All in a twitter: Self-tuning strategies for a deeper understanding of a crisis tweet collection. - Evelina Di Corso; Francesco Ventura; Tania Cerquitelli (IEEE BigData, 2017)

Self-tuning techniques for large scale cluster analysis on textual data collections. - Evelina Di Corso; Tania Cerquitelli; Francesco Ventura (SAC, 2017)

Prompting the data transformation activities for cluster analysis on collections of documents. - Tania Cerquitelli; Evelina Di Corso; Francesco Ventura; Silvia Chiusano (SEBD, 2017)

Data miners' little helper: data transformation activity cues for cluster analysis on document collections. - Tania Cerquitelli; Evelina Di Corso; Francesco Ventura; Silvia Chiusano (WIMS, 2017)

Black-Box Model Explained Through an Assessment of Its Interpretable Features. - Francesco Ventura; Tania Cerquitelli; Francesco Giacalone (ADBIS (Short Papers and Workshops), 2018)

Useful ToPIC: Self-Tuning Strategies to Enhance Latent Dirichlet Allocation. - Stefano Proto; Evelina Di Corso; Francesco Ventura; Tania Cerquitelli (BigData Congress, 2018)

iSTEP, an Integrated Self-Tuning Engine for Predictive Maintenance in Industry 4.0. - Daniele Apiletti; Claudia Barberis; Tania Cerquitelli; Alberto Macii; Enrico Macii; Massimo Poncino; Francesco Ventura (ISPA/IUCC/BDCloud/SocialCom/SustainCom, 2018)

PREMISES, a Scalable Data-Driven Service to Predict Alarms in Slowly-Degrading Multi-Cycle Industrial Processes. - Stefano Proto; Francesco Ventura; Daniele Apiletti; Tania Cerquitelli; Elena Baralis; Enrico Macii; Alberto Macii (BigData Congress, 2019)

A New Unsupervised Predictive-Model Self-Assessment Approach That SCALEs. - Francesco Ventura; Stefano Proto; Daniele Apiletti; Tania Cerquitelli; Simone Panicucci; Elena Baralis; Enrico Macii; Alberto Macii (BigData Congress, 2019)

Towards a real-time unsupervised estimation of predictive model degradation. - Tania Cerquitelli; Stefano Proto; Francesco Ventura; Daniele Apiletti; Elena Baralis (BIRTE, 2019)

Automating concept-drift detection by self-evaluating predictive model degradation. - Tania Cerquitelli; Stefano Proto; Francesco Ventura; Daniele Apiletti; Elena Baralis (CoRR, 2019)

What's in the box? Explaining the black-box model through an evaluation of its interpretable features. - Francesco Ventura; Tania Cerquitelli (CoRR, 2019)

DSLE: A Smart Platform for Designing Data Science Competitions. - Giuseppe Attanasio; Flavio Giobergia; Andrea Pasini; Francesco Ventura; Elena Baralis; Luca Cagliero; Paolo Garza; Daniele Apiletti; Tania Cerquitelli; Silvia Chiusano (COMPSAC, 2020)

Expand your Training Limits! Generating Training Data for ML-based Data Management. - Francesco Ventura; Zoi Kaoudi; Jorge-Arnulfo Quiané-Ruiz; Volker Markl (SIGMOD Conference, 2021)

Explaining the Deep Natural Language Processing by Mining Textual Interpretable Features. - Francesco Ventura; Salvatore Greco; Daniele Apiletti; Tania Cerquitelli (CoRR, 2021)

Enhancing manufacturing intelligence through an unsupervised data-driven methodology for cyclic industrial processes. - Tania Cerquitelli; Francesco Ventura; Daniele Apiletti; Elena Baralis; Enrico Macii; Massimo Poncino (Expert Syst. Appl., 2021)

DataFarm: Farm Your ML-based Query Optimizer's Food! - Human-Guided Training Data Generation -. - Robin Van De Water; Francesco Ventura; Zoi Kaoudi; Jorge-Arnulfo Quiané-Ruiz; Volker Markl (CIDR, 2022)

Farming Your ML-based Query Optimizer's Food. - Robin Van De Water; Francesco Ventura; Zoi Kaoudi; Jorge-Arnulfo Quiané-Ruiz; Volker Markl (ICDE, 2022)

Trusting deep learning natural-language models via local and global explanations. - Francesco Ventura; Salvatore Greco; Daniele Apiletti; Tania Cerquitelli (Knowl. Inf. Syst., 2022)

Explaining deep convolutional models by measuring the influence of interpretable features in image classification. - Francesco Ventura; Salvatore Greco; Daniele Apiletti; Tania Cerquitelli (Data Min. Knowl. Discov., 2024)