Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/27853
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Blume, Till
Scherp, Ansgar
Title: Towards an incremental schema-level index for distributed linked open data graphs
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.
Issue Date: 31-Dec-2018
Series/Report no.: CEUR Workshop Proceedings, 2191
Conference Name: LWDA 2018: Lernen, Wissen, Daten, Analysen
Conference Dates: 2018-08-22 - 2018-08-24
Conference Location: Mannheim, Germany
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.
Status: AM - Accepted Manuscript
Rights: Copyright © 2018 for this paper by its authors. Copying permitted for private and academic purposes

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