Use case driven development methodologies put use cases at the center of the software development process. However, in order to support automated development and analysis, use cases need to be appropriately formalized. This will also help guarantee consistency between requirements specifications and the developed solutions. Formal methods tend to suffer from take up issues, as they are usually hard to accept by industry. In this context, it is relevant not only to produce languages and approaches to support formalization, but also to perform their validation. In previous works we have developed an approach to formalize use cases resorting to ontologies. In this paper we present the validation of one such approach. Through a three stage study, we evaluate the acceptance of the language and supporting tool. The first stage focusses on the acceptance of the process and language, the second on the support the tool provides to the process, and finally the third one on the tool's usability aspects. Results show test subjects found the approach feasible and useful and the tool easy to use.

The paper describes a model that includes an explicit description of the information resources that are assumed to guide use, enabling a focus on properties of "plausible interactions". The information resources supported by an interactive system should be designed to encourage the correct use of the system. These resources signpost a user's interaction, helping to achieve desired goals. Analysing assumptions about information resource support is particularly relevant when a system is safety critical that is when interaction failure consequences could be dangerous, or walk-up-and-use where interaction failure may lead to reluctance to use with expensive consequences. The paper shows that expressing these resource constraints still provides a wider set of behaviours than would occur in practice. A resource may be more or less salient at a particular stage of the interaction and as a result potentially overlooked. For example, the resource may be accessible but not used because it does not seem relevant to the current goal. The paper describes how the resource framework can be augmented with additional information about the salience of the assumed resources. A medical device that is in common use in many hospitals is used as illustration.

Window functions are a sub-class of analytical operators that allow data to be handled in a derived view of a given relation, while taking into account their neighboring tuples. Currently, systems bypass parallelization opportunities which become especially relevant when considering Big Data as data is naturally partitioned.
We present a shuffling technique to improve the parallel execution of window functions when data is naturally partitioned when the query holds a partitioning clause that does not match the natural partitioning of the relation. We evaluated this technique with a non-cumulative ranking function and we were able to reduce data transfer among parallel workers in 85% when compared to a naive approach.

In the pursuit of highly available systems, storage systems began offering eventually consistent data models. These models are suitable for a number of applications but not applicable for all. In this paper we discuss a system that can offer a eventually consistent data model but can also, when needed, offer a strong consistent one.

This work describes a formalization effort, using the Coq proof assistant, of fundamental results related to the classical theory of context-free grammars and languages. These include closure properties (union, concatenation and Kleene star), grammar simplification (elimi- nation of useless symbols, inaccessible symbols, empty rules and unit rules), the existence of a Chomsky Normal Form for context-free grammars and the Pumping Lemma for context-free languages. The result is an important set of libraries covering the main results of context-free language theory, with more than 500 lemmas and theorems fully proved and checked. This is probably the most comprehensive formalization of the classical context-free language theory in the Coq proof assistant done to the present date, and includes the important result that is the formalization of the Pumping Lemma for context-free languages.

A key component in large scale distributed analytical processing is shuffling, the distribution of data to multiple nodes such that the computation can be done in parallel. In this paper we describe the design and implementation of a communication middleware to support data shuffling for executing multi-stage analytical processing operations in parallel. The middleware relies on RDMA (Remote Direct Memory Access) to provide basic operations to asynchronously exchange data among multiple machines. Experimental results show that the RDMA-based middleware developed can provide a 75 % reduction of the costs of communication operations on parallel analytical processing tasks, when compared with a sockets middleware.