Beyond Querying: A Scalable Architecture for Linked Data Monitoring

Tracking #: 2589-3803

This paper is currently under review
Authors: 
Burak Yönyül
Oylum Alatlı
Rıza Cenk Erdur
Oğuz Dikenelli

Responsible editor: 
Guest Editors Web of Data 2020

Submission type: 
Full Paper
Abstract: 
Monitoring the data sources for possible changes is an important consumption requirement for applications running in interaction with the web of data. In this paper, MonARCh (Monitoring Architecture for Result Changes) which is a scalable architecture for monitoring the result changes of registered SPARQL queries in the linked data environment has been introduced. Although MonARCh can be comprehended as a publish/subscribe system in the general sense, it differs in how the communication with the data sources are realized. The reason behind this is that the data sources in the linked data environment do not publish the changes on the data. MonARCh provides the necessary communication infrastructure between the data sources and the consumers for the notification of changes. Users subscribe SPARQL queries to the system which are then converted to federated queries. MonARCh periodically checks for updates by re-executing sub-queries and notifies users in case of any result change. In addition, to provide scalability MonARCh takes the advantage of concurrent computation of the actor model and a parallel join algorithm for faster query execution and result generation. The design science methodology has been used both during the design and implementation stage and for the evaluation of the architecture.
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Under Review