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An Autonomous-UAV Architecture for Remote Sensing and Intelligent Decision-making (Abstract)

Recently, the US Department of Transportations Federal Aviation Administration and other international organizations have proposed a set of requirements for small unmanned aerial vehicles (UAVs) to operate for nonrecreational purposes. However, existing UAV architectures fulfill only some of the established requirements, and not all in one solution. This paper presents an unprecedented event-driven service-oriented architecture that allows autonomous UAVs to satisfy all these requirements and to detect critical situations, performing real-time decision making. The core of this architecture is based on the use of complex event processing (CEP) onboard. The results obtained involve advances in terms of the number of events processed per second, response time, ease of use for nontechnological users, and code reconfiguration before or during the UAV flight. These results have been validated by implementing the architecture.

Identification and analysis of the elements required to manage technical debt by means of a systematic mapping study (Artículo relevante)

Artículo publicado en Journal of Systems and Software (JSS). JSS es clasificada como Q1 en JCR 2017. @article{FERNANDEZSANCHEZ201722,title = «Identification and analysis of the elements required to manage technical debt by means of a systematic mapping study»,journal = «Journal of Systems and Software»,volume = «124»,pages = «22 – 38»,year = «2017»,issn = «0164-1212»,doi = «»,url = «»,author = «Carlos Fernández-Sánchez and Juan Garbajosa and Agustín YagÃŒe and Jennifer Perez»,keywords = «Technical debt, Technical debt management, Systematic mapping, Decision making, Basic decision-making factors, Cost estimation techniques, Practices and techniques for decision-making, Stakeholdersâ?? points of view, Engineering, Engineering management, Business-organizational management, Framework»,abstract = «Technical debt, a metaphor for the long-term consequences of weak software development, must be managed to keep it under control. The main goal of this article is to identify and analyze the elements required to manage technical debt. The research method used to identify the elements is a systematic mapping, including a synthesis step to synthesize the elements definitions. Our perspective differs from previous literature reviews because it focused on the elements required to manage technical debt and not on the phenomenon of technical debt or the activities used in performing technical debt management. Additionally, the rigor and relevance for industry of the current techniques used to manage technical debt are studied. The elements were classified into three groups (basic decision-making factors, cost estimation techniques, practices and techniques for decision-making) and mapped according three stakeholdersâ?? points of view (engineering, engineering management, and business-organizational management). The definitions, classification, and analysis of the elements provide a framework that can be deployed to help in the development of models that are adapted to the specific stakeholdersâ?? interests to assist the decision-making required in technical debt management and to assess existing models and methods. The analysis indicated that technical debt management is context dependent.»}

Towards a Comprehensive Purchasing Model for Cloud Services

The Cloud Service Market has evolved into a complex landscape that challenges the decision making of users as they develop their purchasing process. In particular, we explore the case of cloud infrastructure (IaaS) providers as an example of heterogeneous variety of purchasing options and discounts; this variability represents an important drawback during the decision making process where there is a need to compare and select the best option. In this work, we define a common model to describe purchasing models from different providers taking into account such heterogeneity. This purchasing model represents a first step towards the automated support of decision making problems during the purchasing process. In order to illustrate our approach we apply the model in a real case study of IaaS purchasing.