This work presents the evolution of a solution for predictive maintenance to a Big Data environment. The proposed adaptation aims for predicting failures on wind turbines using a data-driven solution deployed in the cloud and which is composed by three main modules. (i) A predictive model generator which generates predictive models for each monitored wind turbine by means of Random Forest algorithm. (ii) A monitoring agent that makes predictions every 10 minutes about failures in wind turbines during the next hour. Finally, (iii) a dashboard where given predictions can be visualized. To implement the solution Apache Spark, Apache Kafka, Apache Mesos and HDFS have been used. Therefore, we have improved the previous work in terms of data process speed, scalability and automation. In addition, we have provided fault-tolerant functionality with a centralized access point from where the status of all the wind turbines of a company localized all over the world can be monitored, reducing O&M costs.

Abstract. Diversity is prevalent in modern software systems to facilitate adapting the software to customer requirements or the execution environment. Diversity has an impact on all phases of the software development process. Appropriate means and organizational structures are required to deal with the additional complexity introduced by software variability. This introductory article to the special section "Software Diversity -Modeling, Analysis and Evolution" provides an overview of the current state of the art in diverse systems development and discusses challenges and potential solutions. The article covers requirements analysis, design, implementation, verification and validation, maintenance and evolution as well as organizational aspects. It also provides an overview of the articles which are part of this special section and address particular issues of diverse systems development.

Model Driven Development (MDD) is an emerging paradigm for software construction that uses models to specify programs, and model transformations to synthesize executables. Feature Oriented Programming (FOP) is a paradigm for software product lines where programs are synthesized by composing features. Feature Oriented Model Driven Development (FOMDD) is a blend of FOP and MDD that shows how products in a software product line can be synthesized in an MDD way by composing features to create models, and then transforming these models into executables. We present a case study of FOMDD on a product line of portlets, which are components of web portals. We reveal mathematical properties of portlet synthesis that helped us to validate the correctness of our abstractions, tools, and specifications, as well as optimize portlet synthesis.

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