upsurges the capabilities of the hardware resources by optimal and shared utilization. ud provid tion [78]. omers to p extraordinary expertise pertaining to the cloud specific technologies [5]. The management of the technology and s has moved from user to the service provider’s end [5].

Since the inception of cloud computing in mid 2000, academic groups and industry vendors have developed a number of Cloud Re-source Orchestration Frameworks (CROFs) for simplifying the application management. The CROF aids software engineers, scien-tists, and infrastructure administrators to migrate and manage their in-house applications to cloud environments. Despite the higher level of technical maturity of such CROFs, there is clear lack of study that can help cloud application administrators in understand-ing and analyzing the features of CROFs against a common set of concepts and dimensions. Therefore, this study presents a set of generic technical dimensions for clearly analyzing the capabilities of the overriding CROFs. A concise survey and classification of most prominent research work is also presented.

Copyright © 2015 L. Cardoso et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information. 1.