In this paper, we investigate the problem of providing scalability to data Extraction, Transformation, Load and Querying (ETL+Q) process of data warehouses. In general, data loading, transformation and integration are heavy tasks that are performed only periodically. Parallel architectures and mechanisms are able to optimize the ETL process by speedingup each part of the pipeline process as mor...

Introduction: Brain tumors such as glioma are among the most aggressive lesions, which result in a very short life expectancy in patients. Image segmentation is highly essential in medical image analysis with applications, particularly in clinical practices to treat brain tumors. Accurate segmentation of magnetic resonance data is crucial for diagnostic purposes, planning surgical treatments, a...

This paper describes a novel design environment for cognitive systems tailored to the needs of flexibility, speed and transparence demanded in numerous application domains as e.g., automated visual inspection for quality control, mechatronics, medical applications, and other cognitive tasks. Our QuickCog design environment combines methods from statistical pattern recognition, neural networks, ...

An important goal of the Fx compiler project at Carnegie Mellon is to support the use of high level programming languages like High Performance For-tran for applications in image processing, signal processing and computer vision. Such applications typically require fast processing of streams of relatively small data sets. Fx introduced the notion of task parallelism within a data parallel langu...