Niall H. Robinson | Searcharxiv

arXiv2020

Seven Principles for Effective Scientific Big-DataSystems

We should be in a golden age of scientific discovery, given that we have more data and more compute power available than ever before, plus a new generation of algorithms that can learn effectively from data. But paradoxically, in many data-driven fields, the eureka moments are becoming increasingly rare. Scientists are struggling to keep pace with the explosion in the volume and complexity of scientific data. We describe here a few simple architectural principles that we believe are essential in order to create effective, robust, and flexible platforms that make the best use of emerging technology to deal with the exponential growth of scientific data.

cs.DC↗

arXiv2016

A Practical Approach to Spatiotemporal Data Compression

Datasets representing the world around us are becoming ever more unwieldy as data volumes grow. This is largely due to increased measurement and modelling resolution, but the problem is often exacerbated when data are stored at spuriously high precisions. In an effort to facilitate analysis of these datasets, computationally intensive calculations are increasingly being performed on specialised remote servers before the reduced data are transferred to the consumer. Due to bandwidth limitations, this often means data are displayed as simple 2D data visualisations, such as scatter plots or images. We present here a novel way to efficiently encode and transmit 4D data fields on-demand so that they can be locally visualised and interrogated. This nascent "4D video" format allows us to more flexibly move the boundary between data server and consumer client. However, it has applications beyond purely scientific visualisation, in the transmission of data to virtual and augmented reality.

cs.MM↗