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dc.contributor.author
Georgopoulos, Georgios
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dc.date.accessioned
2016-04-07T11:19:38Z
dc.date.available
2016-04-08T00:00:14Z
dc.date.issued
2016-04-07
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/14463
dc.rights
Default License
dc.subject
Semantic Web
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dc.subject
Linked Data
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dc.title
Extend Smart IHU project to publish energy consumption data as Linked Data
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heal.type
masterThesis
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heal.generalDescription
The current master thesis describes the outcome of a dissertation project that aimed at leveraging the interoperability and the reusability of power consumption and environmental parameters measurement data, by publishing them using the Linked Data format and principles and describing them with the use of well-known and widely used ontologies and vocabularies. Furthermore, the project defined a set of technologies, tools and methodologies that facilitate the export of relational data, the subsequent conversion to Linked Data and the final import to a triple store that publishes them through the use of a specialized end-point. Finally a framework is proposed, that can be used to automate the whole process in order for new data to be automatically published.
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heal.keywordURI.LCSH
Sustainable development--Research
heal.keywordURI.LCSH
Sustainable development--Data processing.
heal.keywordURI.LCSH
Sustainable development--Computer simulation
heal.keywordURI.LCSH
Ecosystem management--Data processing
heal.keywordURI.LCSH
Ecosystem management--Computer simulation
heal.keywordURI.LCSH
Energy saving
heal.keywordURI.LCSH
Renewable energy sources--Economic aspects
heal.keywordURI.LCSH
Energy conservation--Environmental aspects
heal.keywordURI.LCSH
Energy consumption
heal.keywordURI.LCSH
Architecture and energy conservation.
heal.keywordURI.LCSH
Energy consumption--Climatic factors.
heal.keywordURI.LCSH
Renewable energy sources--Environmental aspects
heal.keywordURI.LCSH
Energy conservation
heal.language
en
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heal.access
free
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heal.license
http://creativecommons.org/licenses/by-nc/4.0
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heal.references
[1] Berners - Lee, T. (20 06). Linked Data - Design Issues. Retrieved August 23, 2015 from http://www.w3.org/DesignIssues/LinkedData.html [2] Berners - Lee, T. (2009). Putting Government Data Online . Retriev ed August 20, 2015 from http://www.w3.org/DesignIssues/GovData [3] Heath, T. & Bizer, C., (2011). Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web: Theory and Technology 2011 1:1, 1 - 136 [4] Shadbolt, N., O'Hara, K., Berners - Lee, T., Gibbins, N., Glaser, H., Hall, W. & Schraefel M.C., (2012) Open Government Data and the Linked Data Web: Lessons from data.gov.u k. IEEE Intelligent Systems, Vol 27, Issue 3, Spring Issue [5] Peristeras V., Loutas N., Goudos S., Tarabanis K.: A Conceptual Analysis of Se- mantic Conflicts in Pan - European E - Government Services. In Journal of Information Science, vol. 34 (6), pp. 877 - 891, 2008 [6] D7.1.3 ISA Action 1.1 on Semantic Interoperability (2012) Study on persistent URIs, with identification of best practices and recommendations on the topic for the MSs and the EC . Retrieved August , 3, 2015 , from htt ps://joinup.ec.europa.eu/community/semic/document/10 - rules - persistent - uris [7] L. Sauermann and R. Cyganiak, Cool URIs for the Semantic Web , W3C Interest Group Note, Retrieved August , 28 , 20 15, from www.w3.org/ TRcooluris . [8] Berners - Lee, T. ( 1998 ). Cool URIs don’t change Government Data Online . Re- triev ed August 22, 2015 from http://www.w3.org/Provider/Style/URI . [9] LOD2 Deliverable 8.2.2: Guidelines of Best Practices in Semantic Management of Corporate Information Retriev ed August 15, 2015 from http://svn.aksw.org/lod2/D8.2.2/public.pdf [10] Alani, H.; Dupplaw, D.; Sheridan, J.; O’Hara, K.; Darlingt on, J.; Shadbolt, N.; and Tullo, C. 2007. Unlocking the potential of public sector information with semantic web technology . In ISWC/ASWC, 708 – 721 - 98 - [11] Varitimou A., 2012. Publishing Linked Data from existing g overnmental Datasets Master Thesis, Internatio nal Hellenic University,Thessaloniki, Greece. [12] Li Ding, Vassilios Peristeras, Michael Hausenblas , "Linked Open Government Da- ta," IEEE Intelligent Systems, pp. 11 - 15, May - June, 2012 [13] Deloitte Analytics paper (2013) - Open data Driving growth, ingenu ity and innova- tion Retrieved September , 11 , 2015 , from http://www2.deloitte.com/content/dam/Deloitte/uk/Documents/deloitte - analytics/open - growth.pd f [14] A. Bizer, C. Jentzsch and R. Cyganiak. The Linking Open Data Cloud Diagram Webpage. Retrieved from http://www4.wiwiss.fu - berlin.de/lodcloud/state/ [15] Transforming Spreadsheets to RDF. Described in http://www.topquadrant.com/docs/whitepapers/Transforming_Spreadsheets_into_RDF_with_To pBraid.pdf [Accessed October 2015] [16 ] Generate ADMS asset descriptions from a spreadsheet with Refine RDF . De- scribed in https://joinup.ec.europa.eu/asset/adms/document/gener ate - adms - asset - descriptions - spreadsheet - refine - rdf [Accessed October 2015] [17] Schultz, A., Bizer, C., & Isele, R. The R2R Framework. [18] Bizer, C., & Schultz, A. (2010). The R2R Framework: Publishing and Discovering Mappings on the Web. COLD , 665 . [19] Tomic, S., Fensel A. & Pellegrini T. (2010). SESAME Demonstrator: Ontologies, Services and Policies for Energy Efficiency - Conference Paper [20] Bonino D. , Corno F. , & De Russis L. (2014). PowerOnt: An Ontology - based Ap- proach for Power Consumption Estima tion in Smart Homes – Conference Paper [21] A uer S. , Weidl M. , Lehmann J. , Zaveri A. , & Choi K. I18n of Semantic Web Ap- plications [22] Zelkha E ., Epstein B. ; Birrell S . & Dodsworth C . (1998). From De vices to "Ambi- ent Intelligence" , Digital Living Room Conf erence (June 1998) [23] Hoehndorf (2010) . What is an upper lev el ontology? Ontogenesis
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heal.recordProvider
School of Science and Technology, MSc in Information & Communication Technology Systems
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heal.publicationDate
2016-04-06
heal.bibliographicCitation
Georgios Georgopoulos, Extend Smart IHU project to publish energy consumption data as Linked Data, School of Science and Technology, International Hellenic University, 2015
en
heal.abstract
This dissertation was written as a part of the MSc in ICT Systems at the International Hellenic University. Nowadays the interest on data related to power consumption and environmental pa-rameters measurement is constantly increasing due to the need to analyze them and ex-tract information that could lead in reduced consumption for both financial and ecologi-cal reasons. SMART IHU is a project that has already built a network of sensors that collects energy consumption and environmental data and stores them in a relational da-tabase, which imposes constraints on the availability and the usability of the data. The current dissertation project aims into leveraging the interoperability and the reusa-bility of these data, by publishing them using the Linked Data format and principles and describing them with the use of well-known and widely used ontologies and vocabular-ies. Furthermore, the project defines a set of technologies, tools and methodologies that facilitate the export of relational data, the subsequent conversion to Linked Data and the final import to a triple store that will publish them through the use of a specialized end-point. It also proposes a framework that can be used to automate the whole process in order for new data to be automatically published.
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heal.tableOfContents
- iv - Contents ABSTRACT ................................ ................................ ................................ ................. III CO NTENTS ................................ ................................ ................................ .................. IV LIST OF FIGURES ................................ ................................ ................................ ...... VI LIST OF TABLES ................................ ................................ ................................ ..... VII 1 INTRODUCTION ................................ ................................ ................................ ..... 9 1.1 S MART IHU PROJECT ................................ ................................ ........................ 9 1.2 P ROBLEM AND IMPORTANC E ................................ ................................ ........... 11 1.3 O BJEC TIVES OF THE DISSERT ATION ................................ ................................ 13 1.4 S TRUCTURE ................................ ................................ ................................ ..... 14 2 LITERATURE REVIEW ................................ ................................ ........................ 17 2.1 E XISTING ONTOLOGIES A ND VOCABULARIES RELA TED TO THE E NERGY DOMAIN 17 2.1.1 Process Energy Vocabulary (PEN) ................................ ................ 17 2.1.2 Sem antic Smart Home System for Energy Efficiency (SESAME - S) Ontology ................................ ................................ ................................ .... 19 2.1.3 PowerOnt: An Ontology - based Approach for Power Consumption Estimation in Smart Homes ................................ ................ 20 2.1.4 Dolce + DnS Ultralite Ontology (DUL) ................................ ........... 22 2.1.5 Semantic Sensor Network Ontology (SSN) ................................ .. 24 2.1.6 Smart Energy Aware Systems (SEAS) Ontology ........................ 26 2.1.7 Ontology for Meteorological sensors (AWS) ................................ 28 2.1.8 Urban Energy Ontol ogy ................................ ................................ ... 29 2.1.9 Smart Building Ontology for Ambient Intelligence – BonSAI ..... 32 2.1.10 OWL Time Ontology ................................ ................................ ......... 33 2.1.11 Measurement Unit Ontology (MUO) ................................ .............. 35 2.2 E XISTING ENERGY - RELATED DATASETS PUB LISHED AS L INKED D ATA ........... 36 2.3 L INKED D ATA P RINCIPLES AND BENEFI TS ................................ ....................... 38 2.4 T ECHNOLOGIES AND TOOL S FOR PUBLISHING AND CONSUMING L INKED D ATA 45 - v - 2.5 T OOLS AND METHODOLOGI ES FOR CONVERTING R ELATIONAL D ATA TO L INKED D ATA ................................ ................................ ................................ ......................... 53 2.5.1 R2RML: RDB to RDF mapping language ................................ ..... 53 2.5. 2 D2RQ Platform ................................ ................................ .................. 54 2.5.3 Virtuoso RDF Views ................................ ................................ .......... 56 2.6 C ONCLUSIONS ................................ ................................ ................................ .. 57 3 PROBLEM DEFINITION ................................ ................................ ....................... 61 3.1 C URRENT FORMAT AND SC HEMA OF DATA ................................ ....................... 61 3.1.1 Table ccnodes (Current Cost Clamper Devices) ......................... 61 3.1.2 Table ccdata (Current Cost Clamper Consumption Data) .......... 61 3.1.3 Table pwnodes (sensors over specific appliances) ..................... 62 3.1.4 Table pwdata (Hourly appliance consumption) ............................ 63 3.1.5 Table prisma_nodes (Environmental Sensors) ............................ 63 3.1.6 Table prisma_humidity (Humidity measurements) ...................... 64 3.1.7 Table zwnodes (Z - Wave manufactured environmental nodes) . 64 3.1.8 Table zwco2 (CO2 emission measurements) ............................... 65 3.2 P ROBLEMS IN CONVERTIN G RELATIONAL DATA TO L INKED D ATA ................... 66 3.3 I SSUES IN PUBLISHING L INKED D ATA ................................ ............................... 68 4 CONTRIBUTION ................................ ................................ ................................ .... 71 4.1 T HE ENERGY - RELATED ONTOLOGY USE D ................................ ........................ 71 4.1.1 Ontology and URI design ................................ ................................ . 75 4.2 T OOLS AND METHODOLOGY USED FOR CONVERTING R ELATIONAL D ATA TO L INKED D ATA ................................ ................................ ................................ ............. 79 4.3 L IFECYCLE OF THE L INKED D ATA PUBLICATION PROC ESS .............................. 83 4.4 I NSTRUCTIONS ON CONSU MING THE DATA ................................ ....................... 87 5 CONCLUSIO NS ................................ ................................ ................................ ..... 91 REFERENCES AND BIBLI OGRAPHY ................................ ................................ .... 97 APPENDIX A: D2RQ MAP PING DOCUMENT ................................ ...................... 99 APPENDIX B: SMART IH U LINKED DATA ONTOLO GY CODE .................... 106
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heal.advisorName
Bassiliades, Nikos
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heal.committeeMemberName
Peristeras, Vassileios
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heal.committeeMemberName
Berberidis, Christos
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heal.academicPublisher
IHU
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heal.academicPublisherID
ihu
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heal.numberOfPages
110
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