Details
| Original language | English |
|---|---|
| Pages (from-to) | 373-397 |
| Number of pages | 25 |
| Journal | International Journal of Semantic Computing |
| Volume | 12 |
| Issue number | 3 |
| Publication status | Published - Sept 2018 |
Abstract
Keywords
- continuous SPARQL query, Internet of things, semantic enrichment, stream data
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Linguistics and Language
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Artificial Intelligence
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In: International Journal of Semantic Computing, Vol. 12, No. 3, 09.2018, p. 373-397.
Research output: Contribution to journal › Article › Research › peer review
}
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 -