Context Driven Concept Based Image Retrieval

Several semantic image search schemes have been recently proposed to retrieve images from the web. However, the query context is regularly ignored in these techniques and hence, many of the returned images are not adequately relevant. In this paper, we make use of context to further confine the outcome of the semantic search engines. For this purpose, we propose a hybrid search engine which utilizes concept and context for retrieving precise results. In the proposed model, an ontology is exploited for annotating images and accomplishing search process in the semantic level. Furthermore, the query of the user is modified with the concepts available in the ontology. Next, we make use of search context of the user and augment the query with the information extracted from the user’s context to additionally eliminate irrelevant results. Experimental results show that the combination of concept and context is effective in retrieving and presenting the most relevant results to the user

Parallelism Exploration in B2C and B2B Systems

As the e-commerce sites are being more secure and reliable in recent years and the number of transactions is rising rapidly, parallelism can help us to reduce response time and increase throughput for e-commerce transactions. This paper will investigate parallelism in on-line transaction processing. It aims to specify those aspects of e-commerce transactions that would profit from parallel processing and analyze current parallel processing techniques to determine those which can be used for e-commerce transactions. The parallel processing techniques proposed in this paper can be easily applied to B2C and B2B on-line transaction processing. Although some parallel implementations of databases have been proposed, to the best of our knowledge, parallel implementations of on-line transaction processing specific to e-commerce are rarely existed