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Economic models for reuse are very important to organizations aiming to develop software with large scale reuse approaches. In fact, the initial investment is so important that it can discourage managers to commit to those approaches. Thus, economic models can help them to assess the worthiness of such an investment.Product Line Engineering (PLE) seems to be an attractive reuse approach in matter of product quality and time-to-market. Using Commercial Off The Shelf (COTS) in a PLE approach may have a positive impact.This paper reports on the need for an economic model to quantify the predicted benefits of the PLE software development with the use of COTS components. We introduce a Model for Software Cost Estimation in a Product Line Engineering approach that we denote SoCoEMo-PLE 2. This latter includes the usage of COTS components. The potential benefits of the model are described.

The increasing volume of data generated by earth observation programs such as Copernicus, NOAA, and NASA Earth Data, is overwhelming. Although these programs are very costly, data usage remains limited due to lack of interoperability and data linking. In fact, multi-source and heterogeneous data exploitation could be significantly improved in different domains especially in the natural disaster prediction one. To deal with this issue, we introduce the PREDICAT project that aims at providing a semantic service-oriented platform to PREDIct natural CATastrophes. The PREDICAT platform considers (1) data access based on web service technology; (2) ontology-based interoperability for the environmental monitoring domain; (3) data integration and linking via big data techniques; (4) a prediction approach based on semantic machine learning mechanisms. The focus in this paper is to provide an overview of the PREDICAT platform architecture. A scenario explaining the operation of the platform is presented based on data provided by our collaborators, including the international intergovernmental Sahara and Sahel Observatory (OSS).

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