HiveScan
DemonstratorMembersMap innovative companies of a field, with an innovation index you can decompose.
Thesis of Connor MacLean, conducted with funding from EDF
Research / T6
Tech scouting in energy
Who is inventing what, and how do we know?
Text mining, named-entity recognition and topic modelling to map technologies and innovative actors, with indicators that remain traceable.
Doctoral work in the team has applied these techniques to renewable-energy start-ups, with an innovation index whose every component can be inspected. HiveScan is the resulting demonstrator.
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All publications2025 · Conference paper
Prétraitement syntaxique pour enrichir le Bag of Words en Topic Modeling
Connor Maclean, Denis Cavallucci
20e Conférence en Recherche d’Information et Applications (CORIA) 32ème Conférence sur le Traitement Automatique des Langues Naturelles (TALN) 27ème Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RECITAL) Les 18e Rencontres Jeunes Chercheurs en RI (RJCRI)
2025 · Journal article
Large-scale transformer-based topic graphs identify thematic links between engineering and biology
Nicolas Douard, Denis Cavallucci, Ahmed Samet, George Giakos
Scientific Reports
2024 · Conference paper
Information Extraction to Identify Novel Technologies and Trends in Renewable Energy
Connor Maclean, Denis Cavallucci
World Conference of AI-Powered Innovation and Inventive Design 24th IFIP WG 5.4 International TRIZ Future Conference, TFC 2024,
2024 · Conference paper
A Novel Interdisciplinarity Model Towards Inter-domain Information Pairing
Nicolas Douard, Ahmed Samet, George Giakos, Denis Cavallucci
24th International TRIZ Future Conference (TFC)
2024 · Journal article
Assessing Fine-Tuned NER Models with Limited Data in French: Automating Detection of New Technologies, Technological Domains, and Startup Names in Renewable Energy
Connor Maclean, Denis Cavallucci
Machine Learning and Knowledge Extraction
2024 · Conference paper
SynCRF: Syntax-Based Conditional Random Field for TRIZ Parameter Minings
Guillaume Guarino, Ahmed Samet, Denis Cavallucci
16th International Conference on Agents and Artificial Intelligence, ICAART 2024, 24-26 February, 2024
2021 · Conference paper
Automatic Extraction of Potentially Contradictory Parameters from Specific Field Patent Texts
Daria Berdyugina, Denis Cavallucci
TRIZ Future conference
2021 · Conference paper
Automatic extraction of potentially contradictory parameters from specific field patent texts
Daria Berdyugina, Denis Cavallucci
TFC 2021: Creative Solutions for a Sustainable Development, 22–24 septembre 2021, Bolzano, Italy
2021 · Journal article
New Characterizing Method of a 3D Parametric Lattice Structure
Rémi Edouard, Hicham Chibane, Denis Cavallucci
FME Transactions
2020 · Conference paper
A Method to Formulate Problem in Initial Analysis of Inventive Design
Masih Hanifi, Hicham Chibane, Remy Houssin, Denis Cavallucci
17th IFIP International Conference on Product Lifecycle Management (PLM)
2019 · Conference paper
Improving Inventive Design Methodology’s Agility
Masih Hanifi, Hicham Chibane, Rémy Houssin, Denis Cavallucci
19th International TRIZ Future Conference (TFC)
2018 · Journal article
Latent semantic extraction and analysis for TRIZ-based inventive design
Wei Yan, Hong Liu, Yusheng Liu, Jihua Wang, Cecilia Zanni-Merk, Denis Cavallucci, Xiaodan Yan, Liang Zhang
European Journal of Industrial Engineering