Abstract Trial excessive duration is a common problem in Juridical systems worldwide, even if some countries seem to be more affected by it than others. The European Council has provided metrics and statistics to identify this problem and has pointed out solutions, such as the simplification of norms and the digitization of Juridical procedures. The Italian Telematic Civil Process (TCP) is an example of this digitization effort that has surely positively influenced the duration of Trials, their traceability and general complexity. However, there are still many possible actions that can be taken to simplify the work of Judges and Chancellors, and to support their daily operations in dealing with several Trials at once, and with the consistent number of documents that are involved in them. This paper presents a toolchain and a related methodology for the management of documentation attached to Trials, based on semantic technologies and Natural Language Processing techniques, which will help Judges in faster assessing the situation of each Trial they follow, and will also provide the means to identify potential correlations among different Juridical procedures. The methodology is tested against a case study, i.e. the compensation requests related to road accidents, which has been provided and described by Domain Experts from the Italian Ministry of Justice.

To run proper Big Data Analytics, small and medium enterprises (SMEs) need to acquire expertise, hardware and software, which often translates to relevant initial investments for activities not directly connected to the company's business. To reduce such investments, the TOREADOR project proposes a Big Data Analytics framework which supports users in devising their own Big Data solutions by keeping the inherent costs at a minimum, and leveraging pre-existent knowledge and expertise. Among the objectives of the TOREADOR framework is supporting developers in parallelizing and deploying their Big Data algorithms, in order to develop their own analytics solutions. This paper describes the Code-Based approach, adopted within the TOREADOR framework to parallelize users' algorithms and deploy them on distributed platforms, via the annotation of parallelizable code portions with parallelization primitives. The approach, which relies on the guidance of Parallel Patterns to implement the parallelization, and on Skeletons to automatically build execution and deployment templates, is realized through a source-to-source Compiler, also described in the present paper.

Nowadays, most of the information available on the web is in Natural Language. Extracting such knowledge from Natural Language text is an essential work and a very remarkable research topic in the Semantic Web field. The logic programming language Prolog, based on the definite-clause formalism, is a useful tool for implementing a Natural Language Processing (NLP) systems. However, web-based services for NLP have also been developed recently, and they represent an important alternative to be considered. In this paper we present the comparison between two different approaches in NLP, for the automatic creation of an OWL ontology supporting the semantic annotation of text. The first one is a pure Prolog approach, based on grammar and logic analysis rules. The second one is based on Watson Relationship Extraction service of IBM Cloud platform Bluemix. We evaluate the two approaches in terms of performance, the quality of NLP result, OWL completeness and richness.

This work focuses on multi-objective evolutionary optimization by approximation function. It uses the new general concept of evolution control to online enriching the database of correct solutions, which are the basis of the learning procedure for the kriging approximators. Substantially, given an initial very poor model approximation (small size of the database), the database, and consequently the models, is enriched by evaluating part of the individuals of the optimization process. The technique showed being efficient for the considered aerodynamic problem, by requiring few hundreds of true computations when the dimensionality of the problem is 5.