Flexible Framework for Real-Time Embedded Systems Based on Mobile Cloud Computing Paradigm

The development of applications as well as the services for mobile systems faces a varied range of devices with very heterogeneous capabilities whose response times are difficult to predict. The research described in this work aims to respond to this issue by developing a computational model that formalizes the problem and that defines adjusting computing methods. The described proposal combines imprecise computing strategies with cloud computing paradigms in order to provide flexible implementation frameworks for embedded or mobile devices. As a result, the imprecise computation scheduling method on the workload of the embedded system is the solution to move computing to the cloud according to the priority and response time of the tasks to be executed and hereby be able to meet productivity and quality of desired services. A technique to estimate network delays and to schedule more accurately tasks is illustrated in this paper. An application example in which this technique is experimented in running contexts with heterogeneous work loading for checking the validity of the proposed model is described.

Towards a model-driven engineering approach of data mining

Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

MDD vs. traditional software development: a practitioner’s subjective perspective

Context: Today’s project managers have a myriad of methods to choose from for the development of software applications. However, they lack empirical data about the character of these methods in terms of usefulness, ease of use or compatibility, all of these being relevant variables to assess the developer’s intention to use them. Objective: To compare three methods, each following a different paradigm (Model-Driven, Model-Based and Code-Centric) with respect to their adoption potential by junior software developers engaged in the development of the business layer of a Web 2.0 application. Method: We have conducted a quasi-experiment with 26 graduate students of the University of Alicante. The application developed was a Social Network, which was organized around a fixed set of modules. Three of them, similar in complexity, were used for the experiment. Subjects were asked to use a different method for each module, and then to answer a questionnaire that gathered their perceptions during such use. Results: The results show that the Model-Driven method is regarded as the most useful, although it is also considered the least compatible with previous developers’ experiences. They also show that junior software developers feel comfortable with the use of models, and that they are likely to use them if the models are accompanied by a Model-Driven development environment. Conclusions: Despite their relatively low level of compatibility, Model-Driven development methods seem to show a great potential for adoption. That said, however, further experimentation is needed to make it possible to generalize the results to a different population, different methods, other languages and tools, different domains or different application sizes.

Adjustable compression method for still JPEG images

There are a large number of image processing applications that work with different performance requirements and available resources. Recent advances in image compression focus on reducing image size and processing time, but offer no real-time solutions for providing time/quality flexibility of the resulting image, such as using them to transmit the image contents of web pages. In this paper we propose a method for encoding still images based on the JPEG standard that allows the compression/decompression time cost and image quality to be adjusted to the needs of each application and to the bandwidth conditions of the network. The real-time control is based on a collection of adjustable parameters relating both to aspects of implementation and to the hardware with which the algorithm is processed. The proposed encoding system is evaluated in terms of compression ratio, processing delay and quality of the compressed image when compared with the standard method.