Robust Semi-Supervised Structure Learning of Composite Event Rules.


Semi-Supervised Online Structure Learning for Composite Event Recognition.
In Machine Learning, 108(7), pp. 1085–1110, 2019.

PDF Dataset DOI Appendix

The Complex Event Recognition Group.
In SIGMOD Record, 47(2), pp. 61–66, 2018.


Online Learning of Weighted Relational Rules for Complex Event Recognition.
In ECML/PKDD, pp. 396–413, Springer, 2018.

PDF Poster Slides DOI

OSL𝛼: Online Structure Learning Using Background Knowledge Axiomatization.
In ECML/PKDD, pp. 232–247, Springer, 2016.

PDF Code Dataset Poster Slides DOI Appendix

Online Structure Learning for Traffic Management.
In Inductive Logic Programming, pp. 27–39, Springer, 2016.

PDF Poster Slides DOI

R&D Projects

INFORE - Interactive Extreme-Scale Analytics and Forecasting

Research and development for Machine Learning [2019 - present]

Objective: INFORE mission is to pave the way for real-time, interactive, extreme-scale analytics and forecasting on massive data flows streaming in from multitude sources. For instance, maritime surveillance combine high-velocity data streams of vessel position signals; in the financial domain, stock price forecasting combine stock data and real-time information of pricing indicators; at the fight against cancer, complex simulations are used, producing data streams of the effects that drug synergies have on cancer cells.

Track&Know - Big Data Mobility Tracking Knowledge Extraction in Urban Areas

Research and development for Machine Learning [2019 - present]

Objective: Track&Know mission is to develop and exploit a framework for increasing the efficiency of Big Data applications in the transport, mobility, motor insurance and health sectors. Stemming from industrial cases, Track&Know develops user friendly toolboxes that will be readily applicable in the addressed markets.

datAcron - Big Data Analytics for Time Critical Mobility Forecasting

Research and development for the Machine Learning component [2017 - 2019]

Objective: datAcron vision was to advance the management and integrated exploitation of voluminous and heterogeneous data-at-rest (archival data) and data-in-motion (streaming data) sources, so as to significantly advance the capacities of systems to promote safety and effectiveness of critical operations for large numbers of moving entities in large geographical areas.

SPEEDD - Scalable Proactive Event-Driven Decision-Making

Leading developer of the Machine Learning component [2016 - 2017]

Objective: SPEEDD developed a prototype for proactive event-driven decision-making wherein decisions are triggered by forecasting events. The decisions are in real-time, in the sense that they are taken under tight time constraints, and require on-the-fly processing of Big Data, that is, large amounts of noisy data flooding in from various geographical locations, as well as historical data.

REVEAL - Revealing Hidden Concepts in Social Media

Research and development on Statistical Relational Learning [2014 - 2015]

Objective: REVEAL aimed to advance the necessary technologies for making a higher level analysis of social media possible. The project enables users to reveal hidden modalities such as reputation, influence or credibility of information in order to perform social media verification.


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