Systems Dynamics-Based Modeling of Data Warehouse Quality

References

• Jeffrey W. Top 5 data warehouses on the market today; 2015 [accessed 2016 Dec 25]. Monitis. http://www.monitis.com/blog/top-5-data-warehouses-on-the-market-today/
   Google Scholar
• Inmon WH. Building the data warehouse. 2nd ed. New York, NY: Wiley Computer Pub; 1996.
   Google Scholar
• Wang RY, Strong DM. Beyond accuracy: what data quality means to data consumers. J Manage Inform Sys. 1996;12(4):5–33. doi:10.1080/07421222.1996.11518099.
   Google Scholar
• Greenberg I. Data warehouse initiative helps sears weather competition. InfoWorld. 1996;18(31):66.
   Google Scholar
• Hare B. Celebration of fools: An inside look at the rise and fall of JCPenney. New York, NY: AMACOM, American Management Association; 2004.
   Google Scholar
• Colla E, Dupuis M. Research and managerial issues on global retail competition: carrefour/wal-mart. Int J Retail Distribut Manag. 2002;30(2):103–11. http://ezaccess.libraries.psu.edu/login?url=http://search.proquest.com.ezaccess.libraries.psu.edu/docview/210937143?accountid=13158. doi:10.1108/09590550210418128.
   Google Scholar
• Kmart data warehouse to be world’s third biggest. Comput Can. 1998Jun22;24:23. http://ezaccess.libraries.psu.edu/login?url=http://search.proquest.com.ezaccess.libraries.psu.edu/docview/225008930?accountid=13158.
   Google Scholar
• Dedić N, Stanier C. An Evaluation of the Challenges of Multilingualism in Data Warehouse Development. Conference: 18th International Conference on Enterprise Information Systems - ICEIS 2016; 2016; At Rome, Italy, vol. 1. doi: 10.5220/0005858401960206
   Google Scholar
• Kimball R, Ross M. The data warehouse. Indianapolis, IN: John Wiley & Sons; 2013.
   Google Scholar
• Eckerson W. Data quality and the bottom line: achieving business success through a commitment to high quality data. Data Warehous Inst. 2002:1–36.
   Google Scholar
• Sterman JD. Business dynamics: Systems thinking and modeling for a complex world. Boston, Mass: Irwin McGraw-Hill; 2000.
   Google Scholar
• Herndon AA, Cramer M, Nicholson T, Miller S. Analysis of Advanced Flight Management Systems (FMS), Flight Management Computer (FMC) field observations trials: area Navigation (RNAV) holding patterns. Digital Avionics Systems Conference (DASC), 2011 IEEE/AIAA 30th (pp. 4A1–1). Seattle, WA, USA, IEEE; 2011, Oct
   Google Scholar
• Golfarelli M, Rizzi S. A comprehensive approach to data warehouse testing. Proceedings of the ACM twelfth international workshop on Data warehousing and OLAP (pp. 17–24). Chicago, IL, USA, ACM; 2009Nov.
   Google Scholar
• Batini C, Cappiello C, Francalanci C, Maurino A. Methodologies for data quality assessment and improvement. ACM Comput Surv (CSUR). 2009;41(3):1–52. doi:10.1145/1541880.1541883.
   Web of Science ®Google Scholar
• Singh R, Singh K. A descriptive classification of causes of data quality problems in data warehousing. Int J Comput Sci Issue. 2010;7(3):41–50.
   Google Scholar
• Wand Y, Wang RY. Anchoring data quality dimensions in ontological foundations. Commun ACM. 1996;39(11):86–95. doi:10.1145/240455.240479.
   Web of Science ®Google Scholar
• Paim FRS, Castro J. Enhancing data warehouse design with the NFR framework. Wer. 2002;2:40–57.
   Google Scholar
• Sen A, Ramamurthy K, Sinha AP. A model of data warehousing process maturity. IEEE Trans Software Eng. 2012;38(2):336–53. doi:10.1109/TSE.2011.2.
   Web of Science ®Google Scholar
• Calero C, Piattini M, Genero M. Metrics for controlling database complexity. Dev Qual Complex Data. 2000:48–68.
   Google Scholar
• Serrano M, Calero C, Trujillo J, Luján-Mora S, Piattini M. Empirical validation of metrics for conceptual models of data warehouses. International Conference on Advanced Information Systems Engineering (pp. 506–20). Springer Berlin Heidelberg; 2004, Jun.
   Google Scholar
• Serrano M, Trujillo J, Calero C, Piattini M. Metrics for data warehouse conceptual models understandability. Inf Software Technol. 2007;49(8):851–70. doi:10.1016/j.infsof.2006.09.008.
   Web of Science ®Google Scholar
• Gosain A, Nagpal S, Sabharwal S. Quality metrics for conceptual models for data warehouse focusing on dimension hierarchies. ACM SIGSOFT Software Eng Note. 2011;36(4):1–5. doi:10.1145/1988997.1989015.
   Google Scholar
• Ballou DP, Tayi GK. Enhancing data quality in data warehouse environments. Commun ACM. 1999;42(1):73–78. doi:10.1145/291469.291471.
   Web of Science ®Google Scholar
• Han J, Pei J, and Kamber M. Data mining: Concepts and techniques. Waltham, MA: Elsevier; 2012.
   Google Scholar
• Hernández MA, Stolfo SJ. Real-world data is dirty: data cleansing and the merge/purge problem. Data Min Knowl Discov. 1998;2(1):9–37. doi:10.1023/A:1009761603038.
   Web of Science ®Google Scholar
• Nixon N. NongFu Spring Continues Growing With SAP HANA, SAP Business Trends; 2012 [accessed 2015 Aug 31]. http://scn.sap.com/community/business-trends/blog/2012/08/07/nongfu-spring-grows-with-sap-hana
   Google Scholar
• Baboo SS, Kumar PR. Next generation data warehouse design with OLTP and OLAP systems sharing the same database. Int J Comput Appl. 2013;72(13): doi:10.5120/12557-9282.
   Google Scholar
• Lemme S. Database management best practices: expert tips for improving DBMS infrastructures; 2006, Jul 16 [accessed 2017 Feb 17]. http://searchdatamanagement.techtarget.com/news/1199349/Database-management-best-practices-Expert-tips-for-improving-DBMS-infrastructures
   Google Scholar
• Luna-Reyes LF, Andersen DL. Collecting and analyzing qualitative data for system dynamics: methods and models. Syst Dyn Rev. 2003;19(4):271–96. doi:10.1002/(ISSN)1099-1727.
   Web of Science ®Google Scholar
• Fendt J, Sachs W. Grounded theory method in management research. Organ Res Method. 2008;11(3):430–55. doi:10.1177/1094428106297812.
   Web of Science ®Google Scholar
• Glaser B, Strauss A. The discovery of grounded theory: strategies for qualitative research. USA: Yale University Press; 1967.
   Google Scholar
• Strauss AL, Corbin J. Grounded theory research: procedures, canons and evaluative criteria. Zeitschrift Für Soziologie. 1990;19 Jg:S. 418.
   Web of Science ®Google Scholar
• Serrano M, Calero C, Piattini M. Metrics for data warehouse quality. Proceedings of the International Workshop on Design and Management of Data Warehouses (DMDW’2001); 2001 [accessed 2001 Jun 4]; Interlaken, Switzerland.
   Google Scholar
• Gaur H, Sharma R. Assessment of data warehouse model quality. Int J Adv Res Comput Sci. 2013;4(9). http://ezaccess.libraries.psu.edu/login?url=http://search.proquest.com.ezaccess.libraries.psu.edu/docview/1444033602?accountid=13158.
   Google Scholar
• Bouzeghoub M, Fabret F, Matulovic M. Modeling data warehouse refreshment process as a workflow application, Proceedings of the Workshop on Design and Management of Data Warehouses (DMDW’99); 1999; Heidelberg, Germany.
   Google Scholar
• Kimball R. The data warehouse lifecycle toolkit. 2nd ed. Indianapolis, IN: Wiley Pub; 2008. Chapter 1 & 2.
   Google Scholar
• Ariyachandra T, Watson H. Key organizational factors in data warehouse architecture selection. Decis Support Syst. 2010;49(2):200–12. doi:10.1016/j.dss.2010.02.006.
   Web of Science ®Google Scholar
• Sujitparapitaya S. An empirical study of the effects of organizational structure on the implementation of data warehouse topologies. Memphis, TN, USA: Memphis State University; 2000.
   Google Scholar
• Rolland C. A comprehensive view of process engineering. Adv Inf Syst Eng Lecture Note Comput Sci. 1998:1–24. doi:10.1007/bfb0054216.
   Google Scholar
• Atzeni P, Cheung D, Ram S. Conceptual modeling: 31st International conference, ER 2012, Florence, Italy, October 15-18, 2012: Proceedings. Heidelberg: Springer; 2012. 64–77.
   Google Scholar
• Gosain A. Literature review of data model quality metrics of data warehouse. Procedia Comput Sci. 2015;48:236–43. doi:10.1016/j.procs.2015.04.176.
   Google Scholar
• Schuff D, Corral K, Turetken O. Comparing the understandability of alternative data warehouse schemas: an empirical study. Decis Support Syst. 2011;52(1):9–20. doi:10.1016/j.dss.2011.04.003.
   Web of Science ®Google Scholar
• Al-Badarneh A, Al-Badarneh O. Challenges and interesting research directions in model driven architecture and data warehousing: a survey. Int J Comput Sci Inf Secur. 2016;14(3):364–98.
   Google Scholar
• Khajaria K, Kumar M. Modeling of security requirements for decision information systems. ACM SIGSOFT Software Eng Note. 2011;36(5):1. doi:10.1145/2020976.2020989.
   Google Scholar
• Breur T. Data quality is everyone’s business – designing quality into your data warehouse – part 1. J Dir Data Dig Mark Pract. 2009;11(1):20–29. doi:10.1057/dddmp.2009.14.
   Google Scholar
• Calvanese D, De Giacomo G, Lenzerini M, Nardi D, Rosati R. A principled approach to data integration and reconciliation in data warehousing, Proceedings of the International Workshop on Design and Management of Data Warehouses (DMDW’99); 1999; Heidelberg, Germany.
   Google Scholar
• Hwang MI, Xu H. A structural model of data warehousing success. J Comput Inf Syst. 2008;49(1):48–56. doi:10.1080/08874417.2008.11645305.
   Web of Science ®Google Scholar
• Lee ML, Lu H, Ling TW, Ko YT. Cleansing data for mining and warehousing. Lec Note Comput Sci Data Exp Syst Appl. 1999:751–60.
   Google Scholar
• Georgakopoulos D, Hornick D, Sheth A. An overview of workflow management: from process modeling to workflow automation infrastructure. Distributed Parallel Databases. 1995;3:119–53. 38. doi:10.1007/BF01277643.
   Web of Science ®Google Scholar
• Malabocchia F, Buriano L, Mollo M, Richeldi M, Rossotto M. Mining telecommunications databases: an approach to support the business management. NOMS 98 1998 IEEE Network Operations and Management Symposium; 1998. doi:10.1109/noms.1998.654855im.2005.06.007
   Google Scholar
• Manning A. databases for small business: essentials of database management, data analysis, and staff training for entrepreneurs and professionals. Chapter 10. New York, NY: Apress. 2015. doi:10.1007/978-1-4842-0277-7_10.
   Google Scholar
• Courtemanche C, Carden A. Competing with costco and sam’s club: warehouse Club entry and grocery prices. Southern Economic Journal. 2014;80(3):565–85. doi:10.4284/0038-4038-2012.135.
   Web of Science ®Google Scholar
• Fazzinga B, Flesca S, Masciari E, Furfaro F. Efficient and effective RFID data warehousing. Proceedings of the 2009 International Database Engineering & Applications Symposium on - IDEAS ’09; 2009. doi:10.1145/1620432.1620459
   Google Scholar
• Han SC et al. Using MCRDR based Agile approach for expert system development. Computing. 2014:96(9)897–908.
   Web of Science ®Google Scholar
• Westerman P. data warehousing: Using the wal-mart model. San Francisco, CA: Morgan Kaufmann; 2001.
   Google Scholar
• Kohavi R, Mason L, Parekh R, Zheng Z. Lessons and challenges from mining retail E-commerce data. Mach Learn. 2004;57(1–2):83–113. doi:10.1023/B:MACH.0000035473.111.
   Web of Science ®Google Scholar
• Li H. Applications of data warehousing and data mining in the retail industry. Proceedings of ICSSSM ‘05. 2005 International Conference on Services Systems and Services Management; 2005. doi:10.1109/icsssm.2005.1500153
   Google Scholar
• Lu X, Huang L, Heng MS. Critical success factors of inter-organizational information systems: a case study of Cisco and Xiao Tong in China. Inf Manag. 2006;43(3):395–408. doi:10.1016/j.
   Web of Science ®Google Scholar
• Ross D. Retail Data Warehousing: the state of the art; 2005 [accessed 2017 Jan 30]. http://www.b-eye-network.com/view/769
   Google Scholar
• Paul SF, Krishnamurthi M. Forecasting using data warehousing model: Walmart’s experience. J Bus Forecast Method Sys. 2001;20(3):13–17.
   Google Scholar
• Roshna R, Punitha S, Sowbhagya M. Business intelligence solutions in retail. Int Proc Comput Sci Inf Technol. 2014;59:26.
   Google Scholar
• Laurent W. Challenges of the international customer data warehouse. DM Rev. 2004;14(7):36.
   Google Scholar
• Mann CJH. Fundamentals of data warehouses. Kybernetes. 2003;32(7):1196–97. doi:10.1108/k.2003.06732gae.009.
   Google Scholar
• Bruckner RM, Tjoa AM. Capturing delays and valid times in data warehouses–towards timely consistent analyses. J Intell Inf Syst. 2002;19(2):169. doi:10.1023/A:1016555410197.
   Web of Science ®Google Scholar
• Dobbs T, Stone M, Abbott J. UK data warehousing and business intelligence implementation. Qual Market Res. 2002;5(4):235–2D. doi:10.1108/13522750210443182.
   Google Scholar
• Goeke RJ. An examination of the effects of experience, expertise, and perceived flexibility on data warehouse use (Order No. 3236837); Chester, PA: ProQuest. 2006.
   Google Scholar
• Granebring A, Révay P. Service-oriented architecture is a driver for daily decision support. Kybernetes. 2007;36(5):622–35. doi:10.1108/03684920710749712.
   Web of Science ®Google Scholar
• Winter R, Klesse M. Manage business metadata and ensure information quality. Busi Intell J. 2009 Third;14:31–39.
   Google Scholar
• Su Y, Peng J, Jin Z. Modeling information quality risk for data mining in data warehouses. Hum Ecol Risk Assess: Int J. 2009;15(2):332–50. doi:10.1080/10807030902761411.
   Web of Science ®Google Scholar
• Haq QM. Data mapping for data warehouse design. Waltham, MA: Morgan Kaufmann; 2016. 67–165.
   Google Scholar
• Mannino M, Hong SN, Choi IJ. Efficiency evaluation of data warehouse operations. Decis Supp Syst. 2008;44(4):883–98. doi:10.1016/j.dss.2007.10.011.
   Web of Science ®Google Scholar
• Preston D, Brohman K. Outsourcing opportunities for data warehousing business usage. Logistics Inf Manag. 2002;15(3):204–11. doi:10.1108/09576050210426751.
   Google Scholar
• Salim N, Ibrahim R. Quality-based framework for requirement analysis in data warehouse. 2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA); 2014. doi:10.1109/icaicta.2014.7005932
   Google Scholar
• Strauss AL, Corbin J. Basics of qualitative research: Techniques and procedures for developing grounded theory. Thousand Oaks, London, New Delhi: Sage; 1998.
   Google Scholar