Many web computing systems are running real time database services where their information change continuously and expand incrementally. In this context, web data services have a major role and draw significant improvements in monitoring and controlling the information truthfulness and data propagation. Currently, web telemedicine database services are of central importance to distributed systems. However, the increasing complexity and the rapid growth of the real world healthcare challenging applications make it hard to induce the database administrative staff. In this paper, we build an integrated web data services that satisfy fast response time for large scale Tele-health database management systems. Our focus will be on database management with application scenarios in dynamic telemedicine systems to increase care admissions and decrease care difficulties such as distance, travel, and time limitations. We propose three-fold approach based on data fragmentation, database websites clustering and intelligent data distribution. This approach reduces the amount of data migrated between websites during applications' execution; achieves cost-effective communications during applications' processing and improves applications' response time and throughput. The proposed approach is validated internally by measuring the impact of using our computing services' techniques on various performance features like communications cost, response time, and throughput. The external validation is achieved by comparing the performance of our approach to that of other techniques in the literature. The results show that our integrated approach significantly improves the performance of web database systems and outperforms its counterparts.

Ultrasound imaging is one of the most widely used medical imaging modalities for detecting breast cancer. However, the accuracy of diagnosing the tumors in breast ultrasound (BUS) images might vary based on the experience level of the radiologist. Content-based image retrieval (CBIR) systems can be employed to improve the diagnosis accuracy by providing the radiologist with BUS images of previous, clinically relevant cases. In this study, a CBIR system is developed based on deep learning technology to support the diagnosis of BUS images. In particular, each query BUS image submitted to the system is analyzed using a custom-made convolutional autoencoder (CAE) to extract a latent features vector that represents the image. An important advantage of the proposed CAE is its ability to extract the latent features vector without the need to localize or outline the tumor. The latent features vector of the query image is analyzed using a similarity measure to identify and retrieve the most relevant BUS images from a reference BUS image database. Finally, the retrieved BUS images are displayed to the radiologist. The performance of the proposed CBIR system has been evaluated using a set of BUS images that includes benign and malignant breast tumors. The results reported in this study suggest the feasibility of employing the proposed CBIR system to improve the diagnosis of BUS images.

The need for effective approaches to handle big data that is characterized by its large volume, different types, and high velocity is vital and hence has recently attracted the attention of several research groups. This is especially the case when traditional data processing techniques and capabilities proved to be insufficient in that regard. Another aspect that is equally important while processing big data is its security, as emphasized in this paper. Accordingly, we propose to process big data in two different tiers. The first tier classifies the data based on its structure and on whether security is required or not. In contrast, the second tier analyzes and processes the data based on volume, variety, and velocity factors. Simulation results demonstrated that using classification feedback from a MPLS/GMPLS core network proved to be key in reducing the data evaluation and processing time.