Abstract
Data Warehouse is a repository to store huge detailed and summaries data for historical data analysis. In a decision support system which stores data from remote, complex and heterogeneous operational data sources . A clinical data warehouse contains complex, heterogeneous data from different data sources. In literature, there are different data warehouse architectures are present with there own design issues, which are relevant to different application areas. In this paper, we proposed a conceptual and logical view of data warehouse architecture along with physical implementation of the data warehouse. Our main focus in this paper is to efficiently handle the complex heterogeneous medical data stored into the warehouse and improve the performance of data warehouse for data analysis. Here, we proposed a partitioning concept of the dimension tables and fact tables for optimizing the response time, minimizing the disk IO, along with reducing the joining cost of the data warehouse. To show the effectiveness of our system, we, compare with different joining techniques of the dimension and fact tables of fact-consolidated data warehouse schema. A mathematical cost model of disk IO optimization is being calculated. SQL window partitioning techniques are being used for data analysis of the proposed data warehouse. After storing complex heterogeneous data in well organized and efficient way in a data warehouse, efficient searching techniques need to be incorporated. Here, bitmap indexing technique is used for the purpose.
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References
Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. Wiley, New York (2011)
Inmon, W.H.: Building the Data Warehouse. Wiley, USA (2005)
Ado, A., Aliyu, A., Bello, S.A., Garba, A.U.: Building a diabetes data warehouse to support decision making in healthcare industry. Int. Organ. Sci. Res. J. Comput. Eng. (IOSR-JCE) 16(2), 138–143 (2014)
Nealon, J., Rahayu, W., Pardede, E.: Improving clinical data warehouse performance via a windowing data structure architecture. In: International Conference on Computational Science and Its Applications (ICCSA 2009), pp. 243–253. IEEE (2009)
Chaudhuri, S., Dayal, U., Narasayya, V.: An overview of business intelligence technology. Commun. ACM 54(8), 88–98 (2011)
Ni, Z., Guo, J., Wang, L., Gao, Y.: An efficient method for improving query efficiency in data warehouse. JSW 6(5), 857–865 (2011)
Pentaho Analysis Service: Mondrian Project. http://mondrian.pentaho.org/. Accessed 29 Dec 2017
Jovanovic, V., Bojicic, I.: Conceptual data vault model. Proc. SAIS 23, 1–6 (2012)
Kim, J.W., Cho, S., Kim, I.: Column partitioning to improve data warehouse queryperformance. In: International Workshop on Ubiquitous Science and Engineering, Jeju, South Korea (2015)
Bellatreche, L., Karlapalem, K., Mohania, M., Schneider, M.: What can partitioning do for your data warehouses and data marts? In: 2000 International Database Engineering and Applications Symposium, pp. 437–445. IEEE (2000)
Levene, M., Loizou, G.: Why is the snowflake schema a good data warehouse design? Inf. Syst. 28(3), 225–240 (2003)
Chmiel, J., Morzy, T., Wrembel, R.: Multiversion join index for multiversion data warehouse. Inf. Softw. Technol. 51(1), 98–108 (2009)
Ross, K.A., Srivastava, D., Sudarshan, S.: Materialized view maintenance and integrity constraint checking: trading space for time. ACM SIGMOD Rec. 25, 447–458 (1996)
Zhang, C., Yao, X., Yang, J.: An evolutionary approach to materialized views selection in a data warehouse environment. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 31(3), 282–294 (2001)
Rizzi, S., Saltarelli, E.: View materialization vs. indexing: balancing space constraints in data warehouse design. In: Eder, J., Missikoff, M. (eds.) CAiSE 2003. LNCS, vol. 2681, pp. 502–519. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45017-3_34
Wu, M.C., Buchmann, A.P.: Encoded bitmap indexing for data warehouses. In: 1998 Proceedings of the 14th International Conference on Data Engineering, pp. 220–230. IEEE (1998)
Koudas, N.: Space efficient bitmap indexing. In: Proceedings of the 9th International Conference on Information and Knowledge Management, pp. 194–201. ACM (2000)
DeWitt, D.J., Madden, S., Stonebraker, M.: How to build a high-performance data warehouse (2005). http://db.lcs.mit.edu/madden/high_perf.pdf. Accessed June 2011
V. G. H. Information, Information about Health Data Standards and Systems (HDSS) used in Victoria’s Hospital (2008). http://www.health.vic.gov.au/hdss/index.html. Accessed 23 Dec 2017
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Garain, N., Chattopadhyay, S., Mahapatra, G., Chatterjee, S., Mondal, K.C. (2019). Design and Implementation of an Improved Data Warehouse on Clinical Data. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1031. Springer, Singapore. https://doi.org/10.1007/978-981-13-8581-0_23
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DOI: https://doi.org/10.1007/978-981-13-8581-0_23
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