Autor/Autorin: Aslam Ammad
Ammad Aslam, started his career as a Software QA Engineer,testing and improving the quality of software products. Later on, pursued a Masters degree in Computer Science with emphasis on Human Computer Interaction. Saad Bin Azhar holds a research and development position, working with Usability, HMI, and Visualization aspects of industrial systems.
Presenting large amount of Multivariate Data is not a simple problem. When there are multiple correlated variables involved, it becomes difficult to comprehend data using traditional ways. Information Visualization techniques provide an interactive way to present and analyze such data. This research work has been carried out at ABB Corporate Research, Västerås, Sweden. Use of Parallel Coordinates and Multiple Coordinated Views was has been suggested to realize interactive reporting and trending of Multivariate Data for ABB´s Network Manager SCADA system. A prototype was developed and an empirical study was conducted to evaluate the suggested design and test it for usability from an actual industry perspective. With the help of this prototype and the evaluations carried out, we are able to achieve stronger results regarding the effectiveness and efficiency of the visualization techniques used. The results confirm that such interfaces are more effective, efficient and intuitive for filtering and analyzing Multivariate Data.
Presenting large amount of Multivariate Data is not a simple problem. When there are multiple correlated variables involved, it becomes difficult to comprehend data using traditional ways. Information Visualization techniques provide an interactive way to present and analyze such data. This research work has been carried out at ABB Corporate Research, Västerås, Sweden. Use of Parallel Coordinates and Multiple Coordinated Views was has been suggested to realize interactive reporting and trending of Multivariate Data for ABB's Network Manager SCADA system. A prototype was developed and an empirical study was conducted to evaluate the suggested design and test it for usability from an actual industry perspective. With the help of this prototype and the evaluations carried out, we are able to achieve stronger results regarding the effectiveness and efficiency of the visualization techniques used. The results confirm that such interfaces are more effective, efficient and intuitive for filtering and analyzing Multivariate Data.
Aslam, AmmadAmmad Aslam, started his career as a Software QA Engineer,testing and improving the quality of software products. Later on, pursued a Masters degree in Computer Science with emphasis on Human Computer Interaction. Saad Bin Azhar holds a research and development position, working with Usability, HMI, and Visualization aspects of industrial systems.
Über den Autor
Ammad Aslam, started his career as a Software QA Engineer,testing and improving the quality of software products. Later on, pursued a Masters degree in Computer Science with emphasis on Human Computer Interaction. Saad Bin Azhar holds a research and development position, working with Usability, HMI, and Visualization aspects of industrial systems.
Klappentext
Presenting large amount of Multivariate Data is not a simple problem. When there are multiple correlated variables involved, it becomes difficult to comprehend data using traditional ways. Information Visualization techniques provide an interactive way to present and analyze such data. This research work has been carried out at ABB Corporate Research, Västerås, Sweden. Use of Parallel Coordinates and Multiple Coordinated Views was has been suggested to realize interactive reporting and trending of Multivariate Data for ABB¿s Network Manager SCADA system. A prototype was developed and an empirical study was conducted to evaluate the suggested design and test it for usability from an actual industry perspective. With the help of this prototype and the evaluations carried out, we are able to achieve stronger results regarding the effectiveness and efficiency of the visualization techniques used. The results confirm that such interfaces are more effective, efficient and intuitive for filtering and analyzing Multivariate Data.
