@conference {wenskovitch2018computational, title = {The Cognitive and Computational Benefits and Limitations of Clustering for Sensemaking}, booktitle = {CHI {\textquoteright}18 Workshop on Sensemaking in a Senseless World}, year = {2018}, month = {04/2018}, address = {Montreal, QC, Canada}, abstract = {The cognitive process of sensemaking refers to acquiring, representing, and organizing information in order to understand that information. The organization component naturally supports the introduction of clusters, an important enabler for grouping objects such that similar objects are placed in the same cluster. This paper explores the benefits and limitations of introducing clusters into systems for exploratory data analysis. We consider these issues for tasks that the system may support, methods for visualizing and interacting with data in the system, and algorithms that are encoded into the system. We discuss the use of clusters in these systems with respect to cognition and computation, and we call out future areas of research in this area.}, keywords = {clustering, exploratory data analysis, interaction, sensemaking, tasks, visualization}, author = {Wenskovitch, John and Michelle Dowling and North, Chris} } @unpublished {dowling2018construction, title = {Construction and Usage of the Semantic Interaction Pipeline}, year = {2018}, pages = {1-29}, publisher = {InfoVis Lab, Virginia Tech}, type = {Technical Report}, address = {Blacksburg, VA}, abstract = {Semantic interaction techniques in visual data analytics allow users to indirectly adjust model parameters by directly manipulating the visual output of the models. Many existing tools that support semantic interaction do so with a number of similar features, including using an underlying bidirectional pipeline, using a series of statistical models, and performing inverse computations to transform user interactions into model updates. We propose a visual analytics pipeline that captures these necessary features of semantic interactions. Our flexible, multi-model, bidirectional pipeline has modular functionality to enable rapid prototyping. This enables quick alterations to the type of data being visualized, models for transforming the data, semantic interaction methods, and visual encodings. To demonstrate how this pipeline can be used, we developed a series of applications that employ semantic interactions. We also discuss how the pipeline can be used or extended for future research on semantic interactions in visual analytics.}, author = {Michelle Dowling and Wenskovitch, John and Peter Hauck and Adam Binford and Theo Long and Nicholas Polys and North, Chris} }