TY - JOUR T1 - The human is the loop: new directions for visual analytics JF - Journal of Intelligent Information Systems Y1 - 2014 A1 - Endert, Alex A1 - Hossain, M. Shahriar A1 - Ramakrishnan, Naren A1 - North, Chris A1 - Fiaux, Patrick A1 - Andrews, Christopher KW - clustering KW - Semantic interaction KW - Spatialization KW - Storytelling KW - Visual Analytics AB - Visual analytics is the science of marrying interactive visualizations and analytic algorithms to support exploratory knowledge discovery in large datasets. We argue for a shift from a ‘human in the loop’ philosophy for visual analytics to a ‘human is the loop’ viewpoint, where the focus is on recognizing analysts’ work processes, and seamlessly fitting analytics into that existing interactive process. We survey a range of projects that provide visual analytic support contextually in the sensemaking loop, and outline a research agenda along with future challenges. PB - Springer US VL - 43 ER - TY - CONF T1 - The semantics of clustering: analysis of user-generated spatializations of text documents T2 - Proceedings of the International Working Conference on Advanced Visual Interfaces Y1 - 2012 A1 - Endert, Alex A1 - Fox, Seth A1 - Maiti, Dipayan A1 - Leman, Scotland A1 - North, Chris KW - clustering KW - text analytics KW - Visual Analytics KW - visualization AB - Analyzing complex textual datasets consists of identifying connections and relationships within the data based on users' intuition and domain expertise. In a spatial workspace, users can do so implicitly by spatially arranging documents into clusters to convey similarity or relationships. Algorithms exist that spatialize and cluster such information mathematically based on similarity metrics. However, analysts often find inconsistencies in these generated clusters based on their expertise. Therefore, to support sensemaking, layouts must be co-created by the user and the model. In this paper, we present the results of a study observing individual users performing a sensemaking task in a spatial workspace. We examine the users' interactions during their analytic process, and also the clusters the users manually created. We found that specific interactions can act as valuable indicators of important structure within a dataset. Further, we analyze and characterize the structure of the user-generated clusters to identify useful metrics to guide future algorithms. Through a deeper understanding of how users spatially cluster information, we can inform the design of interactive algorithms to generate more meaningful spatializations for text analysis tasks, to better respond to user interactions during the analytics process, and ultimately to allow analysts to more rapidly gain insight. JF - Proceedings of the International Working Conference on Advanced Visual Interfaces T3 - AVI '12 PB - ACM CY - New York, NY, USA SN - 978-1-4503-1287-5 UR - http://doi.acm.org/10.1145/2254556.2254660 ER -