https://speakerdeck.com/itstarik 2025-06-26 08:25:31 -0400 With the continued growth of the Semantic Web, RDF (Resource Description Framework) has become a foundational standard for modeling structured knowledge. However, managing large-scale RDF datasets remains a challenge, especially in distributed environments where effective data partitioning is critical to performance. Existing RDF triplestores predominantly rely on triple-based partitioning strategies, which, although simple, often scatter semantically related data across partitions, resulting in increased query latency and communication overhead. Another important limitation is the lack of involvement of database administrators (DBAs) in the partitioning process. Current systems do not provide mechanisms for DBAs to leverage their domain expertise to influence or adjust the partitioning. As a result, partitioning remains static and rigid, reducing the systems adaptability to evolving workloads or semantic structures. To address these limitations, the PQDAG team at the LIAS laboratory (ISAEENSMA) proposed SemPart, a fragment-based RDF partitioning framework designed to introduce semantic coherence and expert-driven control into the partitioning process by enabling a range of physical transformations on RDF fragments. While SemPart had been introduced as a theoretical concept, this work focuses on its practical implementation. Our contribution consists of designing and developing the full SemPart framework and integrating it into the distributed PQDAG system. By transforming the theoretical principles of SemPart into a functional system, this work enables flexible, semantically rich, and performance-oriented RDF partitioning, with potential applications in dynamic and resource-constrained environments. With the continued growth of the Semantic Web, RDF (Resource Description Framework) has become a foundational standard for modeling structured knowledge. However, managing large-scale RDF datasets remains a challenge, especially in distributed environments where effective data partitioning is critical to performance. Existing RDF triplestores predominantly rely on triple-based partitioning strategies, which, although simple, often scatter semantically related data across partitions, resulting in increased query latency and communication overhead. Another important limitation is the lack of involvement of database administrators (DBAs) in the partitioning process. Current systems do not provide mechanisms for DBAs to leverage their domain expertise to influence or adjust the partitioning. As a result, partitioning remains static and rigid, reducing the systems adaptability to evolving workloads or semantic structures. To address these limitations, the PQDAG team at the LIAS laboratory (ISAEENSMA) proposed SemPart, a fragment-based RDF partitioning framework designed to introduce semantic coherence and expert-driven control into the partitioning process by enabling a range of physical transformations on RDF fragments. While SemPart had been introduced as a theoretical concept, this work focuses on its practical implementation. Our contribution consists of designing and developing the full SemPart framework and integrating it into the distributed PQDAG system. By transforming the theoretical principles of SemPart into a functional system, this work enables flexible, semantically rich, and performance-oriented RDF partitioning, with potential applications in dynamic and resource-constrained environments. Tue, 24 Jun 2025 00:00:00 -0400 https://speakerdeck.com/itstarik/sempart-yet-another-rdf-partitioning-strategy-in-action https://speakerdeck.com/itstarik/sempart-yet-another-rdf-partitioning-strategy-in-action