Actian’s Steffen Kläbe Awarded for Dissertation on Modern Data Analytics

Cloud computing has been one of the most groundbreaking technologies of the last two decades, but where is it heading? As Actian Senior Researcher Steffen Kläbe explains in a dissertation, cloud computing has become a transformative technology, driven by advancements in distributed systems, virtualizations, and fast networks. It delivers key benefits such as elastic and cost-efficient use of resources, ease of use, a low barrier of entry to managed environments, and accessibility for heterogeneous hardware.

As a result, software design must be rethought to natively support the benefits of cloud environments. These are just some of the ideas Kläbe presents in his Ph.D. thesis “Modern Data Analytics in the Cloud Era.”

Kläbe was recently honored with the Dissertation Award for Information Systems by the German Informatics Society for his thesis. He received the award at the Business, Technology, and Web (BTW) 2025 Conference in Bamberg, Germany.

The prestigious award recognizes the top doctoral research in the field over the past two years, making it one of the most significant awards in this sector. Kläbe, who works in Actian’s llmenau office in Thuringia, Germany, was one of only two people to receive the award this year, underscoring the impact and importance of his research for modern enterprises.

Receiving Industry Recognition and Making a Business Impact

At the BTW 2025 conference, Kläbe presented a spotlight paper on Actian Vector 7.0 to approximately 200 industry and research leaders who were in the audience. He shared how Vector, a highly performant analytics database, not only excels in performance, but also offers features for integration with modern ecosystems and for improving ease-of-use for customers.

Kläbe’s co-worker Stefan Hagedorn, Principal Software Engineer at Actian, also presented at the event. Hagedorn’s topic, “Experiences of Implementing In-database TPCx-AI,” explained his team’s insights and learnings when using Vector to perform machine learning workloads of the standardized TPCx-AI benchmark.

The discussions that followed these presentations reinforced the practical implications of Kläbe’s and Hagedorn’s research to address real-world data challenges. These conversations highlighted the growing need for high-performance solutions that can handle modern analytics workloads.

Photo by Maximilian Schüle

A Spotlight on Cloud-Based Data Management Systems

Kläbe dissertation addresses the growing challenges of traditional databases to handle modern use cases. In today’s environment where real-time analytics and machine learning workloads are reshaping data management, Kläbe proposes solutions that focus on cloud computing and modern analytics as key areas that change the way systems are designed and used.

His research provides practical solutions to optimize scalability, elasticity, and performance in cloud computing. Key areas of focus include:

• Elastic scaling for distributed database engines, ensuring seamless resource allocation based on workload demands.
• Approximate database constraints to match fine-grained data ingestion from numerous sources and the need for real-time analytics on live data.
• A novel data partitioning method that, with approximate constraints enabled, offers robust query performance.

These advancements align with the evolving needs of enterprises. By optimizing platforms that efficiently process vast amounts of real-time data while remaining cost-effective and scalable, organizations can gain actionable insights faster and improve decision-making. 

Enhancing Modern Workloads With Machine Learning Integration

Going beyond infrastructure optimization, Kläbe’s research delves into modern database workloads, particularly with regards to machine learning and user-defined functions (UDFs). His research covers:

• Efficient support for UDFs, especially for integrating solutions from Python.
• Engine-level machine learning inference integration, streamlining the application of predictive models within database systems.

Kläbe’s thesis ultimately investigates analytical database management systems and their interaction points with the cloud environment. He identifies challenges that must be addressed to deliver and support the benefits of the cloud when compared to traditional, on-premises deployments.

Shaping the Future of Data Management

Kläbe’s research provides a roadmap for cloud computing and database evolution, making modern analytics more accessible and efficient. His award-winning work helps to advance both a business and an academic understanding of the future of data management. For example, elastic scaling, the Python UDF, and in-database machine learning inference features are now part of Actian products.

As the cloud era continues to redefine how data is managed, Kläbe’s work offers insights to pave the way for more efficient, intelligent, and scalable data solutions. For those interested in exploring the full dissertation, it’s available here, with a 10-page summary accessible here.

This is not the first time Kläbe has been recognized for his research. At a 2023 joint conference by EDBT/ICDT in Greece, he received an award for Best Paper for his research on Patched Multi-Key Partitioning for Robust Query Performance.

About Actian Corporation

Actian makes data easy. Our data platform simplifies how people connect, manage, and analyze data across cloud, hybrid, and on-premises environments. With decades of experience in data management and analytics, Actian delivers high-performance solutions that empower businesses to make data-driven decisions. Actian is recognized by leading analysts and has received industry awards for performance and innovation. Our teams share proven use cases at conferences (e.g., Strata Data) and contribute to open-source projects. On the Actian blog, we cover topics ranging from real-time data ingestion, data analytics, data governance, data management, data quality, data intelligence to AI-driven analytics.