Towards a Data-Driven Enterprise: Supporting Collaboration across Devices in Visual Analytics
We are surrounded by novel device technologies emerging at an unprecedented pace. These devices are heterogeneous in nature—in large and small sizes with different input and sensing mechanisms. In data science, these emerging devices can promote fluid interaction to understand data, allow multiple analysts to collaborate with each other, and support analytics anywhere and anytime. Thus, synthesizing the best of human and machine intelligence. In this talk, I will present interaction models, frameworks, and platforms for collaboration across heterogeneous devices in data analytics with a focus on data visualization, the use of interactive graphics to understand and communicate data. Our Proxemic Lens technique combines interaction techniques based on proxemics and gestures for visual analysis on large wall-sized displays. Extending this technique, our conceptual framework for multi-device environments describes the roles of large and small devices, guidelines, and interactions during exploratory analysis, which were shown to improve the workflow and insights of the users. Pushing further, Vistrates is a web platform for collaborative, cross-device analytical work in both exploratory and explanatory stages. Rooted in HCI and data science, my projects incorporate (1) a strong engineering component to create new analytical systems and (2) a human-centered design component to help users effectively work across their devices on these new interactive systems, driven by real usage scenarios.Moving forward, I plan to build on this work by applying it to modern analytical enterprises in healthcare, civic development, law enforcement, and finance, which contain analysts with diverse expertise working with many devices to make decisions. I believe that with suitable interaction design and analytical tools, anyone can be an analyst. Enabling this analytical thinking in everyone is the first step to enhance modern analytical enterprises as a whole.
Sriram Karthik Badam is a Ph.D. candidate in Computer Science at the University of Maryland, College Park. He has worked with Dr. Niklas Elmqvist as a graduate assistant with research (since 2014) and teaching (since 2017) duties. He led 10+ research projects, published at top-tier HCI and visualization venues such as IEEE TVCG, IEEE InfoVis, ACM CHI, IEEE VAST, EuroVis, and ACM ITS with collaborators across the world. He is excited about emerging aspects of HCI and data science including natural interaction methods, collaborative interfaces, and human-in-the-loop AI. Before his Ph.D. studies, he received a Master of Science in computer engineering in 2014 from Purdue University, West Lafayette. Website: http://karthikbadam.com