WiseKG: Balanced Access to Web Knowledge Graphs

Amr Azzam, Christian Aebeloe, Gabriela Montoya, Ilkcan Kelles, Axel Polleres, Katja Hose

Publikation: Beitrag in Buch/KonferenzbandBeitrag in Sammelwerk

Abstract

SPARQL query services that balance processing between clients and servers become more and more essential to handle the increasing load for open and decentralized knowledge graphs on the Web. To this end, Linked Data Fragments (LDF) have introduced a foundational framework that has sparked research exploring a spectrum of potential Web querying interfaces in between server-side query processing via SPARQL endpoints and client-side query processing of data dumps. Current proposals in between typically suffer from imbalanced load on either the client or the server. In this paper, to the best of our knowledge, we present the first work that combines both client-side and server-side query optimization techniques in a truly dynamic fashion: we introduce WiseKG, a system that employs a cost model that dynamically delegates the load between servers and clients by combining client-side processing of shipped partitions with efficient server-side processing of star-shaped sub-queries, based on current server workload and client capabilities. Our experiments show that WiseKG significantly outperforms state-of-the-art solutions in terms of average total query execution time per client, while at the same time decreasing network traffic and increasing server-side availability.
OriginalspracheEnglisch
Titel des SammelwerksProceedings of the Web Conference 2021
Herausgeber*innen Jure Leskovec, Marko Grobelnik, Marc Najork, Jie Tang, Leila Zia
ErscheinungsortNew York
VerlagAssociation for Computing Machinery
Seiten1422 - 1434
ISBN (Print)978-1-4503-8312-7
DOIs
PublikationsstatusVeröffentlicht - 2021

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