Data and Process Management
In a world of ambient intelligence where large, diverse data sources and fast-evolving contextual information are exploited through complex algorithms for a multiplicity of practical purposes, there is a critical need for proper governance of data, knowledge and processes. This is paramount in ensuring the quality, reliability and sustainability of data-driven services and to expand the scope, accuracy and relevance of future applications.
Our research addresses these needs by developing methods, models, tools, languages and frameworks in support of various aspects of data, knowledge and process governance.
Our data governance research ranges from models and tools to enable instant update and validation of huge volumes of data available in a unified topology/mobility referential, to data quality management in open-ended environments (crowdsourced or enriched by NLP) or to the creation of domain-specific languages for data programming.
In parallel, our research in Process Intelligence centers on easy creation, understanding and governance of processes-based apps, from design to execution. We bring a domain-specific perspective to application modeling, deployment and integration in order to allow anyone to easily create and reuse process-driven solutions that benefit from a large-scale reuse of knowledge.
We also advocate a multi-disciplinary approach to combine process modelling with other fields at the core of ambient intelligence such as machine learning, computer vision or natural language processing