Citation Blume T & Scherp A (2018) Towards an incremental schema-level index for distributed linked open data graphs. In: Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", LWDA 2018. CEUR Workshop Proceedings, 2191. LWDA 2018: Lernen, Wissen, Daten, Analysen, Mannheim, Germany, 22.08.2018-24.08.2018. Aachen, Germany: CEUR Workshop Proceedings, pp. 61-72.
Abstract Semi-structured, schema-free data formats are used in many applications because their flexibility enables simple data exchange. Especially graph data formats like RDF have become well established in the Web of Data. For the Web of Data, it is known that data instances are not only added, changed, and removed regularly, but that their schemas are also subject to enormous changes over time. Unfortunately, the collection, indexing, and analysis of the evolution of data schemas on the web is still in its infancy. To enable a detailed analysis of the evolution of Linked Open Data, we lay the foundation for the implementation of incremental schema-level indices for the Web of Data. Unlike existing schema-level indices, incremental schema-level indices have an efficient update mechanism to avoid costly recomputations of the entire index. This enables us to monitor changes to data instances at schema-level, trace changes, and ultimately provide an always up-to-date schema-level index for the Web of Data. In this paper, we analyze in detail the challenges of updating arbitrary schema-level indices for the Web of Data. To this end, we extend our previously developed meta model FLuID. In addition, we outline an algorithm for performing the updates.