Keywords:Model Metamodel Modeling language Software system Model-driven engineering Model-driven approaches a b s t r a c t During the last decade a new trend of approaches has emerged, which considers models not just documentation artefacts, but also central artefacts in the software engineering field, allowing the creation or automatic execution of software systems starting from those models. These proposals have been classified generically as Model-Driven Engineering (MDE) and share common concepts and terms that need to be abstracted, discussed and understood. This paper presents a survey on MDE based on a unified conceptual model that clearly identifies and relates these essential concepts, namely the concepts of system, model, metamodel, modeling language, transformations, software platform, and software product. In addition, this paper discusses the terminologies relating MDE, MDD, MDA and others. This survey is based on earlier work, however, contrary to those, it intends to give a simple, broader and integrated view of the essential concepts and respective terminology commonly involved in the MDE, answering to key questions such as: What is a model? What is the relation between a model and a metamodel? What are the key facets of a modeling language? How can I use models in the context of a software development process? What are the relations between models and source code artefacts and software platforms? and What are the relations between MDE, MDD, MDA and other MD approaches?

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These last years we have been witnessing a tremendous growth in the volume and availability of data. This fact results primarily from the emergence of a multitude of sources (e.g. computers, mobile devices, sensors or social networks) that are continuously producing either structured, semi-structured or unstructured data. Database Management Systems and Data Warehouses are no longer the only technologies used to store and analyze datasets, namely due to the volume and complex structure of nowadays data that degrade their performance and scalability. Big Data is one of the recent challenges, since it implies new requirements in terms of data storage, processing and visualization. Despite that, analyzing properly Big Data can constitute great advantages because it allows discovering patterns and correlations in datasets. Users can use this processed information to gain deeper insights and to get business advantages. Thus, data modeling and data analytics are evolved in a way that we are able to process huge amounts of data without compromising performance and availability, but instead by "relaxing" the usual ACID properties. This paper provides a broad view and discussion of the current state of this subject with a particular focus on data modeling and data analytics, describing and clarifying the main differences between the three main approaches in what concerns these aspects, namely: operational databases, decision support databases and Big Data technologies.

This paper discusses the results of applied research on the eco-driving domain based on a huge data set produced from a fleet of Lisbon's public transportation buses for a three-year period. This data set is based on events automatically extracted from the control area network bus and enriched with GPS coordinates, weather conditions, and road information. We apply online analytical processing (OLAP) and knowledge discovery (KD) techniques to deal with the high volume of this data set and to determine the major factors that influence the average fuel consumption, and then classify the drivers involved according to their driving efficiency. Consequently, we identify the most appropriate driving practices and styles. Our findings show that introducing simple practices, such as optimal clutch, engine rotation, and engine running in idle, can reduce fuel consumption on average from 3 to 5l/100 km, meaning a saving of 30 l per bus on one day. These findings have been strongly considered in the drivers' training sessions.Index Terms-Driver profile, eco-driving, fuel efficiency, data warehouse, knowledge discovery (KD), public transportation. 1524-9050