EuroVisShort2019
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Browsing EuroVisShort2019 by Author "Chang, Remco"
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Item Defining an Analysis: A Study of Client-Facing Data Scientists(The Eurographics Association, 2019) Mosca, Abigail; Robinson, Shannon; Clarke, Meredith; Redelmeier, Rebecca; Coates, Sebastian; Cashman, Dylan; Chang, Remco; Johansson, Jimmy and Sadlo, Filip and Marai, G. ElisabetaAs the sophistication of data analyses increases many subject matter experts looking to make data-driven decisions turn to data scientists to help with their data analysis needs. These subject matter experts may have little to no experience in data analysis, and may have little to no idea of what exactly they need to support their decision making. It is up to data scientists to determine the exact analysis needs of these clients before they can run an analysis. We call this step of the analysis process initialization and define it as: translating clients' broad, high-level questions into analytic queries. Despite the fact that this can be a very time consuming task for data scientists, few visualization tools exist to support it. To provide guidance on how future tools may fill this gap, we conducted 14 semi-structured interviews with client-facing data scientists in an array of fields. In analyzing interviews we find data scientists generally employ three methods for initialization: working backwards, probing, and recommending. We discus existing techniques that share synergy with each of these methods and could be leveraged in the design of future visualization tools to support initialization.Item The Human User in Progressive Visual Analytics(The Eurographics Association, 2019) Micallef, Luana; Schulz, Hans-Jörg; Angelini, Marco; Aupetit, Michaël; Chang, Remco; Kohlhammer, Jörn; Perer, Adam; Santucci, Giuseppe; Johansson, Jimmy and Sadlo, Filip and Marai, G. ElisabetaThe amount of generated and analyzed data is ever increasing, and processing such large data sets can take too long in situations where time-to-decision or fluid data exploration are critical. Progressive visual analytics (PVA) has recently emerged as a potential solution that allows users to analyze intermediary results during the computation without waiting for the computation to complete. However, there has been limited consideration on how these techniques impact the user. Based on discussions from a Dagstuhl seminar held in October 2018, this paper characterizes PVA users by their common roles, their main tasks, and their distinct focus of analysis. It further discusses cognitive biases that play a particular role in PVA. This work will help PVA visualization designers in devising systems that are tailored for their specific target users and their characteristics.