Operational Research

, Volume 10, Issue 3, pp 349–369 | Cite as

Precise design of environmental data warehouses

Original Paper


People use data warehouses to help them make decisions. For example, public policy decision-makers can improve their decisions by using this technology to analyze the environmental effects of human activity. In production systems, data warehouses provide structures for extracting the knowledge required to optimize systems. Designing data warehouses is a complex task; designers need flexible and precise methods to help them create data warehouses and adapt their analysis criteria to developments in the decision-making process. In this paper, we introduce a flexible method based on UML (Unified Modeling Language). We introduce a UML profile for building multi-dimensional models and for choosing different criteria according to analysis requirements. This profile makes it possible to specify integrity constraints in OCL (Object Constraint Language). We apply our method to the construction of an environmental system for analyzing the use of certain agricultural fertilizers. We integrate various data sources into a multi-dimensional model showing several categories of analysis, and the consistency of data can be checked with OCL constraints.


Data warehouse Environmental data Agriculture Decision support system Unified Modeling Language Object Constraint Language 


  1. Berson A, Smith S (1997) Data warehousing, data mining, and OLAP (Data Warehousing/Data Management), Computing Mcgraw-Hill (640 p)Google Scholar
  2. Bimonte S, Tchounikine A, Miquel M (2006) GeoCube, a multidimensional model and navigation operators handling complex measures: application in spatial OLAP. Lect Notes Comput Sci 4243:100–109CrossRefGoogle Scholar
  3. Brodeur J, Bedard Y, Proulx MJ (2000) Modelling geospatial application databases using UML-based repositories aligned with international standards in geomatics. Proceedings of the international ACM symposium on advances in geographic information systems, USA, pp 39–46Google Scholar
  4. Calì A, Lembo D, Lenzerini M, Rosati R (2003) Source integration for data warehousing. Multidimensional databases, pp 361–392Google Scholar
  5. Demuth B (2005) The dresden OCL toolkit and the business rules approach. European business rules conference (EBRC 2005), AmsterdamGoogle Scholar
  6. Demuth B, Hußmann H (1999) Using UML/OCL constraints for relational database design. Lect Notes Comput Sci 1723:598–613CrossRefGoogle Scholar
  7. Demuth B, Hußmann H, Loecher S (2001) OCL as a specification language for business rules in database applications. Lect Notes Comput Sci 2155:104–117CrossRefGoogle Scholar
  8. Demuth B, Loecher S, Zschaler S (2004) Structure of the dresden OCL toolkit. In: 2nd Interna-tional Fujaba days “MDA with UML and rule-based object manipulation”. Darmstadt, Germany, September 15–17Google Scholar
  9. Duboisset M, Pinet F, Kang MA, Schneider M (2005) Precise modelling and verification of topological integrity constraints in spatial databases: from an expressive power study to code generation principles. Lect Notes Comput Sci 3716:465–482CrossRefGoogle Scholar
  10. Egenhofer M, Franzosa R (1991) Point-set topological spatial relations. Int J Geogr Inf Syst 5(2):161–174CrossRefGoogle Scholar
  11. Egenhofer M, Herring J (1992) Categorizing binary topological relationships between regions, lines, and points in geographic databases. Technical report. Department of surveying engineering. University of Maine, Orono, 28 pGoogle Scholar
  12. Friis-Christensen A, Tryfona N, Jensen C (2001) Requirements and research issues in geographic data modelling. In: Proceedings of the international ACM symposium on advances in geographic information systems, USA, pp 2–8Google Scholar
  13. Klasse Objecten (2008) OCL tools web site http://www.klasse.nl/ocl
  14. Lujan-Mora S, Trujillo J, Song IY (2006) A UML profile for multidimensional modelling in data warehouses. Data Knowl Eng 59(3):725–769CrossRefGoogle Scholar
  15. Malinowski E, Zimanyi E (2006) Hierarchies in a multidimensional model: from conceptual modelling to logical representation. Data Knowl Eng 59(2):348–377CrossRefGoogle Scholar
  16. Malinowski E, Zimanyi E (2008) Advanced data warehouse design: From conventional to spatial and temporal applications, Springer (435 p)Google Scholar
  17. Manolopoulos Y, Papadopoulos A, Vassilakopoulos M (2004) Spatial databases: technologies, techniques and trends, IGI Global (340 p)Google Scholar
  18. Mazon JN, Trujillo J (2008) An MDA approach for the development of data warehouses. Deci Support Syst 45:41–55CrossRefGoogle Scholar
  19. McHugh R, Roche S, Bedard Y (2009) Towards a SOLAP-based public participation GIS. J Environ Manage 90(6):2041–2054CrossRefGoogle Scholar
  20. Miralles A, Libourel T (2007) Spatial database modelling with enriched model driven architecture. Encyclopedia of geographical information sciences, Springer (9 p)Google Scholar
  21. Muzy A, Innocenti E, Aïello A, Santucci JF, Santoni PA, Hill D (2005) Modelling and simulation of ecological propagation processes: application to fire spread. Environ Modell Softw 20(7):827–842CrossRefGoogle Scholar
  22. Nilakanta S, Scheibe K, Rai A (2008) Dimensional issues in agricultural data warehouse designs. Comput Electron Agric 60(2):263–278CrossRefGoogle Scholar
  23. OMG (2005) OMG: OCL 2.0 specification (185 p)Google Scholar
  24. Papajorgji P (2007) State of the art in modeling software for agricultural systems. Encyclopedia of optimization, second edition, SpringerGoogle Scholar
  25. Papajorgji P, Pardalos P (2006) Software engineering techniques applied to agricultural systems: an object-oriented and UML approach. Springer (247 p)Google Scholar
  26. Papajorgji P, Shatar P (2004) Using the unified modelling language to develop soil water-balance and irrigation-scheduling models. Environ Modell Softw 19(5):451–459CrossRefGoogle Scholar
  27. Papajorgji P, Beck H, Braga J (2004) An architecture for developing service-oriented and component-based environmental models. Ecol Modell 179(1):61–76CrossRefGoogle Scholar
  28. Pinet F, Kang MA, Vigier F (2005) Spatial constraint modelling with a GIS extension of UML and OCL: application to agricultural information systems. Lect Notes Comput Sci 3511:160–175Google Scholar
  29. Pinet F, Duboisset M, Soulignac V (2007) Using UML and OCL to maintain the consistency of spatial data in environmental information systems. Environ Modell Softw 22(8):1217–1220CrossRefGoogle Scholar
  30. Pinet F, Duboisset M, Demuth B, Schneider M, Soulignac V, Barnabé F (2009) Constraints modelling in agricultural databases. Chapter In: Advances in modeling agricultural systemsGoogle Scholar
  31. Prat N, Akoka J, Comyn-Wattiau I (2006) A UML-based data warehouse design method, Decision support systems 42(3), pp 1449–1473Google Scholar
  32. Rizzi S, Abello A, Lechtenborger J, Trujillo J (2006) Research in data warehouse modelling and design: dead or alive? Proceedings of the 9th ACM international workshop on data warehousing and OLAP, pp 3–10Google Scholar
  33. RN DEAS (2008) Research network “Design of environmental & agricultural systems”, <http://deas.research.free.fr>
  34. Schmid B, Warmer J, Clark T (2002) Object modelling with the OCL: the rationale behind the object constraint language, springer, (281 p)Google Scholar
  35. Schneider M (2008) A general model for the design of data warehouses. Int J Prod Econ 112(1):309–325CrossRefGoogle Scholar
  36. Schulze C, Spilke J, Lehner W (2007) Data modelling for precision dairy farming within the competitive field of operational and analytical tasks. Comput Electron Agric 59(1):39–55CrossRefGoogle Scholar
  37. Soulignac V, Gibold F, Pinet F, Vigier F (2005) Spreading matter management in France within sigemo. In: Proceedings of the 5th European conference for information technologies in agriculture (EFITA 2005), Vila Real, Portugal, July 25–28 (5 p)Google Scholar
  38. Trujillo J, Luján-Mora S (2003) A UML based approach for modelling ETL processes in data warehouses. Lect Notes Comput Sci 2813:307–320CrossRefGoogle Scholar
  39. Trujillo J, Palomar M, Gomez J, Song IY (2001) Designing data warehouses with OO conceptual models. IEEE Comput 34(12):66–75Google Scholar
  40. Tsois A, Karayannidis N, Sellis T (2001) MAC: conceptual data modelling for OLAP. In: Proceedings of the international workshop on design and management of data warehouses, Interlaken, SwitzerlandGoogle Scholar
  41. Zubcoff JJ, Trujillo J (2007) A UML 2.0 profile to design association rule mining models in the multidimensional conceptual modelling of data warehouses. Data Knowl Eng 63(1):44–62CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.CemagrefAubière CedexFrance
  2. 2.Blaise Pascal University, LIMOSClermont FerrandFrance

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