Examinando por Autor "Panetto, Hervé"
Mostrando 1 - 7 de 7
Resultados por página
Opciones de ordenación
- Documento de conferencia
Acceso Abierto Collaborative, Distributed Simulations of Agri-Food Supply Chains: Analysis on How Linking Theory and Practice by Using Multi-agent Structures(2019) Fernández, Alejandro; Hernandez Hormazabal, Jorge E.; Liu, Shaofeng; Panetto, Hervé; Pankow, Matías Nahuel; Sanchez, EstebanSimulations help to understand and predict the behaviour of complex phenomena?s, likewise distributed socio-technical systems or how stakeholders interacts in complex domains. Such domains are normally based on networked based interaction, where information, product and decision flows comes in to play, especially under the well-known supply chains structures. Although tools exist to simulate supply chains, they do not adequately support multiple stakeholders to collaboratively create and explore a variety of decision-making scenarios. Hence, in order to provide a preliminary understanding on how these interaction affects stakeholders decision-making, this research presents an study, analysis and proposal development of robust platform to collaboratively build and simulate communication among supply chain. Since realistic supply chain behaviours are complex, a multi-agent approach was selected in order to represent such complexities in a standardised manner. The platform provides agent behaviours for common agent patterns. It provides extension hotspots to implement more specific agent behaviour for expert users (that requires programming). Therefore, as key contribution, technical aspects of the platform are presented, and also the role of multi-level supply chain scenario simulation is discussed and analysed, especially under de context of digital supply chain transformation in the agri-food context. Finally, we discuss lessons learned from early tests with the reference implementation of the platform. - Documento de conferencia
Acceso Abierto Extending RCA algorithm to consider ternary relations(2022) Leutwyler, Nicolás; Lezoche, Mario; Panetto, Hervé; Torres, DiegoRelational Concept Analysis (RCA) is a multirelational data mining method that aims to extract knowledge from multiple formal contexts (i.e., objects, attributes, and a binary relation between them) and the relations between them. One of the problems RCA has is the lack of the possibility of extracting knowledge directly from data that is represented with ternary relations. While there are some existing solutions towards this problem, either they require complex preprocessing of the input data, or they lose some capabilities of RCA such as the different meanings of the relations between concepts (∃, ∀, etc). In this work, we present an intuitive extension to RCA to be able to use it with data directly represented with ternary relations. As an example of its usage, we apply it to a dataset called Knomana which includes ternary relations - Artículo
Acceso Abierto Generic software for benchmarking Formal Concept Analysis: Orange3 integration(2023) Leutwyler, Nicolas; Lezoche, Mario; Panetto, Hervé; Torres, DiegoThanks to the internet of things (IoT) and cyber physical systems (CPS), we face an incremental growth of the available data, either on the internet or in private databases. This resulted in data mining techniques becoming an essential piece in the information retrieval process. Moreover, trends like the industry 4.0 encourages its usage to support data driven decisions, for instance. Formal Concept Analysis (FCA) is one of the most used techniques in the unsupervised data mining field due to its inherent ability to find patterns between concepts. As a consequence, many applications need the use of fast algorithms to perform the calculations to retrieve either the lattice or the association rules related with the data at their disposal. Due to this, scientists often rely on manually crafted benchmarks to compare how certain algorithms perform under different circumstances. In this work, we propose the architecture of a software to generalize these benchmarks independently of the algorithms, to be integrated in the open source data analysis software Orange3. - Documento de conferencia
Acceso Abierto Knowledge mobilisation crossing boundaries: a multi-perspective framework for agri-food value chains(2020) Liu, Shaofeng; Zhao, Guoqing; Chen, Huilan; Fernández, Alejandro; Torres, Diego; Antonelli, Leandro; Panetto, Hervé; Lezoche, MarioKnowledge has long been recognised as a valuable asset to individuals, organisations and economy, subsequently knowledge management (KM) has been a well-established area of research. Existing research has developed various classification schemes for knowledge, and a great number of KM process and lifecycle models have been proposed over the last few decades. In particular, knowledge transfer and sharing has received great attention. However, majority of existing work has focused on knowledge sharing within the same organisation or community where people have a shared sense of identity, values and some common practice, hence knowledge process and learning is within relatively homogeneous groups. There is inadequate research to address the issue of knowledge boundaries and approaches to knowledge mobilisation spanning across knowledge boundaries. The knowledge boundaries can erect significant barriers to knowledge sharing and flowing especially in value chain context where there are a great number of players from different domains, with varied level of knowledge, having different and possibly conflicting interests - participating in knowledge sharing activities. This paper will explore how such knowledge boundaries can be identified and how knowledge gaps among different value chain players can be closed by using appropriate boundary-crossing mechanisms. A multi-perspective knowledge mobilisation framework is proposed. Example applications of the knowledge mobilisation framework in agri-food value chain will be illustrated, based on the most recent developments from an EU collaborative project, RUC-APS (standing for Risk and Uncertain Conditions in Agriculture Production Systems), which is funded by European Commission’s Horizon 2020 RISE programme. - Revisión
Acceso Abierto Methods for concept analysis and multi-relational data mining: a systematic literature review(2024) Leutwyler, Nicolás; Lezoche, Mario; Franciosi, Chiara; Panetto, Hervé; Teste, Laurent; Torres, DiegoThe Internet of Things massive adoption in many industrial areas in addition to the requirement of modern services is posing huge challenges to the field of data mining. Moreover, the semantic interoperability of systems and enterprises requires to operate between many different formats such as ontologies, knowledge graphs, or relational databases, as well as different contexts such as static, dynamic, or real time. Consequently, supporting this semantic interoperability requires a wide range of knowledge discovery methods with different capabilities that answer to the context of distributed architectures (DA). However, to the best of our knowledge there is no general review in recent time about the state of the art of Concept Analysis (CA) and multi-relational data mining (MRDM) methods regarding knowledge discovery in DA considering semantic interoperability. In this work, a systematic literature review on CA and MRDM is conducted, providing a discussion on the characteristics they have according to the papers reviewed, supported by a clusterization technique based on association rules. Moreover, the review allowed the identification of three research gaps toward a more scalable set of methods in the context of DA and heterogeneous sources. - Documento de conferencia
Acceso Abierto Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mapping(2023) Leutwyler, Nicolás; Lezoche, Mario; Torres, Diego; Panetto, HervéSmart Enterprises, Smart Manufacturing, and Cyber-Physical Systems are gaining traction in many industry areas. On top of that, the amounts of available data grow rapidly, and organizations are eager to exploit their advantages. To accomplish that, it is mandatory to have a wide variety of methods and algorithms for knowledge extraction in order to fit the different needs and problems of the industry. In this study, we review and dissect the current state of the art in knowledge extraction applied to smart enterprises, smart manufacturing, and cyber-physical systems. More specifically, we provide a classification of the characteristics of the available methods in the literature according to their applications, and point out areas of improvement. - Documento de conferencia
Acceso Abierto Towards a Flexible and Scalable Data Stream Algorithm in FCA(2023) Leutwyler, Nicolás; Lezoche, Mario; Torres, Diego; Panetto, HervéThe amount of different environments where data can be exploited have increased partly because of the massive adoption of technologies such as microservices and distributed architectures. Accordingly, approaches to treat data are in constant improvement. An example of this is the Formal Concept Analysis framework that has seen an increase in the methods carried out to increment its capabilities in the mentioned environments. However, on top of the exponential nature of the output that the framework produces, the data stream processing environment still poses challenges regarding the flexibility in the usage of FCA and its extensions. Consequently, several approaches have been proposed to deal with them considering different constraints, such as receiving unsorted elements or unknown attributes. In this work, the notion of flexibly scalable for FCA distributed algorithms consuming data streams is defined. Additionally, the meaning of different scenarios of lattice merge in a particular data stream model is discussed. Finally, a pseudo-algorithm for merging lattices in the case of disjoint objects is presented. The presented work is a preliminary result and, in the future, it is expected to cover the other aspects of the problem with real data for validation.