Title: HyGraph – a hybrid data model combining graph and time-series data

Project duration: 2023-2026

Research Area: Graph-based Artificial Intelligence

Graphs are simple yet highly expressive data structures for modeling and analyzing relationships between real-world objects. As the structure and content of graphs is continuously changing, e.g. in social networks or transport and mobility networks, novel data models and analysis mechanisms are needed. Our goal is to develop HyGraph, a new hybrid data model that seamlessly combines temporal graphs with time-series and enables high-frequency updates through graph streams. This combination in a unified hybrid model paves the way to novel unprecedented query, analysis, data mining and machine learning tasks. Please check the project website for more information.

Project members

• Mouna Ammar
• Christopher Rost
• Prof. Dr. Erhard Rahm
• Angela Bonifati
• Riccardo Tommasini

Partner

• LIRIS UMR 5205 – IMAGE AND INFORMATION SYSTEMS COMPUTING LABORATORY – University of Lyon, France

Funding / Cooperation

• DFG : from the german side
• ANR : from the french side