TY - JOUR

T1 - DESERT

T2 - A Continuous SPARQL Query Engine for On-Demand Query Answering

AU - Karim, Farah

AU - Lytra, Ioanna

AU - Mader, Christian

AU - Auer, Sören

AU - Vidal, Maria Esther

N1 - (c) 2018 World Scientific Publishing Company

PY - 2018/9

Y1 - 2018/9

N2 - The Internet of Things (IoT) has been rapidly adopted in many domains ranging from household appliances e.g. ventilation, lighting, and heating, to industrial manufacturing and transport networks. Despite the, enormous benefits of optimization, monitoring, and maintenance rendered by IoT devices, an ample amount of data is generated continuously. Semantically describing IoT generated data using ontologies enables a precise interpretation of this data. However, ontology-based descriptions tremendously increase the size of IoT data and in presence of repeated sensor measurements, a large amount of the data are duplicates that do not contribute to new insights during query processing or IoT data analytics. In order to ensure that only required ontology-based descriptions are generated, we devise a knowledge-driven approach named DESERT that is able to on-D––emand factorizE–– and S––emantically E––nrich stR––eam daT––a. DESERT resorts to a knowledge graph to describe IoT stream data; it utilizes only the data that is required to answer an input continuous SPARQL query and applies a novel method of data factorization to reduce duplicated measurements in the knowledge graph. The performance of DESERT is empirically studied on a collection of continuous SPARQL queries from SRBench, a benchmark of IoT stream data and continuous SPARQL queries. Furthermore, data streams with various combinations of uniform and varying data stream speeds and streaming window size dimensions are considered in the study. Experimental results suggest that DESERT is capable of speeding up continuous query processing while creates knowledge graphs that include no replications.

AB - The Internet of Things (IoT) has been rapidly adopted in many domains ranging from household appliances e.g. ventilation, lighting, and heating, to industrial manufacturing and transport networks. Despite the, enormous benefits of optimization, monitoring, and maintenance rendered by IoT devices, an ample amount of data is generated continuously. Semantically describing IoT generated data using ontologies enables a precise interpretation of this data. However, ontology-based descriptions tremendously increase the size of IoT data and in presence of repeated sensor measurements, a large amount of the data are duplicates that do not contribute to new insights during query processing or IoT data analytics. In order to ensure that only required ontology-based descriptions are generated, we devise a knowledge-driven approach named DESERT that is able to on-D––emand factorizE–– and S––emantically E––nrich stR––eam daT––a. DESERT resorts to a knowledge graph to describe IoT stream data; it utilizes only the data that is required to answer an input continuous SPARQL query and applies a novel method of data factorization to reduce duplicated measurements in the knowledge graph. The performance of DESERT is empirically studied on a collection of continuous SPARQL queries from SRBench, a benchmark of IoT stream data and continuous SPARQL queries. Furthermore, data streams with various combinations of uniform and varying data stream speeds and streaming window size dimensions are considered in the study. Experimental results suggest that DESERT is capable of speeding up continuous query processing while creates knowledge graphs that include no replications.

KW - continuous SPARQL query

KW - Internet of things

KW - semantic enrichment

KW - stream data

U2 - 10.1142/S1793351X18400172

DO - 10.1142/S1793351X18400172

M3 - Article

AN - SCOPUS:85053696463

VL - 12

SP - 373

EP - 397

JO - International Journal of Semantic Computing

JF - International Journal of Semantic Computing

SN - 1793-351X

IS - 3

ER -