Critical Evaluation of Application Porting in Mobile Platforms

Smart phone has become increasingly popular and sales numbers from the third quarter of 2010 reported that an estimated 80.5 million smart phones were sold worldwide. The statistics expresses that mobile applications have grown significantly and mobile devices have become a part of the user’s everyday life. As companies would like to see their mobile applications reaching a broader audience, the demand for porting applications is necessary before or after developing a software product. Software productions should make sure their products can be easily implemented in several environments, since it will allow the product to reach larger parts of the market in a cost-effective way. This becomes increasingly important when the target market is diverse, and is not eminently dominated by one software environment, such as the mobile market. However, the methods for making applications portable are often very unofficial and ad-hoc based with many constraints and difficulties. In this paper, we critically evaluated the current issues of mobile application porting from various angles. Then, we proposed a standard methodology with proper tools and techniques to overcome the difficulties of mobile application porting and increase the portability of mobile applications.

User behaviour analysis and personalized TV content recommendation

Nowadays, there are many channels and television (TV) programs available, and when the viewer is confronted with this amount of information has difficulty in deciding which wants to see. However, there are moments of the day that viewers see always the same channels or programs, that is, viewers have TV content consumption habits. The aim of this paper was to develop a recommendation system that to be able to recommend TV content considering the viewer profile, time and weekday. For the development of this paper, were used Design Science Research (DSR) and Cross Industry Standard Process for Data Mining (CRISP-DM) methodologies. For the development of the recommendation model, two approaches were considered: a deterministic approach and a Machine Learning (ML) approach. In the ML approach, K-means algorithm was used to be possible to combine STBs with similar profiles. In the deterministic approach the behaviors of the viewers are adjusted to a profile that will allow you to identify the content you prefer. Here, recommendation system analyses viewer preferences by hour and weekday, allowing customization of the system, considering your historic, recommending what he wants to see at certain time and weekday. ML approach was not used due to amount of data extracted and computational resources available. However, through deterministic methods it was possible to develop a TV content recommendation model considering the viewer profile, the weekday and the hour. Thus, with the results it was possible to understand which viewer profiles where the ML can be used.COMPETE: POCI-01-0145-FEDER-007043 and FCT (Fundação para a Ciência e Tecnologia) within the Project Scope: UID/CEC/00319/2013 and was developed in partnership with AlticeLab

A dynamic classification index to enhance data protection procedures in cloud-based environments

Security is one of the most challenging issues in cloud-based environments and the most important obstacle for advancement of IT-based on-demand services. Classification of data based on attributes is a challenging issues that can improve the rate of reliability and efficiency in cloud computing environments as an emerging technology. Accordingly, a dynamic index classification model has been presented in this paper to ensure data security in cloud computing environments based on their attributes. Index Classification (IC) value has been defined based on three parameters (i.e. data confidentiality, data integrity, and data availability) and various sub-parameters to classify data into 4 main groups. The evaluation procedure of the suggested model involves two main parameters: functionality, and security. Each parameter was examined by simulation processes to investigate strengths and weaknesses of this model in comparison with current models. In overall, the results show that this model has met defined demands of this research to enhance the reliability and efficiency of data protection in cloud computing environments

Cloud computing: vision, architecture and characteristics

Cloud computing is an unprecedented paradigm for hosting and delivering resources by providing on-demand services. The rapid growth of using cloud-based services in recent years is an impossible fact to be denied as it has increased the efficiency in accessing to shared pools of configurable computing resources. According to this rapid growth, it is anticipated that cloud computing will be the most important and challenging issue in IT industry. Therefore, the state-of-the-art of cloud computing has been surveyed in this paper that involves definition and essential concepts, architecture, models, deployment types, key technologies and characteristics of cloud computing

A reliable data protection model based on re-encryption concepts in cloud environments

The rapid growth of using cloud-based services in recent years is an undeniable fact as it has increased the efficiency in accessing shared pools of configurable computing resources. However, there are serious concerns about the reliability of this emerging technology and it is anticipated that cloud computing security concerns will be the most important and challenging issue in IT industry. Accordingly, a hybrid re-encryption model has been presented in this paper to ensure data security in cloud computing environments based on the concepts of index classification, time-based procedures, and attributes. Accordingly, data are classified to four main rings based on their attributes. Furthermore, a hybrid ring was established to provide a secure connection between rings by dual encryption process. This is regarding the characteristics of source and destination rings. Protected rings perform the re-encryption process according to four main parameters: time-based, un-authorized authentication, user revocation, and data owner request. In addition, the functionality, security, and scalability of the suggested model were examined by a simulation analysis to find out the strengths of this re-encryption model in comparison with current models. The analysis results show that this model has met defined demands of this research to enhance the reliability and efficiency of data protection in cloud computing environments