Creating datasets for data analysis through a cloud microservice-based architecture

Data analysis is a trending technique due to the tendency of analyzing patterns or generating knowledge in different domains. However, it is difficult to know at design time what raw data should be collected, how it is going to be analyzed or which analysis techniques will be applied to data. Service-oriented architectures can be applied to solve these problems by providing flexible and reliable architectures. In this paper, we present a microservice-based software architecture in the cloud with the aim of generating datasets to carry out data analysis. This architecture facilitates acquiring data, which may be located in a data center, distributed, or even on different devices (ubiquitous computing) due to the rise of the IoT. It provides an infrastructure over which multiple developer’ groups can work in parallel on the microservices. These microservices also provide a reliable and affordable adaptability to the lack of specific requirements in some functionalities and the fast evolution and variability of them, due to the fast changing of client needs

Optimally Storing the User Interaction in Mashup Interfaces within a Relational Database

Cross-device applications that have user interfaces managed in multiple forms of interaction are prevalent. In particular, component-based (or mashup) applications are growing in popularity due to their easiness to build customized user interfaces with pieces of information from different sources. Since the user interaction on mashup interfaces can generate a large quantity of data, which can be useful to improving the interaction and usefulness of the application, it may involve the creation of cloud infrastructures to manage the dynamic distributed user interfaces within this context. Storing the generated data from the interaction performed over the user interface can be challenging. To achieve these goals, in this paper, a relational database for storing this interaction information generated on distributed user interfaces is proposed. Thus, user interaction over heterogeneous interfaces and devices described in detail, will be easily accessible for further analysis using machine learning and data mining techniques to offer a better user experience

Growth and optical characterization of indirect-gap AlxGa1−xAs alloys

Nonintentionally doped AlxGa1−xAs layers with 0.38 x 0.84 were grown on (100) GaAs substrates by liquid phase epitaxy (LPE) under near-equilibrium conditions. The crystalline quality of the samples was studied by photoluminescence at 2 K and room temperature Raman spectroscopy. The peculiar behavior in the photoluminescence intensities of the indirect bound exciton line and the donor–acceptor pair transition is explained from the evolution of the silicon donor binding energy according to the aluminum composition. It was also possible to observe the excitonic transition corresponding to the AlxGa1−xAs/GaAs interface, despite the disorder and other factors which are normally involved when growing high-aluminum-content layers by this technique. Furthermore, Raman measurements show the quadratic variations of longitudinal optical phonon frequencies with aluminum concentration in good agreement with previous experimental results. In this work we show that high quality indirect-gap AlxGa1−xAs samples can be grown by LPE under near-equilibrium [email protected]