When a decision needs to be made, the decision maker will usually try to get as much information as possible to assist in making such a decision. In general, some simple but very meaningful statistic functions are often used to retrieve information. However, the operational systems have to spend a lot of time and do a lot of calculations in order to fulfill the decision maker’s needs, and which will influence the performance of the daily operations. That is one of the major reasons why a data warehouse comes up in industry.
Data warehouse contains a large amount of information in which a large number of summary tables or materialized aggregate views are built in order to increase the system performance. As it changes, most notably of new transactional data, are collected from the various data sources, all summary tables in the warehouse that depend upon this data ought to be updated accordingly. Since the number of summary tables that need to be maintained is often large, a critical issue for data warehousing is how to maintain the summary tables efficiently.
In this thesis, we investigate the currently available maintenance methods being used by the industry to enhance the data warehouse. By reviewing the literature and proposing an approach, some more auxiliary data will be kept inside a data warehouse whose role is like a legacy to enhance the maintenance processes of some statistic functions. This proposed approach has made a big improvement in the maintenance of the data warehouse process. The time needed to maintain those basic statistical functions reduce from minutes to seconds. And finally, we perform some comparative analysis to verify the proposal methods.

Agrawal, D., Abbadi, A. E., Singh, A., & Yurek, T. (1997). Efficient view maintenance at data warehouses. Paper presented at the ACM SIGMOD international conference on management of data, Tucson, Arizona, United States.
Akinde, M. O., Jensen, O. G., & Bohlen, M. H. (1998). Minimizing detail data in data warehouses. Paper presented at the The sixth international conference on extending database technology, Spain.
Benader, A., Benader, B., Fadlalla, A., & James, G. (2000). Data warehouse administration and management. information systems management, 17(1), 71-80.
Blakeley, J. A., Larson, P.-A., & tompa, F. W. (1986). Efficiently updating materialized views. Paper presented at the 1986 ACM SIGMOD international conference on management of data, Washington, D.C., United States.
Chao, C.-M. (2004). Incremental maintenance of object-oriented data warehouses. Information sciences, 160, 91-110.
Cho, M., Pei, J., & Wang, K. (2007). Answering ad hoc aggregate queries form data streams using prefix aggregate trees. Knowledge and information systems, 12(3), 301-329.
Devlin, B. (2000). Data warehouse from architecture to implementation: Addision Wesley.
Gupta, A., Mumick, I. S., & Subrahmanian, V. S. (1993). Maintaining views incrementally. Paper presented at the ACM SIGMOD international conference on managemetn of data, Washington DC.
Gupta, H., & Mumick, I. S. (2005). Selection of views to materialize in a data warehouse. IEEE transactions on knowledge and data engineering, 17, 24-43.
Gupta, H., & Mumick, I. S. (2006). Incremental maintenance of aggregate and outerjoin expressions. Information systems, 31, 435-464.
Imilinski, T., & Witold Lipski, J. (1984). Imcomplete information in relational database. Jounal of the ACM, 31(4), 761-791.
Inmon, W. H. (1996). Building the data warehouse: Wiley.
Inmon, W. H., Welch, J. D., & Glassey, K. L. (1997). Managing the data warehouse: Wiley.
Kimball, R., Reeves, L., Ross, M., & Thornthwaite, W. (1998). The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing, and Deploying Data Warehouses: Wiley.
KDD Cup (1999) http://archive.ics.uci.edu/ml/databases/kddcup99/kddcup99.html.
Laurent, D., Lechtenborger, J., Spyratos, N., & Vossen, G. (2001). Monotonic complements for independent data warehouses. The VLDB journal, 10, 295-315.
Lee, K. Y., Song, J. H., & Kim, M. H. (2007). Reducing the cost of accessing relations in incremental view maintenance. Decision Support Systems, 43, 512-526.
Li, H.-G., Yu, H., Agrawal, D., & Abbadi, A. E. (2007). Progressive ranking of range aggregates. Data & Knowledge Engineering, 63, 4-25.
Liang, W., Li, H., Wang, H., & Orlowska, M. E. (1999). Making multiple views self-maintainable in a data warehouse. Data & Knowledge Engineering, 30, 121-134.
Mannino, M. V., & Walter, Z. (2006). A framework for data warehouse refresh policies. Decision Support Systems, 42, 121-143.
Mohania, M., & Kambayshi, Y. (2000). Making aggregate views self-maintainable. Data & Knowledge Engineering, 32, 87-109.
Mumick, I. S., Quass, D., & Mumick, B. S. (1997). Maintenance of data cubes and summary tables in a warehouse. Paper presented at the 1997 ACM SIGMOD international conference of management of data, Tucson, Arizona, United States.
Nemati, H. R., Steiger, D. M., Iyer, L. S., & Herschel, R. T. (2002). Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decision Support Systems, 33, 143-161.
Ponniah, P. (2001). Data warehousing fundamentals: Wiley.
Quass, D., Gupta, A., Mumick, I. S., & Widom, J. (1996). Making views self-maintainable for data warehousing. Paper presented at the PDIS.
Sen, A. (2004). Metadata management: past, present and future. Decision Support Systems, 37, 151-173.
Shi, Z., Huang, Y., He, Q., Xu, L., liu, S., Qin, L., et al. (2007). MSMiner-a developing platform for OLAP. Decision Support Systems, 42, 2016-2028.
Shu, H. (1997). View maintenance using conditional tables. Paper presented at the 5th DOOD, Berlin Heidelberg New York.
Skyt, J., Jensen, C. S., & Pedersen, T. B. (2008). Specification -based data reduction in
dimensional data warehouses. Information systems, 33, 36-63.
Tremblay, M. C., Fuller, R., Berndt, D., & Studmicki, J. (2007). Doing more with more
information: Changing healthcare planning with OLAP tools. Decision Support Systems,
43, 1305-1320.
Watson, H. J., & Ariyachandra, T. (2005). Data warehouse architectures: factors in the selection
decision and the success of the architectures . Athens, Georgia: Terry college of business,
university of Georgia.
Yeung, G. C. H., & Gruver, W. A. (2005). Multiagent immediate incremental view maintenance
for data warehouses. IEEE transactions on systems man and cybernetics part a -systems
and humans, 35, 305-310.
Zhang, X., & Rundensteiner, E. A. (2002). Integrating the maintenance and synchronizaiton of
data warehouses using a cooperative framework. Information systems, 27, 219-243.