FlexRML: A Flexible and Memory Efficient Knowledge Graph Materializer

Freund M, Schmid SJ, Dorsch R, Harth A (2024)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Event location: Heraklion, Crete GR

URI: https://2024.eswc-conferences.org/wp-content/uploads/2024/04/146640373.pdf

Abstract

We present FlexRML, a flexible and memory efficient software resource for interpreting and executing RML mappings. As a knowledge graph materializer, FlexRML can operate on a wide range of systems, from cloud-based environments to edge devices, as well as resourceconstrained IoT devices and real-time microcontrollers. The primary goal of FlexRML is to balance memory efficiency with fast mapping execution. This is achieved by using C++ for the implementation and a result size estimation algorithm that approximates the number of N-Quads generated and, based on the estimate, optimizes bit sizes and data structures used to save memory in preparation for mapping execution. Our evaluation shows that FlexRML’s adaptive bit size and data structure selection results in higher memory efficiency compared to conventional methods. When benchmarked against state-of-the-art RML processors, FlexRML consistently shows lower peak memory consumption across different datasets while delivering faster or comparable execution times.

Authors with CRIS profile

Michael Freund Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme Sebastian Josef Schmid Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme Andreas Harth Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Freund, M., Schmid, S.J., Dorsch, R., & Harth, A. (2024). FlexRML: A Flexible and Memory Efficient Knowledge Graph Materializer. In Proceedings of the 21st Extended Semantic Web Conference (ESWC2024). Heraklion, Crete, GR.

MLA:

Freund, Michael, et al. "FlexRML: A Flexible and Memory Efficient Knowledge Graph Materializer." Proceedings of the 21st Extended Semantic Web Conference (ESWC2024), Heraklion, Crete 2024.

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