Integration of sensor data by means of an event abstraction layer

Diese Doktorarbeit stellt eine Methodik vor, die aus Zeitreihen von Sensorbeobachtungen Events (Ereignisse) nahezu in Echtzeit ableiten und darstellen kann. Die Analyse der Sensor-Daten erfolgt unter zur Hilfenahme von Prozessen und Technologien des 'Semantic event processing'. Aus den Dat...

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Verfasser: Llaves Arellano, Alejandro
Weitere Beteiligte: Kuhn, Werner (Gutachter)
FB/Einrichtung:FB 14: Geowissenschaften
Dokumenttypen:Dissertation/Habilitation
Medientypen:Text
Publikation in MIAMI:09.01.2014
Datum der letzten Änderung:27.07.2015
Angaben zur Ausgabe:[Electronic ed.]
Schlagwörter:Event Verarbeitung; Raumzeitliche Modellierung; Datenzusammenführung; Semantische Interoperabilität; Sensoren event processing; spatio-temporal modelling; data integration; semantic interoperability; sensors
Fachgebiet (DDC):000: Informatik, Wissen, Systeme
550: Geowissenschaften, Geologie
Lizenz:InC 1.0
Sprache:English
Format:PDF-Dokument
URN:urn:nbn:de:hbz:6-54359642460
Permalink:https://nbn-resolving.de/urn:nbn:de:hbz:6-54359642460
Onlinezugriff:diss_llaves_arellano.pdf
Inhaltsverzeichnis:
  • 1 introduction 1
  • 1.1 Overview 1
  • 1.2 Motivation 3
  • 1.2.1 The data avalanche in the Digital Earth 3
  • 1.2.2 Modelling spatio-temporal change 4
  • 1.2.3 Semantic interoperability problems 5
  • 1.3 Research questions 6
  • 1.4 Scope and Limitations of the Thesis 7
  • 1.5 Thesis Outline 9
  • 2 background and related work 11
  • 2.1 Sensors and Environmental Monitoring 11
  • 2.2 Semantic Web and Sensor Web 12
  • 2.3 Event Modelling 14
  • 2.3.1 The nature of events 14
  • 2.3.2 Events in DOLCE 16
  • 2.3.3 Event modelling in GIScience 18
  • 2.3.4 Events and observations in the Semantic Sensor
  • Network ontology 20
  • 2.3.5 Generic event models 21
  • 2.4 Event Processing 24
  • 2.4.1 ESP vs. CEP 25
  • 2.4.2 EDA, SOA and the pub/sub paradigm 26
  • 2.4.3 Semantic event processing for general purpose 27
  • 2.4.4 Inferring events from sensor observations in GIScience
  • 28
  • 2.5 Eventing Standards 30
  • 3 inferring events from in situ sensor observations 33
  • 3.1 My Perspective of Events 33
  • 3.2 The Event Abstraction Layer 34
  • 3.3 Semantic Annotation of Event Patterns 35
  • 3.4 The Event Abstraction Ontology 37
  • 4 implementation of the event abstraction layer 43
  • 4.1 Semantic Event Processing Architecture 43
  • 4.2 Event Processing Service Prototype 45
  • 4.2.1 Scheduling sensor data requests 45
  • 4.2.2 Registering event patterns 46
  • 4.2.3 Publication of event instances 47
  • 5 real-time flood monitoring in the danube river 51
  • 5.1 Overview 51
  • 5.2 Two Views on the River Floods: a Governmental Body
  • and a Hydroelectric Power Plant 51
  • 5.3 Inferring Events from Flood Monitoring Observations 53
  • 5.3.1 A domain ontology for flood monitoring 54
  • 5.3.2 Application ontologies and event patterns 55
  • 5.4 Conclusion 59
  • 6 evaluation 61
  • 6.1 Testing the Prototype: Evaluation Experiments 61
  • 6.1.1 Analysis of historical data: the Romanian floods
  • in 2006 61
  • 6.1.2 Simulating floods in real-time 65
  • 6.1.3 Conclusion 67
  • 6.2 Comparing my Approach to Existing Solutions 67
  • 7 conclusions and future work 71
  • 7.1 Answer to Research Questions 71
  • 7.2 Contribution 73
  • 7.3 Future Work 74
  • 8 appendix 77
  • 8.1 EPS Software Engineering 77
  • 8.1.1 EPS class diagram 77
  • 8.2 SOS data request 78
  • 8.3 Event Patterns 79
  • 8.3.1 Event patterns for Romanian Waters 79
  • 8.3.2 Event patterns for Hidroelectrica Romania 82
  • 8.3.3 Event patterns for real-time experiment 86
  • 8.4 Properties of an Event Abstraction Instance 87
  • 8.5 SPARQL Queries 88
  • 8.6 Real-time Data Simulation 92
  • bibliography 93.

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