Figure - available from: Journal on Data Semantics
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Overlaps between the different sets of rules considered in the study. The numbers indicate the number of rules in each set or intersection
Source publication
Stream reasoning is one of the building blocks giving semantic web an advantage in the race for the real-time web. This paper demonstrates implementation of materialisation-based reasoning using an event processor supporting networks of specification-compliant SPARQL Update rules. Collections of rules coded in SPARQL leave the rule implementation e...
Citations
... An efficient event processing method is proposed in the literature [16]. In this method, events are represented in RDF format, and subscriptions containing matching conditions are described in SPARQL, a standard query language based on RDF format data. ...
With the rapid development of society and the popularity of smart devices, the volume of information sent and received is increasing day by day. It has become very difficult to accurately and efficiently match a large number of events with a large number of subscriptions, and the event and subscription matching speed can no longer meet demand. To speed up the matching speed of events and subscriptions, this paper uses similarity and correlation to optimize the clustering operation and increase the data transfer and data throughput of the category fusion strategy. Firstly, the clustering operation is performed on the subscription messages, and the category to which the events belong is found according to the clustering result. Subsequently, in the category, the subscriptions matching the events are found. An on-the-fly subscription publishing algorithm is proposed to coordinate spatial information and event attribute information to handle not only the matching operation of events and subscriptions on-the-fly but also to perform subscription updates and category updates on the distributed environment on-the-fly. It can also perform clustering operations and matching operations instantly without prior knowledge. We design a distributed system for the publish–subscribe algorithm and propose a load balancing strategy for this algorithm on the distributed system. Subsequently, we experimentally validate the proposed publish–subscribe algorithm in this paper by building our own cluster and using real data.
... INSTANS does not support windows over streams, but similar behaviors can be defined manually in queries if timestamps are included for all events. Support for reasoning has been demonstrated for materialization-based reasoning using collections of rules coded in SPARQL [109]. The approach of using pure SPARQL, however, means that the model lacks any highlevel abstractions for expressing temporal reasoning. ...
... Also, ETALIS supports recursive production rules, which can be used to support reasoning. Materialization-based stream reasoning covering RDFS and OWL 2 RL using networks of SPARQL Update rules has also been demonstrated in INSTANS [109]. ...
... Existing work has shown that Datalog can be translated to SPARQL, and vice versa (Polleres, 2007;Maier et al., 2018) and that it is feasible to use either SPARQL or Datalog for reasoning over streams (Rinne and Nuutila, 2017;Margara et al., 2018). The choice of modeling rules as Datalog or SPARQL is a trade-off between semantic expressibility and computation performance. ...
