The everyday consumer is inundated with applications powered by machine learning. But in an ordinary day, do we encounter situations and choices which could also benefit from machine learning for which there is no specific tool invented yet? We describe scenarios where people without any machine learning background could find it useful to define their own solution which uses machine learning. Although machine learning is becoming ubiquitous, the average person is unaware of the steps involved. This abstraction makes sense, in many situations, such as traffic predictions, it is not necessary for the driver to know what machine learning algorithm is running. However, we consider examples where knowing how to incorporate machine learning into a problem would assist in decision making. We propose a workflow with operations leading to a final application. There are several challenges here, namely, the average consumer is not expected to have a mathematical background, nor is expected to acquire any additional background. To achieve this new utility, we use a visual analytic pipeline which integrates machine learning and the person. We use the IEEE VAST 2018 Challenge as a case study in which the user steps through the workflow. Finally, we envision the resulting application.
Many research and development activities for scientific data analysis have focused on scalability challenges and data-driven features. Conversely, data visualization that focuses on models requiring human interaction rarely involve practical and largescale scientific data analysis. Therefore, a gap exists between interactive data visualization and scientific data analysis applications. In this paper, we present a design study of interactive data visualization to support scientists who visually analyze data from neutron scattering experiments. This study was conducted in multiple phases: 1) problem characterization; 2) initial design and formative evaluation; and 3) iterative design. We characterize the problems and the design requirements for the analysis of the specific physical science data. We discuss the design, development, evaluation of our visual analytics tool and as well as our iterative developments with physical scientists. We show how to bridge the gap between the two disciplines uncovering new potential to solve their challenges in this design study. We focus on a specific challenge, finding an optimal color mapping, which plays a critical role in neutron scattering science and is broadly applicable to other scientific domains. To address the challenge, we propose two interactive visualization techniques: a dynamic color scale bar (DCSB) and a multi-scale histogram (MSH)