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MSc thesis in Geomatics Exploring the use of the semantic web for discovering, retrieving and processing data from Sensor Observation Services Ivo de Liefde June 2016 EXPLORINGTHEUSEOFTHESEMANTICWEBFOR DISCOVERING,RETRIEVINGANDPROCESSINGDATAFROM SENSOROBSERVATIONSERVICES A thesis submitted to the Delft University of Technology in partial fulfillment of the requirements for the degree of Master of Science in Geomatics by Ivo de Liefde June 2016 Ivo de Liefde: Exploring the use of the semantic web for discovering, retrieving and processing data from Sensor Observation Services (2016) cb This work is licensed under a Creative Commons Attribution 4.0 Inter- national License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The work in this thesis was made in the: Department of GIS Technology OTB Research Institute for the Built Environment Faculty of Architecture & the Built Environment Delft University of Technology Supervisors: Drs. M.E. de Vries Dr.ir. B.M. Meijers Co-reader: Dr. Dipl.-Ing. S. Zlatanova ABSTRACTDevelopments such as smart cities, the Internet of Things (IoT) and the Infras- tructure for Spatial Information in Europe (INSPIRE) are causing a growing amount of observation data to be produced. The Open Geospatial Con- sortium (OGC) has developed Sensor Web Enablement (SWE) standards for modelling and publishing this data online. However, their use is currently limited to geo information specialists, who have knowledge about which data services are available and how to access them. With the use of the semantic web, online processes can automatically find and understand ob- servation metadata. This opens up the SWE services to a large user audience. Therefore, this thesis has designed a conceptual system architecture that uses the semantic web to improve sensor data discovery as well as the inte- gration and aggregation of sensor data from multiple sources. A conceptual system architecture is presented containing two web pro- cesses. The first process automatically creates an online semantic knowl- edge base of sensor metadata, by harvesting Sensor Observation Services. Metadata based on the Observations and Measurements (O&M) data model are retrieved, which includes the location of deployed sensors, what they observe, how they observe it and at which SWE service their data can be requested. The second process automatically translates logical queries of users into observation data requests. It also performs further processing be- fore returning the observation data to the user. Both of these processes have been tested in a proof of concept implementation. A Web Processing Service (WPS) has been created as a proof of concept, making the two processes available online. The proof of concept is able to harvest sensor metadata, convert it to linked data, and publish it on the semantic web with links to and from other metadata. It results in a se- mantic knowledge base of sensor metadata, which improves the discovery in a machine understandable way. It allows multiple Sensor Observation Services to be used in combination with each other, due to the harmonisa- tion involved in creating linked data. The WPS is therefore able to retrieve and process observation data from multiple Sensor Observation Services, using queries such as: What are the average particulate matter levels per month in neighbourhoods of Delft over the last five years? In conclusion, the presented conceptual system architecture contains two processes which improve the discovery, retrieval and aggregation of sensor data from multiple Sensor Observation Services using the semantic web. However, creating a completely automated process for harvesting metadata, which works with every Sensor Observation Service is not yet feasible, as non-meaningful identifiers are allowed to be used according to the SWE stan- dards. Linked data ontologies should also be extended to include all avail- able metadata. Currently there are no ontologies able to define the concept of a geo web service. Another conclusion is that the SPARQL endpoints used in this thesis cannot cope with complex vector geometries. This is due to their inability to handle verbose queries. Nevertheless, the proof of concept shows that the semantic web can enable current SWE services to be used in new ways, making observation data available to a larger user audience. i ACKNOWLEDGEMENTSThis thesis is the result of several months of hard work, and the support of many people. I would first of all like to thank my supervisors Marian de Vries en Martijn Meijers. You have guided me throughout the whole process and regularly took the time to help me reflect on my thesis. Thank you for all of your help and inspiration. Special thanks goes out to Michel Grothe from Geonovum, for introduc- ing me to the SOS data sources and the ways in which they can be accessed. I would also like to thank my coreader Sisi Zlatanova and the delegate of the board of examiners Verena Balz. Your critical feedback has been very important in the process of writing the thesis. I would like to thank the thesis coordinator Hugo Ledoux, for shedding light on all the different aspects of the graduation manual. Another special thanks goes out to Florian Fichtner for his valuable peer review. I would also like to express my gratitude to all the Geomatics teachers and staff members, of whom I have learned a lot during my time in Delft. You have provided me with all the necessary knowledge and skills to make it this far. Finally, I would like to thank all of my friends and family who have supported me over the course of the last couple of months. iii CONTENTS1 1 1.1 Background . 1 1introduction.2 Motivation . 2 1.3 Problem statement . 3 1.4 Test case . 5 1.5 Research questions . 6 1.6 Contributions . 6 1.7 Thesis outline . 7 2 9 2.1 Sensor Web Enablement . 9 2related.2 Sensor work data aggregation . 15 2.3 Sensor metadata in catalogue services . 16 2.4 Semantic web . 19 2.5 Sensor data ontologies . 22 2.6 Semantic sensor data middleware . 24 2.7 Concluding remarks . 25 3 27 3.1 Creating linked data . 27 3methods.2 Ontologies . 28 3.3 Adding links to DBPedia . 30 3.4 Spatial queries with SPARQL . 30 3.5 Web Processing Service . 32 4 35 4.1 Creating linked data from sensor metadata . 35 4conceptual.2 Using logical system queries architecture to retrieve sensor data . 43 5 47 5.1 Preparing geographic data . 47 5proof.2 Creating of concept linked data from sensor metadata . 52 5.3 Using logical queries to retrieve sensor data . 58 5.4 Setting up the Web Processing Services . 68 6 71 6.1 Implementation differences of Sensor Observation Services . 71 6results.2 Semantics in Sensor Observation Services . 72 6.3 Missing classes in linked data ontologies . 74 6.4 Spatial queries with SPARQL . 74 6.5 Reusing the OM Observation Schema for output data . 76 6.6 Comparison with a Catalog Service for the Web . 77 6.7 Comparison with semantic sensor middleware . 78 6.8 Concluding remarks . 79 7 81 7.1 Answering the research questions . 81 7conclusions.2 Discussion . 85 v vi 8Contents 87 8.1 Recommendations . 87 8recommendations.2 Future work . .and . future. work. 88 a 91 b mappings 95 b.1 Example Capabilities Document . 95 bweb.2 processingExample Describe service Process response Document documents . 98 b.3 Example Execute Document . 100 LISTOFFIGURESFigure 1.1 Web application created in this thesis, which uses the semantic to find and retrieve sensor data from Sensor Observation Services based on logical input. 4 Figure 1.2 The combined air quality sensor network of the Sen- sor Observation Services of the Belgian interregional environment agency (IRCEL-CELINE) and the Dutch na- tional institute for public health and the environment (RIVM) in Belgium and the South of the Netherlands. 5 Figure 2.1 Basic observation in the Observations and Measure- ments (O&M) data model. Image courtesy of [ISO, 2011, p. 9]......................... 10 Figure 2.2 DescribedObject class in Sensor Modelling Language (SensorML). Image courtesy of [OGC, 2014, p. 39]... 11 Figure 2.3 Definition of a physical process in Sensor Modelling Language (SensorML). Image courtesy of [OGC, 2014, p. 57]............................ 12 Figure 2.4 Data model behind the capabilities document of a SOS. Image courtesy of Broring¨ et al.[ 2012]...... 14 Figure 2.5 Architecture of Catalog Service for the Web. Image courtesy of [OGC, 2007, p. 26].............. 16 Figure 2.6 Overview of the interaction between CSW, SIR, SOR and potential users. Image courtesy of 52 North[ 2016] 17 Figure 2.7 Hierarchy of the semantic web. Image courtesy by Berners-Lee[ 2000]..................... 20 Figure 2.8 Triples of object, predicate and subject define Delft as a municipality with a geometry . 21 Figure 2.9 Structure of URI, URN and URL . 21 Figure 2.10 Triples of Figure 2.8 in the Turtle notation . 22 Figure 2.11 Persistent Uniform Resource Locator (PURL) resolves to the current resource location. Image courtesy of Shafer et al.[ 2016]..................... 22 Figure 2.12 The stimulus–sensor–observation pattern [Compton et al., 2012, p. 28]..................... 23 Figure 3.1 Workflow diagram for publishing linked open data from existing spatial data. Image courtesy of [Missier, 2015, p. 28]......................... 28 Figure 3.2 Basic classes and relations in the PROV-O by [Lebo et al., 2013]......................... 29 Figure 3.3 Artifacts of the Web Processing Service (WPS) service model [OGC, 2015, p. 15]................ 32 Figure 3.4 UML model of WPS capabilities document [OGC, 2015, p. 70]............................ 33 Figure 4.1 General overview of creating linked data of metadata from Sensor Observation Services . 36 Figure 4.2 Workflow diagram of web process for creating linked sensor metadata (adapted from Missier[ 2015]) . 36 Figure 4.3 Sensor metadata retrieved from a Sensor Observation Service (SOS).......................

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