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3 results on '"Cante, L."'

1. Semantic and knowledge based support to business model evaluation to stimulate green behaviour of electric vehicles’ drivers and energy prosumers

Author

Bas Bosma, Salvatore Venticinque, Luigi Colucci Cante, Dario Branco, Reinhard Scholten, Beniamino Di Martino, Ethics, Governance and Society, Di Martino, B., Branco, D., Colucci Cante, L., Venticinque, S., Scholten, R., and Bosma, B.

Subjects
Knowledge management, General Computer Science, Computer science, Population, Context (language use), 02 engineering and technology, Ontology (information science), Business model, computer.software_genre, 7. Clean energy, Domain (software engineering), Knowledge extraction, 0502 economics and business, 11. Sustainability, GreenCharge, 0202 electrical engineering, electronic engineering, information engineering, education, Expert system, computer.programming_language, education.field_of_study, business.industry, Ontology, 05 social sciences, Web Ontology Language, Business Model evaluation, 020201 artificial intelligence & image processing, business, computer, Semantic, 050203 business & management
Abstract

This paper proposes a semantic framework for Business Model evaluation and its application to a real case study in the context of smart energy and sustainable mobility. It presents an ontology based representation of an original business model and examples of inferential rules for knowledge extraction and automatic population of the ontology. The real case study belongs to the GreenCharge European Project, that in these last years is proposing some original business models to promote sustainable e-mobility plans. An original OWL Ontology contains all relevant Business Model concepts referring to GreenCharge’s domain, including a semantic description of TestCards, survey results and inferential rules.

Published
2022
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2. Tweets analysis with big data technology and machine learning to evaluate smart and sustainable urban mobility actions in barcelona

Author

Beniamino Di Martino, Mariangela Graziano, Luigi Colucci Cante, Regina Enrich Sard, Leonard Barolli, Aneta Poniszewska-Maranda,Tomoya Enokido, Di Martino, B., Colucci Cante, L., Graziano, M., and Enrich Sard, R.

Subjects
Data source, Focus (computing), business.industry, Computer science, Big data, Sentiment analysis, Machine learning, computer.software_genre, Work (electrical), Spark (mathematics), Social media, Artificial intelligence, business, computer, Natural language
Abstract

Social media can become a valuable data source to gather user opinions on topics of interest. Setting the focus on Twitter as one of the most popular social media channels to share opinions, three challenges have been identified in this work: to obtain users’ sentiment: to classify the topics of interest, to decide whether the opinion is positive or negative by applying sentiment analysis to natural language in written form, and to handle in an efficient manner the huge volume of data generated by Twitter. This paper shows how machine learning and big data technologies have been applied to classify user opinions on electromobility in Barcelona. Supervised and unsupervised approaches have been compared in terms of accuracy and a big data framework based on Spark has been implemented for real time processing combined with batch modelling. The results obtained show potential to apply them as a complementary mechanism to surveys.

Published
2021

3. An ontology framework for evaluating e-mobility innovation

Author

Beniamino Di Martino, Salvatore Venticinque, Luigi Colucci Cante, Vari, L. Barolli , A. Poniszewska-Maranda, T. Enokido, Di Martino, B., Colucci Cante, L., and Venticinque, S.

Subjects
Knowledge management, Computer science, business.industry, Context (language use), Web Ontology Language, Business model, Ontology (information science), business, computer, computer.programming_language
Abstract

This paper presents a semantic description of an evaluation framework that has been defined in the context of the GreenCharge European project. GreenCharge aims at developing innovative solutions to accelerate the transition to e-mobility. In particular, we present an original OWL Ontology, which describes and relates all the various basic concepts of the Business Models and of the relevant knowledge that is necessary for evaluating the impact on sustainable mobility and on energy utilization by developed solutions.

Published
2020

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