Skip to main content

Data Warehouse Design Methods Review: Trends, Challenges and Future Directions for the Healthcare Domain

  • Conference paper
  • First Online:
New Trends in Databases and Information Systems (ADBIS 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 539))

Included in the following conference series:

  • East European Conference on Advances in Databases and Information Systems

Abstract

In secondary data use context, traditional data warehouse design methods don’t address many of today’s challenges; particularly in the healthcare domain were semantics plays an essential role to achieve an effective and implementable heterogeneous data integration while satisfying core requirements. Forty papers were selected based on seven core requirements: data integrity, sound temporal schema design, query expressiveness, heterogeneous data integration, knowledge/source evolution integration, traceability and guided automation. Proposed methods were compared based on twenty-two comparison criteria. Analysis of the results shows important trends and challenges, among them (1) a growing number of methods unify knowledge with source structure to obtain a well-defined data warehouse schema built on semantic integration; (2) none of the published methods cover all the core requirements as a whole and (3) their potential in real world is not demonstrated yet.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Abelló, A., Martín, C.: A bitemporal storage structure for a corporate data warehouse. In: Proceedings of the 5th International Conference on Enterprise Information Systems, pp. 177–183 (2003)

    Google Scholar 

  2. Adlassnig, K.-P., Combi, C., Das, A.K., Keravnou, E.T., Pozzi, G.: Temporal representation and reasoning in medicine: Research directions and challenges. Artif. Intell. Med. 38(2), 101–113 (2006)

    Article  Google Scholar 

  3. Bakhtouchi, A., Bellatreche, L., Jean, S., Yamine, A.-A.: MIRSOFT: mediator for integrating and reconciling sources using ontological functional dependencies. Int. J. Web Grid Serv. 8(1), 72–110 (2012)

    Article  Google Scholar 

  4. Branson, A., Hauer, T., McClatchey, R., Rogulin, D., Shamdasani, J.: A data model for integrating heterogeneous medical data in the Health-e-Child project. Stud. Health Technol. Inform. 138, 13–23 (2008)

    Google Scholar 

  5. Burney, A., Mahmood, N., Ahsan, K.: TempR-PDM: a conceptual temporal relational model for managing patient data. In: Proceedings of the 9th International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, pp. 237–243. World Scientific and Engineering Academy and Society (WSEAS), Stevens Point (2010)

    Google Scholar 

  6. Chute, C.G., Beck, S.A., Fisk, T.B., Mohr, D.N.: The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data. J. Am. Med. Inform. Assoc. JAMIA. 17(2), 131–135 (2010)

    Article  Google Scholar 

  7. Codd, E.F.: The Relational Model for Database Management: Version 2. Addison-Wesley Longman Publishing Co., Inc., Boston (1990)

    MATH  Google Scholar 

  8. Combi, C., Pozzi, G.: HMAP A Temporal Data Model Managing Intervals with Different Granularities and Indeterminacy from Natural Language Sentences. VLDB J. 9(4), 294–311 (2001)

    MATH  Google Scholar 

  9. Cravero, A., Sepúlveda, S.: Multidimensional design paradigms for data warehouses: a systematic mapping study. J. Softw. Eng. Appl. 2014(7), 53–61 (2013)

    Google Scholar 

  10. Cravero Leal, A., Mazón, J.N., Trujillo, J.: A business-oriented approach to data warehouse development. Ing. E Investig. 33(1), 59–65 (2013)

    Google Scholar 

  11. Date, C.J., Darwen, H., Lorentzos, N.A.: Time and relational theory: temporal databases in the relational model and SQL. Morgan Kaufmann, Waltham (2014)

    Google Scholar 

  12. Elamin, E., Feki, J.: Toward an ontology based approach fro data warehousing. Presented at the The International Arab Conference on Information Technology (ACIT2014) , University of Nizwa, Oman (2014)

    Google Scholar 

  13. Giorgini, P., Rizzi, S., Garzetti, M.: GRAnD: A goal-oriented approach to requirement analysis in data warehouses. Decis. Support Syst., 4–21 (2008)

    Google Scholar 

  14. Gosain, A., Singh, J.: Conceptual multidimensional modeling for data warehouses: a survey. In: Satapathy, S.C., Biswal, B.N., Udgata, S.K., Mandal, J.K. (eds.) Proc. of the 3rd Int. Conf. on Front. of Intell. Comput. (FICTA) 2014- Vol. 1. AISC, vol. 327, pp. 305–316. Springer, Heidelberg (2015)

    Google Scholar 

  15. Hachaichi, Y., Feki, J.: An Automatic Method for the Design of Multidimensional Schemas From Object Oriented Databases. Int. J. Inf. Technol. Decis. Mak. 12(6), 1223–1259 (2013)

    Article  Google Scholar 

  16. Hu, H., Correll, M., Kvecher, L., Osmond, M., Clark, J., Bekhash, A., Schwab, G., Gao, D., Gao, J., Kubatin, V., Shriver, C.D., Hooke, J.A., Maxwell, L.G., Kovatich, A.J., Sheldon, J.G., Liebman, M.N., Mural, R.J.: DW4TR: A Data Warehouse for Translational Research. J. Biomed. Inform. 44(6), 1004–1019 (2011)

    Article  Google Scholar 

  17. Husemann, B., Lechtenbörger, J., Vossen, G.: Conceptual data warehouse design. In: Proceedings of the International Workshop on Design and Management of Data Warehouses, DMDW 2000, pp. 3–9 (2000)

    Google Scholar 

  18. Jensen, M.R., Holmgren, T., Pedersen, T.B.: Discovering multidimensional structure in relational data. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2004. LNCS, vol. 3181, pp. 138–148. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  19. Jindal, R., Taneja, S., et al.: Comparative study of data warehouse design approaches: a survey. Int. J. Database Manag. Syst. 4(1), 33–45 (2012)

    Article  Google Scholar 

  20. Jovanovic, P., Romero, O., Simitsis, A., Abelló, A., Candón, H., Nadal, S.: Quarry: digging up the gems of your data treasury. In: Alonso, G., Geerts, F., Popa, L., Barceló, P., Teubner, J., Ugarte, M., Bussche, J.V. den, and Paredaens, J. (eds.) Proceedings of the 18th International Conference on Extending Database Technology, EDBT 2015, Brussels, Belgium, March 23–27, 2015, pp. 549–552. OpenProceedings.org (2015)

    Google Scholar 

  21. Jovanovic, P., Romero, O., Simitsis, A., Abelló, A., Mayorova, D.: A requirement-driven approach to the design and evolution of data warehouses. Inf. Syst. 44, 94–119 (2014)

    Article  Google Scholar 

  22. Kerkri, E.M., Quantin, C., Allaert, F.A., Cottin, Y., Charve, P., Jouanot, F., Yétongnon, K.: An Approach for Integrating Heterogeneous Information Sources in a Medical Data Warehouse. J. Med. Syst. 25(3), 167–176 (2001)

    Article  Google Scholar 

  23. Khnaisser, C., Lavoie, L., Diab, H., Éthier, J.-F.: Data Warehouse Design Methods Review for the Healthcare Domain. http://info.usherbrooke.ca/llavoie/projets/epiiramide

  24. Khouri, S., Bellatreche, L., Jean, S., Ait-Ameur, Y.: Requirements driven data warehouse design: we can go further. In: Margaria, T., Steffen, B. (eds.) ISoLA 2014, Part II. LNCS, vol. 8803, pp. 588–603. Springer, Heidelberg (2014)

    Google Scholar 

  25. Khouri, S., Boukhari, I., Bellatreche, L., Sardet, E., Jean, S., Baron, M.: Ontology-based structured web data warehouses for sustainable interoperability: requirement modeling, design methodology and tool. Comput. Ind. 63(8), 799–812 (2012)

    Article  Google Scholar 

  26. Krneta, D., Jovanovic, V., Marjanovic, Z.: A direct approach to physical Data Vault design. Comput. Sci. Inf. Syst. 11(2), 569–599 (2014)

    Article  Google Scholar 

  27. Lin, S.-H., Lee, Y.-C.G., Hsu, C.-Y.: Data warehouse approach to build a decision-support platform for orthopedics based on clinical and academic requirements. In: Ślęzak, D., Arslan, T., Fang, W.-C., Song, X., Kim, T.-h. (eds.) BSBT 2009. CCIS, vol. 57, pp. 89–96. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  28. Lowe, H.J., Ferris, T.A., Hernandez, P.M., Weber, S.C.: STRIDE – an integrated standards-based translational research informatics platform. In: AMIA. Annu. Symp. Proc. 2009, pp. 391–395 (2009)

    Google Scholar 

  29. Lujan-Mora, S., Trujillo, J.: Applying the UML and the Unified Process to the design of Data Warehouses. J. Comput. Inf. Syst. 47(5), 30–58 (2006)

    Google Scholar 

  30. Malinowski, E., Zimányi, E.: A conceptual solution for representing time in data warehouse dimensions. In: Proceedings of the 3rd Asia-Pacific Conference on Conceptual Modelling. vol. 53, pp. 45–54. Australian Computer Society, Inc., Darlinghurst (2006)

    Google Scholar 

  31. Maté, A., Trujillo, J.: Tracing conceptual models’ evolution in data warehouses by using the model driven architecture. Comput. Stand. Interfaces 36(5), 831–843 (2014)

    Article  Google Scholar 

  32. Mate, S., Köpcke, F., Toddenroth, D., Martin, M., Prokosch, H.-U., Bürkle, T., Ganslandt, T.: Ontology-Based Data Integration between Clinical and Research Systems. PLoS ONE 10, 1 (2015)

    Article  Google Scholar 

  33. Mazón, J.-N., Trujillo, J., Lechtenbörger, J.: Reconciling requirement-driven data warehouses with data sources via multidimensional normal forms. Data Knowl. Eng. 63(3), 725–751 (2007)

    Article  Google Scholar 

  34. Moreira, J., Cordeiro, K., Campos, M.L., Borges, M.: OntoWarehousing – multidimensional design supported by a foundational ontology: a temporal perspective. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 35–44. Springer, Heidelberg (2014)

    Google Scholar 

  35. De Mul, M., Alons, P., van der Velde, P., Konings, I., Bakker, J., Hazelzet, J.: Development of a clinical data warehouse from an intensive care clinical information system. Comput. Methods Programs Biomed. 105(1), 22–30 (2012)

    Article  Google Scholar 

  36. Murphy, S.N., Weber, G., Mendis, M., Gainer, V., Chueh, H.C., Churchill, S., Kohane, I.: Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J. Am. Med. Inform. Assoc. 17(2), 124–130 (2010)

    Article  Google Scholar 

  37. Nazri, M.N.M., Noah, S.A., Hamid, Z.: Using lexical ontology for semi-automatic logical data warehouse design. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds.) RSKT 2010. LNCS, vol. 6401, pp. 257–264. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  38. Nebot, V., Berlanga, R.: Building data warehouses with semantic web data. Decis. Support Syst. 52(4), 853–868 (2012)

    Article  Google Scholar 

  39. Neil, C.G., De Vincenzi, M.E., Pons, C.F.: Design method for a Historical Data Warehouse, explicit valid time in multidimensional models. Ingeniare Rev. Chil. Ing. 22(2), 218–232 (2014)

    Article  Google Scholar 

  40. Pardillo, J., Mazón, J.-N.: Using ontologies for the design of data warehouses. Int. J. Database Manag. Syst. 3, 2 (2011)

    Google Scholar 

  41. Phipps, C., Davis, K.C.: Automating Data Warehouse Conceptual Schema Design and Evaluation. Design and Management of Data Warehouses, pp. 23–32. Citeseer (2002)

    Google Scholar 

  42. Post, A.R., Kurc, T., Cholleti, S., Gao, J., Lin, X., Bornstein, W., Cantrell, D., Levine, D., Hohmann, S., Saltz, J.H.: The Analytic Information Warehouse (AIW): A platform for analytics using electronic health record data. J. Biomed. Inform. 46(3), 410–424 (2013)

    Article  Google Scholar 

  43. Prat, N., Akoka, J., Comyn-Wattiau, I.: A UML-based data warehouse design method. Decis. Support Syst. 42(3), 1449–1473 (2006)

    Article  Google Scholar 

  44. Rizzi, S., Abello, A., Lechtenborger, J., Trujillo, J.: Research in data warehouse modeling and design: dead or alive?. In: 9th ACM International Workshop on Data Warehousing and OLAP – DOLAP 2006, held in Conjunction with the ACM 15th Conference on Information and Knowledge Management, CIKM 2006, November 10, 2006–November 10, 2006, pp. 3–10. Association for Computing Machinery, New York (2006)

    Google Scholar 

  45. Romero, O., Abelló, A.: A framework for multidimensional design of data warehouses from ontologies. Data Knowl. Eng. 69(11), 1138–1157 (2010)

    Article  Google Scholar 

  46. Romero, O., Abelló, A.: A Survey of Multidimensional Modeling Methodologies. Int. J. Data Warehous. Min. IJDWM. 5(2), 1–23 (2009)

    Article  Google Scholar 

  47. Romero, O., Simitsis, A., Abelló, A.: GEM: requirement-driven generation of ETL and multidimensional conceptual designs. In: Cuzzocrea, A., Dayal, U. (eds.) Data Warehousing and Knowledge Discovery, pp. 80–95. Springer, Berlin Heidelberg (2011)

    Chapter  Google Scholar 

  48. Rönnbäck, L., Regardt, O., Bergholtz, M., Johannesson, P., Wohed, P.: Anchor modeling — Agile information modeling in evolving data environments. Data Knowl. Eng. 69(12), 1229–1253 (2010)

    Article  Google Scholar 

  49. Rubin, D.L., Desser, T.S.: A Data Warehouse for Integrating Radiologic and Pathologic Data. J. Am. Coll. Radiol. 5(3), 210–217 (2008)

    Article  Google Scholar 

  50. Sabaini, A., Zimányi, E., Combi, C.: An OLAP-based approach to modeling and querying granular temporal trends. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 69–77. Springer, Heidelberg (2014)

    Google Scholar 

  51. Sahama, T.R., Croll, P.R.: A data warehouse architecture for clinical data warehousing. In: Proceedings of the 5th Australasian Symposium on ACSW Frontiers, pp. 227–232. Australian Computer Society, Inc., Darlinghurst (2007)

    Google Scholar 

  52. Shortliffe, E.H., Cimino, J.C. (eds.): Biomedical informatics: computer applications in health care and biomedicine. Springer, London (2014)

    Google Scholar 

  53. Sitompul, O.S., Noah, S.A.: A Transformation-oriented Methodology to Knowledge-based Conceptual Data Warehouse Design. J. Comput. Sci. 2(5), 460–465 (2006)

    Article  Google Scholar 

  54. Snodgrass, R.T.: Developing time-oriented database applications in SQL. Morgan Kaufmann Publishers, San Francisco (2000)

    Google Scholar 

  55. Song, I.Y., Khare, R., Dai, B.: SAMSTAR: a semi-automated lexical method for generating star schemas from an entity-relationship diagram. In: Proceedings of the ACM Tenth International Workshop on Data Warehousing and OLAP, pp. 9–16. ACM (2007)

    Google Scholar 

  56. Tebourski, W., Karâa, W.B.A., Ghezala, H.B.: Semi-automatic Data Warehouse Design methodologies: a survey. Int. J. Comput. Sci. Issues IJCSI. 10(5), 48 (2013)

    Google Scholar 

  57. Thenmozhi, M., Vivekanandan, K.: A Tool for Data Warehouse Multidimensional Schema Design using Ontology. Int. J. Comput. Sci. Issues IJCSI. 10(2), 161–168 (2013)

    Google Scholar 

  58. Di Tria, F., Lefons, E., Tangorra, F.: Hybrid methodology for data warehouse conceptual design by UML schemas. Inf. Softw. Technol. 54(4), 360–379 (2012)

    Article  Google Scholar 

  59. Wisniewski, M.F., Kieszkowski, P., Zagorski, B.M., Trick, W.E., Sommers, M., Weinstein, R.A.: Development of a Clinical Data Warehouse for Hospital Infection Control. J. Am. Med. Inform. Assoc. JAMIA. 10(5), 454–462 (2003)

    Article  Google Scholar 

  60. Zekri, M., Marsit, I., Adellatif, A.: A new data warehouse approach using graph. In: 2011 IEEE 8th International Conference on e-Business Engineering (ICEBE), pp. 65–70. IEEE Computer Society (2011)

    Google Scholar 

  61. Zepeda, L., Ceceña, E., Quintero, R., Zatarain, R., Vega, L., Mora, Z., Clemente, G.G.: A MDA tool for data warehouse. In: 2010 International Conference on Computational Science and Its Applications (ICCSA), pp. 261–265 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christina Khnaisser .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Khnaisser, C., Lavoie, L., Diab, H., Ethier, JF. (2015). Data Warehouse Design Methods Review: Trends, Challenges and Future Directions for the Healthcare Domain. In: Morzy, T., Valduriez, P., Bellatreche, L. (eds) New Trends in Databases and Information Systems. ADBIS 2015. Communications in Computer and Information Science, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-319-23201-0_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23201-0_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23200-3

  • Online ISBN: 978-3-319-23201-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics