Abstract
Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions.
Highlights
With data growing in size and complexity, and analysis methods getting more sophisticated and computationally intensive, the idea of Progressive Visual Analytics (PVA) [1,2] becomes increasingly appealing
PVA follows this conceptual flow, yet is more flexible and less sequential in the combination of these four steps, as it already allows visualizing and interacting while some data are still being prepared and while the computational process is still running. This flexibility is reflected by PVA requirements that explicitly address the interplay of the different aspects of PVA—e.g., the implications of the processing on the interaction facilities, which need to provide for managing the progression (RT36), as well as the implications of the interaction on the processing, which must adhere to the respective time constraints (RT32)
The use case at hand is taken from Angelini et al, [32]. It supports explorative decision making for marketing strategies of Telecom Italia Mobile (TIM). It seeks to find a subset of the 110 Italian provinces that leads to an optimal Return of Investment (RoI) as captured by a given objective function
Summary
With data growing in size and complexity, and analysis methods getting more sophisticated and computationally intensive, the idea of Progressive Visual Analytics (PVA) [1,2] becomes increasingly appealing. PVA yields partial results of increasing completeness or approximative results of increasing correctness, respectively This is useful in a wide range of visual analytics scenarios:. As the field of PVA is still emerging, the presented collection of publications as well as the characterization and recommendations derived from them should not be mistaken for a survey that aims to wrap-up a mature field of research. Instead, they should rather be understood as an overview of the current understanding of PVA that bundles the research results achieved so far and serves as a stepping stone for new ones.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
