GPU-based Streaming for Parallel Level of Detail on Massive Model Rendering

Rendering massive 3D models in real-time has long been recognized as a very challenging problem because of the limited computational power and memory space available in a workstation. Most existing rendering techniques, especially level of detail (LOD) processing, have suffered from their sequential execution natures, and does not scale well with the size of the models. We present a GPU-based progressive mesh simplification approach which enables the interactive rendering of large 3D models with hundreds of millions of triangles. Our work contributes to the massive rendering research in two ways. First, we develop a novel data structure to represent the progressive LOD mesh, and design a parallel mesh simplification algorithm towards GPU architecture. Second, we propose a GPU-based streaming approach which adopt a frame-to-frame coherence scheme in order to minimize the high communication cost between CPU and GPU. Our results show that the parallel mesh simplification algorithm and GPU-based streaming approach significantly improve the overall rendering performance

Surfacing ERP exploitation risks through a risk ontology

Purpose – The purpose of this paper is to develop a risk identification checklist for facilitating user companies to surface, organise and manage potential risks associated with the post-adoption of Enterprise Resource Planning (ERP) systems. Design/methodology/approach – A desktop study, based on the process of a critical literature review, was conducted by the researchers. The critical review focused on IS and business research papers, books, case studies and theoretical articles, etc. Findings – By systematically and critically analysing and synthesising the literature reviewed, the researchers identified and proposed a total of 40 ERP post-implementation risks related to diverse operational, analytical, organisation-wide and technical aspects. A risk ontology was subsequently established to highlight these ERP risks, as well as to present their potential causal relationships. Research limitations/implications – For researchers, the established ERP risk ontology represents a starting point for further research, and provides early insights into a research field that will become increasingly important as more and more companies progress from implementation to exploitation of ERPs. Practical implications – For practitioners, the risk ontology is an important tool and checklist to support risk identification, prevention, management and control, as well as to facilitate strategic planning and decision making. Originality/value – There is a scarcity of studies focusing on ERP post-implementation in contrast with an over abundance of studies focusing on system implementation and project management aspects. This paper aims to fill this significant research gap by presenting a risk ontology of ERP post-adoption. It represents a first attempt in producing a comprehensive model in its area. No other such models could be found from the literature reviewed