Over the last ten years, a new field of research has emerged: Big Data Analytics (BDA). Massively distributed systems offer new tools for exploring large datasets. In parallel, a steady increase in computing power and available training data has enabled the field of Artificial Intelligence (AI) to gain critical mass. The challenge of managing, investigating, and visualizing big datasets is not new in the fields of Science, Technology, Engineering and Mathematics (STEM). Recent developments in BDA offer a new set of tools for overcoming these challenges, however, there are significant challenges that arise from the structural differences between most STEM data and the unstructured textual data typical in classical Big Data applications. In order to address these challenges, we propose using Locally Refined (LR-) spline data modelling to turn Big Data into Smart Data. In early implementations of LR-spline algorithms in 2D and 3D, we have seen their potential as compact interactive models for visual and quantitative analytics on big datasets, well suited for hardware-accelerated interrogation and visualization. By spatial tiling and stitching, we have an extremely versatile and parallelizable approach, well suited for Big Data infrastructures. We can therefore include time and other relevant variables in a compact, interactive, multi-scale, higher order, locally refined model. However, substantial theoretical developments are needed before this vision of LR-spline modelling of Big Data can be realized. ANALYST will provide the research platform to bring the theoretical foundation of LR-splines up to a level where their full potential can be explored, combining BDA and AI to provide advanced analytics on the LR-spline model. While the focus will be on data from applications in the STEM fields, the resulting algorithms have a wider applicability, providing highly scalable complex modelling tools for Big Data Analytics.
Project leader: Tor Dokken
Category: Teknisk-industrielle institutter
Institution: Mathematics and Cybernetics