benefits of LHRDs for Visual Analytics tasks, where the user is analyzing complex data sets. However, LHRDs are fundamentally different from desktop and mobile computing environments, presenting some unique usability challenges and opportunities, and need to be better understood. There is thus a need for additional studies to analyze the impact of LHRD size and display resolution on content spatialization strategies and Visual Analytics task performance. We present the results of two studies of the effects of physical display size and resolution on analytical task successes and also analyze how participants spatially cluster visual content in different display conditions. Overall, we found that navigation technique preferences differ significantly among users, that the wide range of observed spatialization types suggest several different analysis techniques are adopted, and that display size affects clustering task performance whereas display resolution does not.","PeriodicalId":92742,"journal":{"name":"2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)","volume":"11 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2018-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83779705","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 : 2018-01-01DOI: 10.1007/978-3-030-01388-2_6
Petra Isenberg, Bongshin Lee, Huamin Qu, Maxime Cordeil
{"title":"Immersive Visual Data Stories","authors":"Petra Isenberg, Bongshin Lee, Huamin Qu, Maxime Cordeil","doi":"10.1007/978-3-030-01388-2_6","DOIUrl":"https://doi.org/10.1007/978-3-030-01388-2_6","url":null,"abstract":"","PeriodicalId":92742,"journal":{"name":"2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)","volume":"9 1","pages":"165-184"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87813374","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 : 2018-01-01DOI: 10.1007/978-3-030-01388-2_11
T. Chandler, T. Morgan, T. Kuhlen
{"title":"Exploring Immersive Analytics for Built Environments","authors":"T. Chandler, T. Morgan, T. Kuhlen","doi":"10.1007/978-3-030-01388-2_11","DOIUrl":"https://doi.org/10.1007/978-3-030-01388-2_11","url":null,"abstract":"","PeriodicalId":92742,"journal":{"name":"2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)","volume":"20 1","pages":"331-357"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87176104","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 : 2018-01-01DOI: 10.1007/978-3-030-01388-2_2
K. Marriott, Jian Chen, Marcel Hlawatsch, T. Itoh, Miguel A. Nacenta, G. Reina, W. Stuerzlinger
{"title":"Immersive Analytics: Time to Reconsider the Value of 3D for Information Visualisation","authors":"K. Marriott, Jian Chen, Marcel Hlawatsch, T. Itoh, Miguel A. Nacenta, G. Reina, W. Stuerzlinger","doi":"10.1007/978-3-030-01388-2_2","DOIUrl":"https://doi.org/10.1007/978-3-030-01388-2_2","url":null,"abstract":"","PeriodicalId":92742,"journal":{"name":"2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)","volume":"8 1","pages":"25-55"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87085624","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}
2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)
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