BigVis 2022

Information Visualization is nowadays one of the cornerstones of Data Science, turning the abundance of Big Data being produced through modern systems into actionable knowledge. Indeed, the Big Data era has realized the availability of voluminous datasets that are dynamic, noisy and heterogeneous in nature. Transforming a data-curious user into someone who can access and analyze that data is even more burdensome now for a great number of users with little or no support and expertise on the data processing part. Thus, the area of data visualization, visual exploration and analysis has gained great attention recently, calling for joint action from different research areas from the HCI, Computer graphics and Data management and mining communities.

In this respect, several traditional problems from these communities such as efficient data storage, querying & indexing for enabling visual analytics, new ways for visual presentation of massive data, efficient interaction and personalization techniques that can fit to different user needs are revisited. The modern exploration and visualization systems should nowadays offer scalable techniques to efficiently handle billion objects datasets, limiting the visual response in a few milliseconds along with mechanisms for information abstraction, sampling and summarization for addressing problems related to visual information overplotting. Further, they must encourage user comprehension offering customization capabilities to different user-defined exploration scenarios and preferences according to the analysis needs. Overall, the challenge is to offer self-service visual analytics, i.e., enable data scientists and business analysts to visually gain value and insights out of the data as rapidly as possible, minimizing the role of IT-expert in the loop.

The Big Data Visual Exploration and Analytics workshop (BigVis) aims at addressing the above challenges and issues by providing a forum for researchers and practitioners to discuss, exchange, and disseminate their work. BigVis attempts to attract attention from the research areas of: Data Management & Mining, Information Visualization, Human-Computer Interaction, Machine Learning, and Computer Graphics, and highlight novel works that bridge together these communities.