Enabling Operator-Agnostic Complex Processing of Massive Graphs through Higher-Order Pipeline Architectures

Thursday, June 13, 2024 | 03:00 pm (CET) | Room: S.2.42 | Alpen-Adria Universität Klagenfurt

Dr. Daniel Thilo Schroeder | Researcher at SINTEF

Abstract: In an era where efficient processing of complex graph structures is increasingly vital across various domains, such as social networks and biological system modeling, there arises a need for robust computational architectures. This Kolloquium introduces a novel approach that incorporates a higher-order pipeline architecture, aiming to enhance graph data processing. Integrating functional programming with object-oriented principles facilitates complex data processing through an intuitive, modular system. Central to this system is the notion of treating computation units as first-class entities, which promotes modular and type-safe environments. Additionally, the system includes a higher-order traversal abstraction to support flexible data manipulation strategies, a directed data-transfer protocol for efficient data flow management, and an operator model that enhances the robust lifecycle management of operations. The adaptability and performance of this system are further augmented by its capability to incorporate various graph computation models, such as vertex-centric and edge-centric processing. This approach contributes to scalable solutions that meet diverse computational needs without overcomplicating the user interface by offering a structured yet adaptable method for graph data handling.

Bio: Daniel Thilo Schroeder is a Research Scientist at SINTEF’s Smart Data group, where he develops high-performance, scalable platforms for processing and analyzing extreme data using massive graph representations, facilitated by serverless computing. He is actively involved in the enRichMyData project, focusing on creating FAIR-compliant datasets essential for Big Data and AI applications, and the GraphMassivizer Project, which seeks to revolutionize data processing through sustainable, advanced graph technologies. Previously, during his postdoctoral research at Simula’s Department for High-Performance Computing, he developed new computational frameworks for Graph Neural Networks to enhance deep learning for unstructured data. Daniel earned his PhD by focusing on the early detection and analysis of online misinformation, aiming to develop strategies for its mitigation. Contact Daniel at daniel.t.schroeder@sintef.no or visit https://danielthiloschroeder.org/