Examinando por Autor "Leutwyler, Nicolás"
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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 - 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.