Optimizing I/O for Big Array Analytics

Big array analytics is becoming indispensable in answering important scientific and business questions. Most analysis tasks consist of multiple steps, each making one or multiple passes over the arrays to be analyzed and generating intermediate results. In the big data setting, I/O optimization is a key to efficient analytics. In this paper, we develop a framework and techniques for capturing a broad range of analysis tasks expressible in nested-loop forms, representing them in a declarative way, and optimizing their I/O by identifying sharing opportunities. Experiment results show that our optimizer is capable of finding execution plans that exploit nontrivial I/O sharing opportunities with significant savings.Comment: VLDB201

Standardization of Rational Bézier Surfaces

National audienceThe sufficient and necessary condition for the existence of linear Möbius transformations that can standardize the rational Bézier surfaces is given based on Möbius reparameterization theorem. To obtain the standard form of an arbitrary cubic rational Bézier surface, we then present a quadratic reparameterization algorithm to reparameterize the surface so that all the corner weights of the surface are equal to one. Examples are included to show the performance of the new method.