An image retrieval framework for distributed datacenters
As massive data is stored in cloud datacenters, it is necessary to effectively locate interest data in such a distributed environment. However, since it is difficult to create a visual vocabulary due to the lack of global information, most existing systems of Content Based Image Retrieval (CBIR) only focus on global image features. In this paper, we propose a novel image retrieval framework, which efficiently incorporates the bag-of-visual-word model into Distributed Hash Tables (DHTs). Its key idea is to establish visual words for local image features by exploiting the merit of Locality Sensitive Hashing (LSH), so that similar image patches are most likely gathered into the same nodes without the knowledge of any global information. Extensive experimental results demonstrate that our approach yields high accuracy at very low cost, while keeping the load balanced.
Summary In the pilot study, a new geomodeling technology is presented for developing 3D heterogonous geomodels for Athabasca McMurray (MCMR) oil sands. The geomodels were used to simulate SAGD thermal recovery. Both conventional SAGD completions and SAGD with ICD completions were tested. The simulation results show that for a heterogeneous Athabasca McMurray oil sand reservoir, the ICD completion technology enhances oil production and reduces steam consumption. As a result, the economics of SAGD recovery from the heterogeneous sands is improved.