Beyond visualization of big data: a multi-stage data exploration approach using visualization, sonification, and storification

摘要

As the sheer volume of data grows exponentially, it becomes increasingly difficult for existing visualization techniques to keep pace. The sonification field attempts to address this issue by enlisting our auditory senses to detect anomalies or complex events that are difficult to detect via visualization alone. Storification attempts to improve analyst understanding by converting data streams into organized narratives describing the data at a higher level of abstraction than the input stream that they area derived from. While these techniques hold a great deal of promise, they also each have a unique set of challenges that must be overcome. Sonification techniques must represent a broad variety of distributed heterogeneous data and present it to the analyst/listener in a manner that doesn't require extended listening - as visual "snapshots" are useful but auditory sounds only exist over time. Storification still faces many human-computer interface (HCI) challenges as well as technical hurdles related to automatically generating a logical narrative from lower-level data streams. This paper proposes a novel approach that utilizes a service oriented architecture (SOA)-based hybrid visualization/ sonification / storification framework to enable distributed human-in-the-loop processing of data in a manner that makes optimized usage of both visual and auditory processing pathways while also leveraging the value of narrative explication of data streams. It addresses the benefits and shortcomings of each processing modality and discusses information infrastructure and data representation concerns required with their utilization in a distributed environment. We present a generalizable approach with a broad range of applications including cyber security, medical informatics, facilitation of energy savings in "smart" buildings, and detection of natural and man-made disasters.