Prof. Vakali research efforts focus on Data Science disciplines with emphasis on Big Data analytics and Data technologies. Her research work spans from foundational principles (with algorithms and feature engineering approaches) to design and implementations of actual technologies, tools, and systems implemented in various domains. At her core research work, prof Vakali explores the : (i) dynamics and trends which drive online Web content and usage evolution; (ii) modelling and management of big data entities; (iii) knowledge extraction by appropriate graph mining and AI inspired techniques; and (iv) study of emerging data technologies from cloud to the edge, and their extend to decentralized (blockchain) frameworks. Her research work has valuable contributions to multiple domains since her research work is extensively validated by robust experimentation in social networks, health, energy, creative platforms, and smart cities. Under her lead, the Datalab research team is intensively involved in research and innovation actions with a focus on the next main topics:
- Evolving big data analytics with various methodologies (community detection, graph mining, incremental clustering, data mining, machine learning, etc.) on multiple (Web logs, social networks, reviews, etc.) datasets targeting opinions, topics, emotions and latent knowledge detection from big data sources;
- Social media and networks modelling and mining with usage and content summarization methodologies for topics and events detection in various contexts (geo-location, time, sentiment, etc). Emphasis on social networks trends and phenomena, such as social media fraud, and behavioural patterns revealing;
- Smart cities and IoT data integration and summarization with focus on smart city data threads management and detection of communities, trends, events, and sentiment on geo-located data and on distributed smart city infrastructures;
- Cloud to the edge and fog evolving data streams analytics with efforts on the design and implementation of cloud to the edge frameworks for health, wellbeing, energy, and social networks domains at which decentralized technologies are also explored and validated;
- Social and sustainable innovation, creative and cultural platforms designs and implementations via data driven technologies which establish methods, tools and frameworks at which content and usage are reflected and analysed under ethical and protected data regulations and norms.