Resultados de búsqueda para reasoning

Modern applications of Big Data are transcendingfrom being scalable solutions of data processing and analysis, to nowprovide advanced functionalities with the ability to exploit and understandthe underpinning knowledge. This change is promoting the developmentof tools in the intersection of data processing, data analysis,knowledge extraction and management. In this paper, we proposeTITAN, a software platform for managing all the life cycle of scienceworkflows from deployment to execution in the context of Big Data applications.This platform is characterised by a design and operation modedriven by semantics at different levels: data sources, problem domain andworkflow components. The proposed platform is developed upon an ontologicalframework of meta-data consistently managing processes andmodels and taking advantage of domain knowledge. TITAN comprises awell-grounded stack of Big Data technologies including Apache Kafka forinter-component communication, Apache Avro for data serialisation andApache Spark for data analytics. A series of use cases are conducted forvalidation, which comprises workflow composition and semantic metadatamanagement in academic and real-world fields of human activityrecognition and land use monitoring from satellite images.

The proper configuration of systems has become a fundamental factor to avoid cybersecurity risks. Thereby, the analysis of cybersecurity vulnerabilities is a mandatory task, but the number of vulnerabilities and system configurations that can be threatened is extremely high. In this paper, we propose a method that uses software product line techniques to analyse the vulnerable configuration of the systems. We propose a solution, entitled AMADEUS, to enable and support the automatic analysis and testing of cybersecurity vulnerabilities of configuration systems based on feature models. AMADEUS is a holistic solution that is able to automate the analysis of the specific infrastructures in the organisations, the existing vulnerabilities, and the possible configurations extracted from the vulnerability repositories. By using this information, AMADEUS generates automatically the feature models, that are used for reasoning capabilities to extract knowledge, such as to determine attack vectors with certain features. AMADEUS has been validated by demonstrating the capacities of feature models to support the threat scenario, in which a wide variety of vulnerabilities extracted from a real repository are involved. Furthermore, we open the door to new applications where software product line engineering and cybersecurity can be empowered.

Using models to represent business processes provides several advantages, such as being able to check the correctness of the processes before their implementation. In contrast to traditional process modeling approaches, the artifact-centric approach treats data as a key element of the process, also considering the tasks or activities that are performed in it. This paper presents a way to verify and validate the semantic correctness of an artifact-centric business process model defined using a combination of UML and OCL models – a BAUML model. To do this, we provide a method to translate all BAUML components into a set of logic formulas. The result of this translation ensures that the only changes allowed are those specified in the model, and that those changes are taking place according the order established by the model. Having obtained this logic representation, these models can be validated by any existing reasoning method able to deal with negation of derived predicates. Moreover, we show how to automatically generate the relevant tests to validate the models and we prove the feasibility of our approach.