Managing Big Data for Scientific Visualization

TLDR

Big data challenges affect many domains, including engineering and scientific visualization, where solutions are still underdeveloped. The study proposes a structured framework to review existing big‑data management methods in engineering and scientific visualization and to chart promising future directions. Using this framework, the authors analyze prior work and apply it to develop demand‑paged segment techniques for fluid‑flow visualization. The demand‑paged segment approach improves visualization performance within the framework, illustrating its practical value.

Abstract

Many areas of endeavor have problems with big data. Some classical business applications have faced big data for some time (e.g. airline reservation systems), and newer business applications to exploit big data are under construction (e.g. data warehouses, federations of databases). While engineering and scientific visualization have also faced the problem for some time, solutions are less well developed, and common techniques are less well understood. In this section we offer some structure to understand what has been done to manage big data for engineering and scientific visualization, and to understand and go forward in areas that may prove fruitful. With this structure as backdrop, we discuss the work that has been done in management of big data, as well as our own work on demand-paged segments for fluid flow visualization.