Functional Dependencies Unleashed for Scalable Data Exchange. - Angela Bonifati; Ioana Ileana; Michele Linardi (SSDBM, 2016)

ChaseFUN: a Data Exchange Engine for Functional Dependencies at Scale. - Angela Bonifati; Ioana Ileana; Michele Linardi (EDBT, 2017)

ULISSE: ULtra Compact Index for Variable-Length Similarity Search in Data Series. - Michele Linardi; Themis Palpanas (ICDE, 2018)

Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series. - Michele Linardi; Yan Zhu 014; Themis Palpanas; Eamonn J. Keogh (SIGMOD Conference, 2018)

VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series. - Michele Linardi; Yan Zhu 014; Themis Palpanas; Eamonn J. Keogh (SIGMOD Conference, 2018)

Scalable, Variable-Length Similarity Search in Data Series: The ULISSE Approach. - Michele Linardi; Themis Palpanas (Proc. VLDB Endow., 2018)

Effective and Efficient Variable-Length Data Series Analytics. - Michele Linardi (PhD@VLDB, 2019)

Variable-length similarity search for very large data series: subsequence matching, motif and discord detection. (Recherche de similarité de longueur variable pour l'analyse de grandes séries temporelles: appariement de séquences, recherche de motifs et anomalies). - Michele Linardi (2019)

SAD: An Unsupervised System for Subsequence Anomaly Detection. - Paul Boniol; Michele Linardi; Federico Roncallo; Themis Palpanas (ICDE, 2020)

Automated Anomaly Detection in Large Sequences. - Paul Boniol; Michele Linardi; Federico Roncallo; Themis Palpanas (ICDE, 2020)

Matrix profile goes MAD: variable-length motif and discord discovery in data series. - Michele Linardi; Yan Zhu 014; Themis Palpanas; Eamonn J. Keogh (Data Min. Knowl. Discov., 2020)

Scalable data series subsequence matching with ULISSE. - Michele Linardi; Themis Palpanas (VLDB J., 2020)

Unsupervised and scalable subsequence anomaly detection in large data series. - Paul Boniol; Michele Linardi; Federico Roncallo; Themis Palpanas; Mohammed Meftah; Emmanuel Remy (VLDB J., 2021)

Evaluating Explanation Methods of Multivariate Time Series Classification through Causal Lenses. - Etienne Vareille; Adel Abbas; Michele Linardi; Vassilis Christophides (DSAA, 2023)

Towards Explainable AI4EO: An Explainable Deep Learning Approach for Crop Type Mapping using Satellite Images Time Series. - Adel Abbas; Michele Linardi; Etienne Vareille; Vassilis Christophides; Claudia Paris (IGARSS, 2023)

Studying Socially Unacceptable Discourse Classification (SUD) through different eyes: "Are we on the same page ?". - Bruno Machado Carneiro; Michele Linardi; Julien Longhi (CoRR, 2023)

Correction to: Unsupervised and scalable subsequence anomaly detection in large data series. - Paul Boniol; Michele Linardi; Federico Roncallo; Themis Palpanas; Mohammed Meftah; Emmanuel Remy (VLDB J., 2023)

ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions. - Etienne Vareille; Michele Linardi; Ioannis Tsamardinos; Vassilis Christophides (NeurIPS, 2024)

Analysis of Socially Unacceptable Discourse with Zero-shot Learning. - Rayane Ghilene; Dimitra Niaouri; Michele Linardi; Julien Longhi (CoRR, 2024)