Scalable metadata environments (MDE): artistically impelled immersive environments for large-scale data exploration

Ruth G. West, Todd Margolis, Andrew Prudhomme, Jürgen P. Schulze, Iman Mostafavi, J. P. Lewis, Joachim Gossmann, Rajvikram Singh, Margaret Dolinsky, Ian E. McDowall
2014 The Engineering Reality of Virtual Reality 2014  
Scalable Metadata Environments (MDEs) are an artistic approach for designing immersive environments for large scale data exploration in which users interact with data by forming multiscale patterns that they alternatively disrupt and reform. Developed and prototyped as part of an art-science research collaboration, we define an MDE as a 4D virtual environment structured by quantitative and qualitative metadata describing multidimensional data collections. Entire data sets (e.g.10s of millions
more » ... g.10s of millions of records) can be visualized and sonified at multiple scales and at different levels of detail so they can be explored interactively in real-time within MDEs. They are designed to reflect similarities and differences in the underlying data or metadata such that patterns can be visually/aurally sorted in an exploratory fashion by an observer who is not familiar with the details of the mapping from data to visual, auditory or dynamic attributes. While many approaches for visual and auditory data mining exist, MDEs are distinct in that they utilize qualitative and quantitative data and metadata to construct multiple interrelated conceptual coordinate systems. These "regions" function as conceptual lattices for scalable auditory and visual representations within virtual environments computationally driven by multi-GPU CUDA-enabled fluid dyamics systems. MAKING THE ABSTRACT EXPERIENTIAL As we race towards a "digital universe" of 40 trillion gigabytes by 2020 that encompasses the full scope of human endeavor from science to the economy, humanities, telecommunication and the arts, we are challenged not only by its size, but its ephemerality[1]. We must also come to terms with its incompleteness and our inability to effectively search, aggregate and cross-reference its myriad elements [2] . While data is often considered a resource, a raw material that can be manipulated and refined along a continuum from information-to-knowledge-to-wisdom[3] fundamentally there is, and may always be, a gap between the data, the underlying phenomena it represents, and the meaning ascribed to it. One can devise rules to assign meaning to the output of rule-based systems, yet the output itself must be interpreted in turn, leading to an infinite regress [4] . Generating, storing, accessing, representing and interpreting data also necessarily involve subjective choices. This is not always acknowledged nor made explicit. Through choices such as what to sample, the sampling resolution, file formats, what gets discarded versus stored when the data is too large to retain all of it, or the database schemas utilized in managing it, unspoken framing narratives arise that encode agreed upon assumptions about what the creators think they will find in the data, what they think they can know. Framing narratives also arise from our choice of representational schemas, statistics, and algorithms, displays, interaction technologies, and metaphors. Recording complex phenomena from the personal to the global as digital data with technologies that often transcend the capacities of our senses (E.g. fitness wearables, terrestrial observatories, ultra-high resolution sub-cellular imaging, databases of consumer transactions, genomics etc.) creates digital repositories with information content rich enough to produce an enormous number of observations. Yet, an individual or given domain expert can only generate a limited number of interpretations, mostly guided by their specific expertise and the respective framing narratives of data creation and representation. These observations combined with the emergence of ArtScience as an approach for creating new ways of seeing and knowing through hybrid strategies[5] motivate our pursuit of aesthetic and artistically-impelled approaches to support intuitive exploration of large data collections that transcend disciplinary boundaries and individual
doi:10.1117/12.2038673 fatcat:tsu7ondg3fflvebn5w7auralzi