| Content: | Graph databases [1] are a powerful tool for managing and
querying highly connected data. Unlike traditional relational
databases, graph databases use nodes, edges, and
properties to represent and store information. Nodes
represent entities or discrete objects, such as individuals
or products, in a graph data model, while edges define the
relationships between these entities, hold data in
properties, and are classified by labels. For example, in a
social network, a node could represent a user, with
properties such as "name" and "age," and it could be
connected to other users by relationships like "FRIEND" or
"FOLLOW," each with its own properties, such as "since"
or "interaction_count."
Graph databases are widely adopted across various industries for applications such
as social networks, recommendation systems, fraud detection, and network analysis.
Leading technology companies such as LinkedIn, Facebook, Amazon, Bosch, Siemens,
and Samsung leverage graph databases to enhance their services and deliver more
personalized user experiences.
This seminar provides an overview of existing graph database technologies,
covering fundamental concepts such as graph data modeling, query languages (e.g.,
Cypher, GQL,GraphQL,PGQL,SPARQL, etc.), and performance optimization, along
with their real-world applications. Participants, based on their interests, will also have
the opportunity to explore extended graph database-related research topics, such as
distributed graph processing, and the integration of graph databases with other
emerging data management technologies.
Procedure
Following the introductory sessions, participants will choose a topic of interest and
work on a technical report. The report will then be peer-reviewed by fellow
participants. At the end, participants will submit the finalized report as part of the
final assessment. The seminar will be conducted in English, and both the
presentations and seminar reports must be delivered in English.
Dr. Ratan Bahadur Thapa
ratan.thapa@ki.uni-stuttgart.de
Graph Database Management
Seminar Winter 2026 Analytic Computing Group
The seminar is designed for master’s students in the field of computer science and
related study programs, such as software engineering and AI & data science. While no
prior experience with graph databases is required, basic knowledge of relational
databases is necessary.
Prof. Dr. Steffen Staab
steffen.staab@ki.uni-stuttgart.de
[1] Robinson, Ian, et al. “Graph Databases: New Opportunities for Connected Data,” July
2015 https://dl.acm.org/doi/10.5555/2846367 |
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