A Window into the Multiple Views of Linked Data

Abstract

RDF mapping engines enable access to existing heterogeneous data sources as RDF Knowledge Graph (KG). However, these mapping engines have two challenges: i) processing streaming data sources with changing velocity efficiently, ii) and providing a rich variety in the format of the generated KG output. To tackle these challenges, I carry out my research in 3 steps. I will first design a highly scalable data stream mapping solution to handle dynamic velocity of streaming data sources. Preliminary results indicate that our stream mapping solution outperforms state of the art engines with lower latency, constant memory usage, and higher throughput. I will then refine this architecture in a task-based fashion, aiming to be a common architecture for any kind of mapping. Finally, I will utilize the common modular mapping architecture and extend it with a component to derive an intermediate representation of the data mapping process, enabling heterogeneous to heterogeneous data mapping. The combined solution will provide a highly scalable heterogeneous to heterogeneous data stream mapping engine, enabling us to have multiple views of the underlying KG.