8 citations · 20 across the 7 of their papers we have counts for
8 papers
Improved Join Order Optimization for Database Queries using Hybrid Quantum-Classical Approaches for QUBO Problems
Nitin Nayak, Tobias Winker, Umut Çalıkyılmaz +2
Efficient query optimization is crucial for relational database systems, especially for optimizing join orders in complex queries. This work introduces a hybrid approach that integ…
Opportunities and Challenges for Data Quality in the Era of Quantum Computing
Sven Groppe, Valter Uotila, Jinghua Groppe
In an era where data underpins decision-making across science, politics, and economics, ensuring high data quality is of paramount importance. Conventional computing algorithms for…
OptiMA: A Transaction-Based Framework with Throughput Optimization for Very Complex Multi-Agent Systems
Umut Çalıkyılmaz, Nitin Nayak, Jinghua Groppe +1
In recent years, the research of multi-agent systems has taken a direction to explore larger and more complex models to fulfill sophisticated tasks. We point out two possible pitfa…
QCardEst/QCardCorr: Quantum Cardinality Estimation and Correction
Tobias Winker, Jinghua Groppe, Sven Groppe
Cardinality estimation is an important part of query optimization in DBMS. We develop a Quantum Cardinality Estimation (QCardEst) approach using Quantum Machine Learning with a Hyb…
Automated Archival Descriptions with Federated Intelligence of LLMs
Jinghua Groppe, Andreas Marquet, Annabel Walz +1
Enforcing archival standards requires specialized expertise, and manually creating metadata descriptions for archival materials is a tedious and error-prone task. This work aims at…
Variables are a Curse in Software Vulnerability Prediction
Jinghua Groppe, Sven Groppe, Ralf Möller
Deep learning-based approaches for software vulnerability prediction currently mainly rely on the original text of software code as the feature of nodes in the graph of code and th…
Research Trends for the Interplay between Large Language Models and Knowledge Graphs
Hanieh Khorashadizadeh, Fatima Zahra Amara, Morteza Ezzabady +7
This survey investigates the synergistic relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs), which is crucial for advancing AI's capabilities in understan…
Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text
Hanieh Khorashadizadeh, Nandana Mihindukulasooriya, Sanju Tiwari +2
Knowledge graphs can represent information about the real-world using entities and their relations in a structured and semantically rich manner and they enable a variety of downstr…