Data-centric AI
Data-centric AI is defined as “the discipline of systematically engineering the data needed to build a successful AI system.” and concerns with real-world data that are unstructured, often incomplete/limited in number, and partially inconsistent. Starting from real-world case studies and datasets coming different application contexts such as clinical medicine, human-resource management, industrial production, aso, our objectives is to study specific data-preparation and AI methodologies for processing data in-the-wild, making AI performance adequate and stable in these challenging contexts, and to study AI solutions meeting the specific requirements arising in the involved application-contexts.
Funded Projects
WASABI: White-label shop for digital intelligent assistance and human-AI collaboration in manufacturing
HybridAI: an hybrid approach to Natural Language Understanding
Work Datafication and Behavioral Visibility in The Digital Workplace
Assessment of well-being state in Post-acute COVID Syndrome: an international multicentre prospective cohort study
Machine Learning to Operationalize WG definition and Generate a Personalized Phenotypic Characterization in PLWHIV
Information sharing, interoperability, and retrieval
The ever-growing and widespread availability of data from Internet information sources has placed great interest in the potential of information sharing and interoperability. In line with this view, the Semantic Web aims at converting the current web, dominated by unstructured and semi-structured documents into a “web of data”. The need to complement the Web with semantics has spurred efforts toward a rich representation of data, giving rise to the widespread use of ontologies, XML schemas, and RDF schemas.
Our work in this field is concerned with the sharing and interoperability of heterogeneous and distributed data sources and is mainly focused on the Peer-to-Peer (P2P) paradigm and its evolutions toward dataspaces. We propose techniques for creating effective Peer Data Management Systems (PDMSs) with limited information loss and solutions for query reformulation and processing and query routing. We consider various application contexts ranging from digital libraries to business intelligence.
Funded Projects
NeP4B: Networked Peers for Business
WISDOM: Web Intelligent Search based on DOMain ontologies
DELOS – A Network of Excellence on Digital Libraries
Technologies and Services for Enhanced Content Delivery (ECD)
Non-conventional data management
We study solutions for an efficient management of data streams including sensor data, RFID data, data coming from OnBoardUnits (OBUs) aso. We are especially interested in developing techniques for real-time query processing in the context of workload-intensive applications. We are also concerned with the specificities of the different kinds of streaming data as RFID data which are dealt with as probabilistic data or OBU data that are spatio-temporal data. This activity was partially funded by the PEGASUS project.
Funded Projects
Pegasus
Outdoor Video Protection: innovative vision and information technologies for safe outdoors work environments
Semantic Self-organizing Mobile Networks of Radio Sensors