Prof. Bonifati will present her latest results on enhancing the quality of querying and inference processes on healthcare data. She and her team operate on real-life data of patients from a French hospital and provide the domain experts with useful data management and learning techniques that can help them with their diagnoses and analyses. In the lecture, she will mainly focus on two techniques. The first technique allows us to add inconsistency annotations to structured data and queries, while the second technique allows us to identify similarities among long time series corresponding to patients’ signals. In both cases, their research aims at providing the caregivers with a better understanding of their clinical data thanks to the improved outcomes of the performed analytics.
Angela Bonifati is a professor of Computer Science at Lyon 1 University and has been affiliated with the CNRS Liris research lab since 2015. She is a specialist of advanced database applications such as data integration and exchange, web and graph databases, and query refinement on both structured and semi-structured data models. Bonifati has been a visiting scholar at Stanford University, the University of British Columbia, Saarland University, and most recently the University of Waterloo (2019).
Bonifati’s current research interests are on the interplay of relational and graph-oriented data paradigms, particularly on data integration, Big data curation for life-science, query processing, and learning for structure and unstructured data models. She is Principal Investigator of the ANR research project QualiHealth: Enhancing the Quality of Healthcare Data (2018-2023) on Big data curation in the healthcare domain, which will be the topic of her invited lecture.