Dynatrace Research is shaping the technological future of Dynatrace in the domain of software intelligence.

We are applying and advancing existing research by collaborating, influencing, and contributing to various domains: software intelligence, distributed data systems, real-time analytics, generative AI, and application security—putting a focus on future technologies to solve the most advanced use cases.

Real-Time Analytics

Research on methods for real-time processing and analysis of massive data streams.

Distributed Data Systems

Researching and inventing new technologies and algorithms for the next generation of distributed data systems.

Cloud-Native Security

Research on methods that are capable of automatically identifying threats in modern cloud environments.

Generative Artificial Intelligence

Research in generative AI, harnessing the power of large language models

Meet our partners

Collaboration is at the very core of the work developed at Dynatrace. Check out our key partnerships in the academic field and see how we collaborate for the most innovative results.

Co-Innovation Lab @ LIT Open Innovation Center

Dynatrace Research is part of the Linz Institute of Technology (LIT) Open Innovation Center, a research and development center focused on collaboration projects at the interface of science, academia and business.

AIDOaRt

AIDOaRt proposes to enhance the engineering process with AI-augmented methods (AIOps), integrating DevOps and Model Driven Engineering (MDE) principles, to observe and analyse collected data from both runtime and design time artefacts in rapid CSE cycles.

Christian Doppler Laboratory

The Christian Doppler Laboratory for Monitoring and Evolution of Very-Large-Scale Software Systems (2013-2020), has been working on theoretical foundations as well as methods, tools, and infrastructures for managing and supporting the monitoring and evolution of VLSS.

Technical University of Graz

Together with the TU Graz, Dynatrace has called to life the Dynatrace Scholarship, an endowment of 12,000 euros, awarded to a one of the students on the “Dean’s List” for the great work and efforts.

Alpen-Adria-University Klagenfurt

In a cooperative effort with the Alpen-Adria University in Klagenfurt, we are looking for the best IT theses works at the university in the first round of our DO*IT*TA.

Browse our many contributions to the global research community

Our researchers aspire to make discoveries, drive solutions, and share their findings, driving the progress in the field.

A Comprehensive Benchmarking Analysis of Fault Recovery in Stream Processing Frameworks

Nowadays, several software systems rely on stream processing architectures to deliver scalable performance and handle large volumes of data in near real time. Stream processing frameworks facilitate scalable computing by distributing the application's execution across multiple machines. Despite performance being extensively studied, the measurement...

Adriano Vogel, Sören Henning, Esteban Perez-Wohlfeil, Otmar Ertl, Rick Rabiser

| 18th ACM International Conference on Distributed and Event-Based Systems (DEBS'24) | 2024

ShuffleBench: A Benchmark for Large-Scale Data Shuffling Operations with Distributed Stream Processing Frameworks

Distributed stream processing frameworks help building scalable and reliable applications that perform transformations and aggregations on continuous data streams. This paper introduces ShuffleBench, a novel benchmark to evaluate the performance of modern stream processing frameworks. In contrast to other benchmarks, it focuses on use cases where s...

Sören Henning, Adriano Vogel, Michael Leichtfried, Otmar Ertl, Rick Rabiser

| ICPE '24: Proceedings of the 15th ACM/SPEC International Conference on Performance Engineering | 2024

UltraLogLog: A Practical and More Space-Efficient Alternative to HyperLogLog for Approximate Distinct Counting

Since its invention HyperLogLog has become the standard algorithm for approximate distinct counting. Due to its space efficiency and suitability for distributed systems, it is widely used and also implemented in numerous databases. This work presents UltraLogLog, which shares the same practical properties as HyperLogLog. It is commutative, idempote...

Otmar Ertl

| arXiv preprint arXiv:2308.16862 | 2023

A Dataset Perspective on Offline Reinforcement Learning

The application of Reinforcement Learning (RL) in real world environments can be expensive or risky due to sub-optimal policies during training. In Offline RL, this problem is avoided since interactions with an environment are prohibited. Policies are learned from a given dataset, which solely determines their performance. Despite this fact, how da...

Kajetan Schweighofer, Andreas Radler, Marius-Constantin Dinu, Markus Hofmarcher, Vihang Patil, Angela Bitto-Nemling, Hamid Eghbal-zadeh, Sepp Hochreiter

| 2022

An Approach for Ranking Feature-based Clustering Methods and its Application in Multi-System Infrastructure Monitoring

Companies need to collect and analyze time series data to continuously monitor the behavior of software systems during operation, which can in turn be used for performance monitoring, anomaly detection or identifying problems after system crashes. However, gaining insights into common data patterns in time series is challenging, in particular, when...

Andreas Schörgenhumer; Thomas Natschläger; Paul Grünbacher; Mario Kahlhofer; Peter Chalupar; Hanspeter Mössenböck

| 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) | 2021

See all publications

Meet our research team

Our team of research experts drives advancements in software development, machine learning, generative AI and data analytics, to develop cutting-edge solutions ready to be applied to real-world use cases.