In-Formation Flocking: An Approach to Data Visualization Using Multi-agent Formation Behavior

摘要

This paper presents in-formation flocking, a novel information visualization technique that extends the original information flocking concept with dynamic and data-driven visual formation behavior generation. This approach extends the emergent swarming properties of a decentralized multi-agent system in order to represent complex time-varying datasets through visually-recognizable formations and motion typologies. In-formation flocking is capable of representing volatile and inherently chaotic time-varying datasets while sustaining a comprehensible representation at a global level as well as revealing more detailed patterns in subsets of the data. This paper demonstrates the capabilities of in-formation flocking to historical stock market data.