Abstract
Smart home, smart grids, smart museum, smart cities, etc. are making the vision for living in smart environments come true. These smart environments are built based upon the Internet of Things paradigm where many devices and applications are involved. In these environments, data are collected from various sources in diverse formats. The data are then processed by different intelligent systems with the purpose of providing efficient system planning, power delivery, and customer operations. Even though there are known technologies for most of these smart environments, putting them together to make intelligent and context-aware systems is not an easy task. The reason is that there are semantic inconsistencies between applications and systems. These inconsistencies can be solved by using metadata. This chapter presents management of big data metadata in smart grids. Three important issues in managing and solutions to overcome them are discussed. As a part of future grids, some concrete examples from the offshore wind energy are used to demonstrate the solutions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ackoff, R.L.: From data to wisdom. J. Appl. Syst. Anal. 16, 3–9 (2010)
Antoniou, G., Harmelen, F.v.: Web ontology language: OWL. Handbook on Ontologies. International Handbooks on Information Systems, pp. 91–110. Springer, Berlin Heidelberg (2009)
Baclawski, K., Kokar, M., Waldinger, R., Kogut, P.: Consistency checking of semantic web ontologies. The Semantic Web ISWC 2002, pp. 454–459 (2002)
Balsters, H.: Modelling database views with derived classes in the UML/OCL-framework. In: UML 2003-The Unified Modeling Language. Modeling Languages and Applications, pp. 295–309. Springer (2003)
Barnaghi, P., Wang, W., Henson, C., Taylor, K.: Semantics for the internet of things: early progress and back to the future. Int. J. Semant. Web Inf. Syst. (IJSWIS) 8(1), 1–21 (2012)
Baumgartner, N., Gottesheim, W., Mitsch, S., Retschitzegger, W., Schwinger, W.: Improving situation awareness in traffic management. In: Proceedings of the International Conference on Very Large Data Bases (2010)
Bechhofer, S., Van Harmelen, F., Hendler, J., Horrocks, I., McGuinness, D., Patel-Schneider, P., Stein, L., et al.: OWL web ontology language reference. W3C Recommendation 10, 10 (2004)
Berners-Lee, T., Fischetti, M.: Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor. HarperInformation, 256 p. (2000)
Bredillet, P., Lambert, E., Schultz, E.: CIM, 61850, COSEM standards used in a model driven integration approach to build the smart grid service oriented architecture. In: First IEEE International Conference on Smart Grid Communications (SmartGridComm), 2010, pp. 467–471 (2010)
Bröring, A., Maué, P., Janowicz, K., Nüst, D., Malewski, C.: Semantically-enabled sensor plug & play for the sensor web. Sensors 11(8), 7568–7605 (2011)
Camacho, E.F., Samad, T., Garcia-Sanz, M., Hiskens, I.: Control for renewable energy and smart grids. The Impact of Control Technology, Control Systems Society, pp. 69–88 (2011)
Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., Zhou, X.: Big data challenge: a data management perspective. Front. Comput. Sci. 7(2), 157–164 (2013)
Compton, M., Barnaghi, P., Bermudez, L., GarcÃa-Castro, R., Corcho, O., Cox, S., Graybeal, J., Hauswirth, M., Henson, C., Herzog, A., et al.: The SSN ontology of the W3C semantic sensor network incubator group. Web Semant.: Sci. Serv. Agents World Wide Web 17, 25–32 (2012)
Datastax Corporation: Big Data: Beyond the Hype. White paper (2013)
ETP: Smart Grids—Strategic Deployment Document for Europe’s Electricity Networks of the Future (2010)
Geisler, S., Weber, S., Quix, C.: Onotology-based data quality framewrok for data stream applications. In: 16th International Conference on Information Quality, Nov 2011, Adelaide, AUS (2011)
Ghazel, M., Toguyéni, A., Bigand, M.: An UML approach for the metamodelling of automated production systems for monitoring purpose. Comput. Ind. 55(3), 283–299 (2004)
Gruber, T.R., et al.: Toward principles for the design of ontologies used for knowledge sharing. Int. J. Hum. Comput. Stud. 43(5), 907–928 (1995)
Hachem, N.I., Qiu, K., Serrao, N., Gennert, M.A.: GaeaPN: A Petri Net model for the management of data and metadata derivations in scientific experiments. Worcester Polytechnic Institute, Computer Science Department, Technical Report WPI-CS-TR-94 1 (1994)
Horrocks, I., Patel-Schneider, P., Van Harmelen, F.: From SHIQ and RDF to OWL: the making of a web ontology language. Web semant.: Sci. Serv. Agents World Wide Web 1(1), 7–26 (2003)
Huang, K.T., Lee, Y.W., Wang, R.Y.: Quality Information and Knowledge. Prentice Hall PTR (1998)
Iannone, L., Rector, A.L.: Calculations in OWL. In: OWLED (2008)
IEC: IEC 61400 Wind Turbines—part 25: Communications for Monitoring and Control of Wind Power Plants (2006)
IEEE: Guide for Smart Grid Interoperability of Energy Technology and Information Technology Operation with the Electric Power System (EPS), End-Use Applications, and Loads. IEEE Std 2030-2011 pp. 1–126 (2011)
Informatica Corporation: Metadata Management for Holistic Data Governance. White paper (2013)
ISO: ISO 5725–2: 1994: Accuracy (Trueness and Precision) of Measurement Methods and Results—Part 2: Methods for the Determination of Repeatability and Reproductibility. International Organization for Standardization (1994)
Kollia, I., Glimm, B., Horrocks, I.: SPARQL query answering over OWL ontologies. In: The Semantic Web: Research and Applications, pp. 382–396. Springer (2011)
Le-Phuoc, D., Nguyen-Mau, H.Q., Parreira, J.X., Hauswirth, M.: A middleware framework for scalable management of linked streams. Web Semant.: Sci. Serv. Agents World Wide Web 16, 42–51 (2012)
Lee, Y.W., Strong, D.M., Kahn, B.K., Wang, R.Y.: AIMQ: a methodology for information quality assessment. Inf. Manage. 40(2), 133–146 (2002)
Lenzerini, M., Milano, D., Poggi, A.: Ontology representation and reasoning. Universit di Roma La Sapienza, Roma, Italy, Technical report NoE InterOp (IST-508011) (2004)
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data: the next frontier for innovation, competition, and productivity. Technical report, McKinsey Global Institute (2011)
Margaritopoulos, T., Margaritopoulos, M., Mavridis, I., Manitsaris, A.: A conceptual framework for metadata quality assessment. Universitätsverlag Göttingen 104 (2008)
Muljadi, E., Pierce, K., Migliore, P.: Control strategy for variable-speed, stall-regulated wind turbines. In: Proceedings of the 1998 American Control Conference, vol. 3, pp. 1710–1714. IEEE (1998)
Muyeen, S., Tamura, J., Murata, T.: Wind turbine modeling. Stability Augmentation of a Grid-Connected Wind Farm, pp. 23–65 (2009)
Neuhaus, H., Compton, M.: The semantic sensor network ontology. AGILE workshop on challenges in geospatial data harmonisation, Hannover, Germany, pp. 1–33 (2009)
Nguyen, T.H., Prinz, A., Friiso, T., Nossum, R.: Smart grid for offshore wind farms: towards an information model based on the iec 61400-25 standard. In: IEEE PES Innovative Smart Grid Technologies (ISGT), 2012, pp. 1–6 (2012). doi:10.1109/ISGT.2012.6175686
Nguyen, T.H., Prinz, A., Friisø, T., Nossum, R., Tyapin, I.: A framework for data integration of offshore wind farms. Renew. Energy 60, 150–161 (2013)
NIST: NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 2.0. NIST Special Publication 1108R2 edn. (2012)
O’Connor, M., Das, A.: SQWRL: a query language for OWL. In: Proceedings of 6th OWL: Experiences and Directions, Workshop (OWLED2009) (2009)
Park, J.R.: Metadata quality in digital repositories: a survey of the current state of the art. Cataloging Classif. Q. 47(3–4), 213–228 (2009)
Patel-Schneider, P.F., et al., Hayes, P., Horrocks, I., et al.: OWL web ontology language semantics and abstract syntax. W3C Recommendation 10 (2004)
Ragheb, M., Ragheb, A.M.: Wind turbines theory-the betz equation and optimal rotor tip speed ratio. In: Carriveau, R. (ed.) Fundamental and Advanced Topics in Wind Power, pp. 19–37 (2011)
Ramirez, R.G., Kulkarni, U.R., Moser, K.A.: Derived data for decision support systems. Decis. Support Syst. 17(2), 119–140 (1996)
Rasta, K., Nguyen, T.H., Prinz, A.: A framework for data quality handling in enterprise service bus. In: Third International Conference on Innovative Computing Technology (INTECH), 2013, pp. 491–497 (2013)
Rossouw, L., Re, G.: Big data-big opportunities. RISK 16(2) (2012)
Sheth, A., Anantharam, P., Henson, C.: Physical-cyber-social computing: an early 21st century approach. IEEE Intell Syst 28(1), 78–82 (2013). doi:10.1109/MIS.2013.20
Singh, A.: Standards for smart grid. Int. J. Eng. Res. Appl. (IJERA) (2012)
Sirin, E., Parsia, B.: SPARQL-DL: SPARQL query for OWL-DL. In: OWLED, vol. 258 (2007)
Sirin, E., Parsia, B., Grau, B., Kalyanpur, A., Katz, Y.: Pellet: a practical OWL-DL reasoner. Web Semant.: Sci. Serv. Agents World Wide Web 5(2), 51–53 (2007)
Snyder, B., Kaiser, M.J.: Ecological and economic cost-benefit analysis of offshore wind energy. Renew. Energy 34(6), 1567–1578 (2009)
Solntseff, N., Yezerski, A.: A survey of extensible programming languages. Ann. Rev. Autom. Prog. 7, 267–307 (1974)
Strong, D.M., Lee, Y.W., Wang, R.Y.: Data duality in context. Commun. ACM 40(5), 103–110 (1997)
Studer, R., Benjamins, V., Fensel, D.: Knowledge engineering: principles and methods. Data Knowl. Eng. 25(1–2), 161–197 (1998)
Tambouris, E., Manouselis, N., Costopoulou, C.: Metadata for digital collections of e-government resources. Electron. Libr. 25(2), 176–192 (2007)
Tannenbaum, A.: Metadata Solutions: Using Metamodels, Repositories, XML, and Enterprise Portals to Generate Information on Demand. Addison-Wesley Longman Publishing Co., Inc., Boston (2001)
Vermesan, O., Friess, P., Guillemin, P., Gusmeroli, S., Sundmaeker, H., Bassi, A., Jubert, I.S., Mazura, M., Harrison, M., Eisenhauer, M., et al.: Internet of things strategic research roadmap. In: Vermesan, O., Friess, P., Guillemin, P., Gusmeroli, S., Sundmaeker, H., Bassi, A., et al. (eds.) Internet of Things: Global Technological and Societal Trends, pp. 9–52 (2011)
Wagner, A., Speiser, S., Harth, A.: Semantic web technologies for a smart energy grid: Requirements and challenges. In: ISWC Posters and Demos (2010)
Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. J. Manag. Inf. Syst. 5–33 (1996)
Warmer, J., Kleppe, A.: The object constraint language: getting your models ready for MDA. Addison-Wesley Longman Publishing Co., Inc., Boston (2003)
Xu, H.: Critical success factors for accounting information systems data quality. Ph.D. thesis, University of Southern Queensland (2009)
Zikopoulos, P.C., Eaton, C., DeRoos, D., Deutsch, T., Lapis, G.: Understanding Big Data. The McGraw-Hill Companies (2012)
Zubcoff, J., Pardillo, J., Trujillo, J.: A UML profile for the conceptual modelling of data-mining with time-series in data warehouses. Inf. Softw. Technol. 51(6), 977–992 (2009)
Acknowledgments
This work has been (partially) funded by the Norwegian Centre for Offshore Wind Energy (NORCOWE) under grant 193821/S60 from the Research Council of Norway (RCN). NORCOWE is a consortium with partners from industry and science, hosted by Christian Michelsen Research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Nguyen, T.H., Nunavath, V., Prinz, A. (2014). Big Data Metadata Management in Smart Grids. In: Bessis, N., Dobre, C. (eds) Big Data and Internet of Things: A Roadmap for Smart Environments. Studies in Computational Intelligence, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-319-05029-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-05029-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05028-7
Online ISBN: 978-3-319-05029-4
eBook Packages: EngineeringEngineering (R0)