Automatically Updating a Dynamic Region Connection Calculus for Topological Reasoning

Proceedings of: Workshop on User-Centric Technologies and Applications (CONTEXTS 2011), Salamanca, Spain, April 6-8, 2011During the last years ontology-based applications have been thought without taking in account their limitations in terms of upgradeability. In parallel, new capabilities such as topological sorting of instances with spatial characteristics have been developed. Both facts may lead to a collapse in the operational capacity of this kind of applications. This paper presents an ontology-centric architecture to solve the topological relationships between spatial objects automatically. The capability for automatic assertion is given by an object model based on geometries. The object model seeks to prioritize the optimization using a dynamic data structure of spatial data. The ultimate goal of this architecture is the automatic storage of the spatial relationships without a noticeable loss of efficiency.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/ TIC-1485) and DPS2008-07029-C02-02.Publicad

Spark deployment and performance evaluation on the MareNostrum supercomputer

In this paper we present a framework to enable data-intensive Spark workloads on MareNostrum, a petascale supercomputer designed mainly for compute-intensive applications. As far as we know, this is the first attempt to investigate optimized deployment configurations of Spark on a petascale HPC setup. We detail the design of the framework and present some benchmark data to provide insights into the scalability of the system. We examine the impact of different configurations including parallelism, storage and networking alternatives, and we discuss several aspects in executing Big Data workloads on a computing system that is based on the compute-centric paradigm. Further, we derive conclusions aiming to pave the way towards systematic and optimized methodologies for fine-tuning data-intensive application on large clusters emphasizing on parallelism configurations.Peer ReviewedPostprint (author's final draft