TY - CONF T1 - Explainable Interactive Projections For Image Data T2 - Advances in Visual Computing: 17th International Symposium, ISVC 2022, San Diego, CA, USA, October 3–5, 2022, Proceedings, Part I Y1 - 2022 A1 - Huimin Han A1 - Faust, Rebecca A1 - Norambuena, Brian Felipe Keith A1 - Prabhu, Ritvik A1 - Smith, Timothy A1 - Song Li A1 - North, Chris KW - Explainable AI KW - Image data KW - Interactive dimension reduction KW - Semantic interaction AB - Making sense of large collections of images is difficult. Dimension reductions (DR) assist by organizing images in a 2D space based on similarities, but provide little support for explaining why images were placed together or apart in the 2D space. Additionally, they do not provide support for modifying and updating the 2D space to explore new relationships and organizations of images. To address these problems, we present an interactive DR method for images that uses visual features extracted by a deep neural network to project the images into 2D space and provides visual explanations of image features that contributed to the 2D location. In addition, it allows people to directly manipulate the 2D projection space to define alternative relationships and explore subsequent projections of the images. With an iterative cycle of semantic interaction and explainable-AI feedback, people can explore complex visual relationships in image data. Our approach to human-AI interaction integrates visual knowledge from both human mental models and pre-trained deep neural models to explore image data. We demonstrate our method through examples with collaborators in agricultural science. JF - Advances in Visual Computing: 17th International Symposium, ISVC 2022, San Diego, CA, USA, October 3–5, 2022, Proceedings, Part I PB - Springer-Verlag CY - Berlin, Heidelberg SN - 978-3-031-20712-9 UR - https://doi.org/10.1007/978-3-031-20713-6_6 ER - TY - JOUR T1 - Interactive Visual Analytics for Sensemaking with Big Text JF - Big Data Research Y1 - 2019 A1 - Michelle Dowling A1 - Nathan Wycoff A1 - Brian Mayer A1 - Wenskovitch, John A1 - Leman, Scotland A1 - House, Leanna A1 - Nicholas Polys A1 - North, Chris A1 - Peter Hauck KW - Big data KW - interactive visual analytics KW - Semantic interaction KW - text analytics KW - Topic modeling KW - visualization AB - Analysts face many steep challenges when performing sensemaking tasks on collections of textual information larger than can be reasonably analyzed without computational assistance. To scale up such sensemaking tasks, new methods are needed to interactively integrate human cognitive sensemaking activity with machine learning. Towards that goal, we offer a human-in-the-loop computational model that mirrors the human sensemaking process, and consists of foraging and synthesis sub-processes. We model the synthesis loop as an interactive spatial projection and the foraging loop as an interactive relevance ranking combined with topic modeling. We combine these two components of the sensemaking process using semantic interaction such that the human's spatial synthesis actions are transformed into automated foraging and synthesis of new relevant information. Ultimately, the model's ability to forage as a result of the analyst's synthesis activities makes interacting with big text data easier and more efficient, thereby facilitating analysts' sensemaking ability. We discuss the interaction design and theory behind our interactive sensemaking model. The model is embodied in a novel visual analytics prototype called Cosmos in which analysts synthesize structure within the larger corpus by directly interacting with a reduced-dimensionality space to express relationships on a subset of data. We then demonstrate how Cosmos supports sensemaking tasks with a realistic scenario that investigates the affect of natural disasters in Adelaide, Australia in September 2016 using a database of over 30,000 news articles. VL - 16 UR - http://www.sciencedirect.com/science/article/pii/S2214579618302995 ER - TY - CONF T1 - SIRIUS: Dual, Symmetric, Interactive Dimension Reductions T2 - 2018 IEEE Conference on Visual Analytics Science and Technology (VAST) Y1 - 2018 A1 - Michelle Dowling A1 - Wenskovitch, John A1 - J.T. Fry A1 - Leman, Scotland A1 - House, Leanna A1 - North, Chris KW - attribute projection KW - dimension reduction KW - exploratory data analysis KW - observation projection KW - Semantic interaction AB - Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights. JF - 2018 IEEE Conference on Visual Analytics Science and Technology (VAST) ER - TY - CONF T1 - Observation-Level Interaction with Clustering and Dimension Reduction Algorithms T2 - Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics Y1 - 2017 A1 - Wenskovitch, John A1 - North, Chris KW - data clustering KW - Observation-Level Interaction (OLI) KW - Semantic interaction KW - sensemaking KW - Visual Analytics JF - Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics T3 - HILDA'17 PB - ACM CY - New York, NY, USA SN - 978-1-4503-5029-7 UR - http://doi.acm.org/10.1145/3077257.3077259 ER - TY - CONF T1 - Event-Based Text Visual Analytics T2 - VAST Challenge 2014 Y1 - 2014 A1 - Wang, Ji A1 - Lauren Bradel A1 - North, Chris KW - event extraction KW - Semantic interaction KW - sensemaking KW - topic modelling AB - We present an event-based approach for solving a directed sensemaking task in which we combine powerful information foraging tools with intuitive synthesis spaces to solve the VAST 2014 Mini-Challenge 1 (MC1). A combination of student-created and commericially available software are used to solve various aspects of the scenario. In addition to applying entitiy extraction and topic modelling, we enable the user to explore a large dataset using multi-model semantic interaction, which infers analytical reasoning from user actions to augment the data spatialization and determine what information should be presented and suggested to the user. Additionally, we visualize extracted topics using Tableau to construct a timeline of events surrounding the questions posed by the challen JF - VAST Challenge 2014 CY - Paris, France ER - 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 -