Paper Info
Title
Sparkwave: continuous schema-enhanced pattern matching over RDF data streams
Abstract
Data streams, often seen as sources of events, have appeared on the Web. Stream processing on the Web needs however to cope with the typical openness and heterogeneity of the Web environment. Semantic Web technologies, meant to facilitate data integration in an open environment, can help to address heterogeneities across multiple streams. In this paper we present Sparkwave, an approach for continuous pattern matching over RDF data streams. Sparkwave is based on the Rete algorithm, which allows efficient and truly continuous processing of data streams. Sparkwave is able to leverage RDF schema information associated to data streams to compute entailments, so that implicit knowledge is taken into account for pattern matching. In addition, it further extends Rete to support time-based sliding windows and static data instances, to cope with the streaming nature of processed data and real-world use cases.
Year
DOI
Venue
2012
10.1145/2335484.2335491
DEBS
Keywords
Field
DocType
processed data,static data instance,semantic web technology,rdf schema information,web environment,continuous schema-enhanced pattern,data integration,continuous pattern,rete algorithm,rdf data stream,data stream,stream processing,rete,rdf,sliding window,semantic web,pattern matching,data integrity,use case
Data integration,Data mining,Data stream mining,Computer science,Data Web,Semantic Web,Linked data,Rete algorithm,RDF Schema,RDF
Conference
Citations 
PageRank 
References 
39
1.27
22
Authors
3
Name
Order
Citations
PageRank
Srdjan Komazec1634.91
Davide Cerri2543.17
Dieter Fensel35545662.62