Compositional Microservices for Immersive Social Visual Analytics

As humans, we have developed to process highly complex visual data from our surroundings. This is why data visualization and interaction is one of the quickest ways to facilitate investigation and communicate understanding. To perform visual analytics effectively at the \textit{big data} scale it is crucial that we develop an integrated processing and visualization ecosystem. However, to date, in Large High-Resolution Display (LHRD) environments the worlds of data processing and visualization remain largely disconnected. In this paper, we propose a common architectural approach to enable integrated data processing and distributed visualization via the composition of discrete microservices. Each of these microservices provides a very specific clearly-defined function, such as analyzing data, creating a visualization, sharding data or providing a synchronization source. By defining common transport, data and API formats we enable the composition of these microservices from processing raw data through to analytics, visualization and rendering. This compositionality, inspired by successful data-driven visualization frameworks provides a common platform for immersive social visual analytics.