Projects and Theses

Query Processing on Graph Databases

Screenshot of an example graph in neo4j

Large-scale data analysis increasingly focuses on the relationships between entities and the networks that are formed. From analyzing traffic in road networks to user interactions in social networks, the field of graph analysis has found many key applications among different domains. The need for efficient graph data management has motivated the development of various graph database management systems (DBMS) and processing frameworks. Despite the increasing need for efficient methods for querying and analyzing graph data, existing graph DBMS lack some key components thus failing to reach their full potential. Projects in this area aim at extending and improving the current state-of-the-art on graph DBMS by adapting existing approaches and developing new ones for graph data indexing and graph query opimization with a particular focus on path queries.

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Graph Databases and Machine Learning

Query optimization, i.e., the translation of a declarative query statement into an efficient query execution plan, is one of the central problems of database systems research. A recent idea towards improving query optimization in relational databases is to employ Machine Learning. However, despite the vast amount of research on both traditional and ML-based methods for query optimization on relational databases, the same is not the cases for non-relational database systems. Acknowledging the fact that an increasing number of data sets is graph-structured and, in particular, represented in the Resource Description Framework (RDF)  or in the Property Graph Mode,  we argure that graph database systems can profit from similar machine learning techniques as relational database systems. Projects in this area aim at proposal exploring open research problems with regard to the use of machine learning for optimzing query processing in graph databases.

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Spatial Networks and Trajectories

The recent developments in the field of location-based services and GPS tracking have resulted in the accumulation of huge collections of users’ location trajectories of driving, cycling, hiking, etc. Projects in this area aim at tackling many interesting problems that arise in the area of trajectory data processing for mobility analytics. 

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