A Model-Based Approach Towards the Conceptualization of Digital Twins: The Case of the EU-Project COGITO

In agile business ecosystems, digitalization is a key enabler for agility and flexibility. However, digital transformation is often challenging for instance due to unclear definitions and a lack of problem understanding. In this work this complexity is addressed with a model-based approach for conceptualizing digitalization and related meta modelling activities to enable the conceptual integration of diverse concepts. Existing modelling approaches – BPMN and ArchiMate – are leveraged with domain specific considerations that are relevant for the digitalization. The construction use case from the European project COGITO serves as a foundation for ideation and first requirements engineering. Physical experiments in the OMiLAB Innovation Environment are used as an experimental method towards identifying relevant digital twinning concepts, while modelling methods can be seen as an integration platform for physical and digital elements. Key digitalization aspects towards digital twinning are discussed and conceptualized in a meta model

Personal Information Markets AND Privacy: A New Model to Solve the Controversy

From the early days of the information economy, personal data has been its most valuable asset. Despite data protection laws, companies trade personal information and often intrude on the privacy of individuals. As a result, consumers feel out of control and lose trust in electronic environments. Technologists and regulators are struggling to develop solutions that meet businesses’ demand for more personal information while maintaining privacy. However, no promising proposals seem to be in sight. We propose a 3-tier personal information market model with privacy. In our model, clear roles, rights and obligations for all actors re-establish trust. The ‘relationship space’ enables data subjects and visible business partners to build trusting relationships. The ‘service space’ supports customer relationships with distributed information processing. The ‘rich information space’ enables anonymized information exchange. To transition to this model, we show how existing privacy-enhancing technologies and legal requirements can be integrated

Improving argumentation-based recommender systems through context-adaptable selection criteria

Recommender Systems based on argumentation represent an important proposal where the recommendation is supported by qualitative information. In these systems, the role of the comparison criterion used to decide between competing arguments is paramount and the possibility of using the most appropriate for a given domain becomes a central issue; therefore, an argumentative recommender system that offers an interchangeable argument comparison criterion provides a significant ability that can be exploited by the user. However, in most of current recommender systems, the argument comparison criterion is either fixed, or codified within the arguments. In this work we propose a formalization of context-adaptable selection criteria that enhances the argumentative reasoning mechanism. Thus, we do not propose of a new type of recommender system; instead we present a mechanism that expand the capabilities of existing argumentation-based recommender systems. More precisely, our proposal is to provide a way of specifying how to select and use the most appropriate argument comparison criterion effecting the selection on the user´s preferences, giving the possibility of programming, by the use of conditional expressions, which argument preference criterion has to be used in each particular situation.Fil: Teze, Juan Carlos Lionel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Gottifredi, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: García, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentin

SocioPath: Bridging the Gap between Digital and Social Worlds

International audienceIn everyday life, people use more and more digital resources (data, application systems, Internet, etc.) for all aspects of their life like administrative procedures, financial management, private exchanges, collaborative work, etc. This leads to non-negligible dependences on the digital distributed resources that reveal strong reliance at the social level, for instance on providers, physical or moral persons, of these resources. Users are often not aware of their real autonomy regarding the management of their digital resources. Thus, currently, people underestimate social dependences generated by the system architecture they use and the resulting potential risks. We argue that it is necessary to be aware of some key aspects of system's architectures to be able to know dependences. In this paper, we proposes SOCIOPATH, a generic meta-model to derive dependences generated by system's architecture. In particular, SOCIOPATH focuses on relations, like access, control, support, ownership, and so forth, among the different entities of the system (digital resources, hardware, persons, etc.). Enriched with deduction rules and definitions, SOCIOPATH allows to reveal the dependences of a person towards each entity in the system. SOCIOPATH could then be useful in the evaluation process of a system, as a modeling tool that bridges the gap between the digital and the social worlds

Q-analysis based clustering of online news

With online publication and social media taking the main role in dissemination of news, and with the decline of traditional printed media, it has become necessary to devise ways to automatically extract meaningful information from the plethora of sources available and to make that information readily available to interested parties. In this paper we present a method of automated analysis of the underlying structure of online newspapers based on Q-analysis and modularity optimisation. We show how the combination of the two strategies allows for the identification of well defined news clusters that are free of noise (unrelated stories) and provide automated clustering of information on trending topics on news published online

Assessing metric structures on GPGPU environments

Similarity search consists on retrieving objects within a database that are similar or relevant to a particular query. It is a topic of great interest to scientific community because of its many fields of application, such as searching for words and images on the World Wide Web, pattern recognition, detection of plagiarism, multimedia databases, among others. It is modeled through metric spaces, in which objects are represented in a black-box that contains only the distance between objects; calculating the distance function is costly and search systems operate at a high query rate. Metrical structures have been developed to optimize this process; such structures work as indexes and preprocess data to decrease the distance evaluations during the search. Processing large volumes of data makes unfeasible the use of such structures without using parallel processing environments. Technologies based on multi- CPU and GPU architectures are among the most force due to its costs and performance.XV Workshop de Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

Assessing metric structures on GPGPU environments

Similarity search consists on retrieving objects within a database that are similar or relevant to a particular query. It is a topic of great interest to scientific community because of its many fields of application, such as searching for words and images on the World Wide Web, pattern recognition, detection of plagiarism, multimedia databases, among others. It is modeled through metric spaces, in which objects are represented in a black-box that contains only the distance between objects; calculating the distance function is costly and search systems operate at a high query rate. Metrical structures have been developed to optimize this process; such structures work as indexes and preprocess data to decrease the distance evaluations during the search. Processing large volumes of data makes unfeasible the use of such structures without using parallel processing environments. Technologies based on multi- CPU and GPU architectures are among the most force due to its costs and performance.XV Workshop de Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

Instance-level matching

sanchez2016aThis paper describes precisely an ontology matching technique based on the extensional definition of a class as set of instances. It first provides a general characterisation of such techniques and, in particular the need to rely on links across data sets in order to compare instances. We then detail the implication intensity measure that has been chosen. The resulting algorithm is implemented and evaluated on XLore, DBPedia, LinkedGeoData and Geospecies