In this work, we seek to understand the needs of interaction designers involved in industrial system engineering processes. While current research offers a set of methods and tools for them, we believe that more empirical user studies focusing on designers are needed, in particular to support how model-based activity analysis may inform their decisions. Our designers' need analysis is conducted through participatory design and contextual inquiry, and applied through a real use-case project: a distributed tactile tool for airborne maritime surveillance. Thanks to this study, we report on our insights on the usability problems and needs related in particular to scenario-based modeling, model-based design rationales and design-based model refinement.Interactive System Design; System Engineering Empirical Studies; Model-based Engineering; Participatory Design; Scenario-based Design; Design Rationales.
In this paper we present HeloVis: a 3D interactive visualization that relies on immersive properties to improve the user performance during SIGINT analysis. SIGINT, which stands for SIGnal INTelligence, is a field facing many challenges like huge amounts of data, complex data and novice users. HeloVis draws on perceptive biases, highlighted by Gestalt laws, and on depth perception to enhance the recurrence properties contained into the data and to abstract from interferences such as noise or missing data. In this paper, we first present SIGINT and the challenges that it brings to visual analytics. Then, we present the existing work that is currently used in or that fits the SIGINT context. Finally, we present HeloVis, an innovative application on an immersive context that allows performing SIG-INT analysis and we present its evaluation performed with military operators who are the end-users of SIGINT analysis.
In this paper we address the question of the relationships between visualization challenges and the representation components that provide solutions to these challenges. Our approach involves extracting such relationships through an identification of the context and the components of a significant number of representations and a comparison of the result to existing theoretical studies. To make such an identification possible, we rely on a characterization of the representation context based on a thoughtful aggregation of existing characterizations of the data type, the tasks and the context of use of the representations. We illustrate our approach on a use-case with examples of a relationships extraction and of a comparison of that relationships to the theory. We believe that the establishment of such relationships makes it possible to understand the mechanisms behind the representations, in order to build a representation design recommendation tool. Such a tool will enable us to recommend the components to use in a representation, given a visualization challenge to address.
This paper presents an evaluation of HeloVis: a 3D interactive visualization that relies on immersive properties to improve user performance during SIGnal INTelligence (SIGINT) analysis. HeloVis draws on perceptive biases, highlighted by Gestalt laws, and on depth perception to enhance the recurrence properties contained in the data. In this paper, we briefly recall what is SIGINT, the challenges that it brings to visual analytics, and the limitations of state of the art SIGINT tools. Then, we present HeloVis, and we evaluate its efficiency through the results of an evaluation that we have made with civil and military operators who are the expert end-users of SIGINT analysis.
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