Database evolution and data provenance are two closely related research fields. On the one hand, the registry (via provenance) of the schema evolution allows the maintenance of its version record. On the other hand, the origin of the data (i.e., its provenance) will always be affected by modifications (i.e., the evolution) in the schema on which they are based. Despite these interrelationships, there are few works in the literature that have proposed advances in that direction. In particular, to the best of our knowledge, there is no research that has resulted in a general and interoperable solution to the problem of managing database evolution using provenance. In this article, we present PROV-IDEA: a PROV-Interoperable Database Evolution Approach. This is a proposal that allows the simultaneous management of the provenance of schemas (of relational databases) and data, using the PROV standard as a way to guarantee interoperability. Furthermore, it is an adaptable and expandable approach (by using PROV templates), which allows a non-intrusive and seamless integration with existing applications, as well as different aspects of provenance information generation. These properties are demonstrated in the article by presenting a proof of concept built on top of a third-party relational database evolution tool.

Key Performance Indicators (KPIs) are becoming essential elements for measuring business performance. In recent years, KPIs management has been the subject to sustained interests for researchers and practitioners alike, deriving into a large research corpus of approaches addressing aspects in matters as varied as modelling, maintenance or expressiveness of KPIs. In particular, since both businesses and processes have to be adapted to ever-changing requirements, the KPIs that measure their performance must evolve accordingly. However, based on a previous review of the literature, we found that little attention has been paid to the provision of mechanisms to manage KPIs evolution. Our long-term research goal is to provide a fully proposal for supporting KPIs evolution management. In this position paper, we present the first ideas of a conceptual framework for addressing this issue, proposing a pattern-driven KPI evolution specification and a KPI evolution metamodel made up of two interconnected views. Our proposal is general enough to be applied regardless of the specific KPIs management approach being used.

Software engineers must be able to manage complex projects, so that skills such as teamwork, leadership or initiative are critical to their successful development. Because of this, it is fundamental that the learning of software engineering as an academic discipline provides solid links between theory and practice. Educational frameworks such as those derived from the European Higher Education Area state that student-centered approaches are a useful tool for achieving these objectives. In this context, we present a project-based learning (PBL) experience report in a software engineering program of a Spanish university. The experience is based on the formation of small heterogeneous teams, which face the initial phases of a software methodology during the development of a project close to a real one. Through a strategy of role rotation and documentation transfer, all students perform different tasks and face different challenges throughout the project. Summative assessment is also adopted, considering not only teacher ratings but also students' peer assessment. The results prove the positive effect of using PBL to improve the training of students in acquiring different skills as future software engineers.

In a competitive market, business process improvement is a requirement for any organization. This improvement can only be achieved with the support of comprehensive systems that fully monitor business processes. We propose an occurrence - based approach to business process monitoring that provides a holistic perspective of system dynamics, lending support to evolution aspects. More specifically, we present a three-dimensional artifact, called Occurrence, in which structure, behavior, and guidance are considered simultaneously. Based on this, we define more complex structures, namely Occurrence Base and Occurrence Management System, which serve as scaffolding to develop business process monitoring systems. We also present a specific occurrence-based design strategy that, using an MDE approach, has been applied by our research group for the development of successful monitoring applications.

A clear understanding of the dynamic semantics of UML State Machines is essential for a large and varied audience such as final users of the language, CASE tool builders or method engineers. Our proposal aims at achieving such an understanding by suggesting a different metamodeling approach. This approach is based on a two-layer architecture which puts forward the explicit distinction between the generic behavior represented in a dynamic model (Base Layer) and the behavior represented in relation to a particular situation (Snapshot Layer). Using this architecture as a starting point, a metamodel of UML State Machines is proposed, which consists basically of two UML class diagrams (one diagram for each layer of the architecture) and two maps. These maps represent, respectively, the determination of the initial status and the process performed by a run-to-completion step as defined in the UML semantics.