BI 系統最主要的功能是整理龐大資料，轉化為有用訊後產生報表給 主管做決策的參考，因此資料新鮮度成為非常重要議題；越代表 主管做決策的參考，因此資料新鮮度成為非常重要議題；越代表 主管做決策的參考，因此資料新鮮度成為非常重要議題；越代表 主管做決策的參考，因此資料新鮮度成為非常重要議題；越代表 主管做決策的參考，因此資料新鮮度成為非常重要議題；越代表 著主管所獲取的資料越即時 (real time)，主管可以藉著這些資料做出更為準確的決 ，主管可以藉著這些資料做出更為準確的決 策。
但是處理資料的過 程必須經ETL ETL的步驟， ETL ETL的架構複雜且處理時間久， 資料往喪失其新鮮度。許多企業為了解決此問題，開發以即時 (real time)為目的資料儲存體(cube)工具，但是多樣化的選擇卻造成了決策上困難。
因此本研究的主要目為建立資料儲存體 (cube)模型，幫助使用者在不同新 鮮度及限制下的資料儲存體 (cube)中做選擇，避免浪費時間在不合需求的工具執 行

The most important purpose of BI system is to store integrated data for helping mangers making decisions with timely reports, which need to be generated in a limited time interval and containing fresh data.
Data cleansing and integration is very time consuming operations, which may take hours to be completed in many organizations. With the long processing time, some organizations opt to execute the operations in batch, which may be executed in less business hours to speed up system performance in trading of freshness. On the other hand, some reports such as inventory level summary, many need most current data which need to be load into BI systems in the real time. Therefore, users have to make decisions if the data cleansing and integration operations need to be processed in real time or in batch for every reports. This research proposes a methodology to suggest the decision based on the freshness requirement and waiting time users are willing to wait for reports.

[1]Agrawal, R., A. Gupta, et al. (1997). Modeling multidimensional databases, IEEE.
[2]Blaser, B., K. Fleckenstein, et al. (2011) SAP Enterprise Data Warehouse for Point of Sales Data Optimized for IBM DB2 for Linux, UNIX, and Windows on IBM Power Systems.
[3]Cho, J. and H. Garcia-Molina (2000). Synchronizing a database to improve freshness, ACM.
[4]Datta, A. and H. Thomas (1999). "The cube data model: a conceptual model and algebra for on-line analytical processing in data warehouses." Decision Support Systems 27(3): 289-301.
[5]Gray, J., S. Chaudhuri, et al. (1997). "Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals." Data Mining and Knowledge Discovery 1(1): 29-53.
[6]Gyssens, M. and L. V. S. Lakshmanan (1997). A foundation for multi-dimensional databases, Citeseer.
[7]Hammer, J., H. Garcia-Molina, et al. (1995). "The Stanford data warehousing project."
[8]Harinarayan, V., A. Rajaraman, et al. (1996). Implementing data cubes efficiently, ACM.
[9]Inmon, W. H. (2005). Building the data warehouse, Wiley-India.
[10]Kimball, R. and M. Ross (2002). The data warehouse toolkit: the complete guide to dimensional modeling. New York, Wiley.
[11]Mecella, M., M. Scannapieco, et al. (2002). "Managing data quality in cooperative information systems." On the Move to Meaningful Internet Systems 2002: CoopIS, DOA, and ODBASE: 486-502.
[12]Palekar, A., B. Patel, et al. (2010). A Practical Guide to SAP NetWeaver Business Warehouse 7.0.
[13]Peralta, V. (2006). Data freshness and data accuracy: A state of the art, Tech. rep., Universidad de la Republica, Uruguay.
[14]SAP (2006). BW310 Data Warehousing SAP NetWeaver.
[15]Segev, A. and W. Fang (1990). Currency-based updates to distributed materialized views, IEEE.
[16]Shin, B. (2003). "An exploratory investigation of system success factors in data warehousing." Journal of the Association for Information Systems 141(170): 170.
[17]Song, J., Y. Bao, et al. (2010). A Triggering and Scheduling Approach for ETL in a Real-time Data Warehouse, IEEE.
[18]Theodoratos, D. and M. Bouzeghoub (1999). Data currency quality factors in data warehouse design.
[19]Wang, R. Y. and D. M. Strong (1996). "Beyond accuracy: What data quality means to data consumers." Journal of management information systems: 5-33.
[20]Weyerhaeuser, C., T. Mindnich, et al. (2008). Exploiting Graphic Card Processor Technology to Accelerate Data Mining Queries in SAP NetWeaver BIA, IEEE.
[21]國立中央大學管理學院ERP中心 (2010). 商業智慧, 滄海書局.
[22]薛如珊 (2007). 商業智慧18年，台灣IDC(國際數據資訊)分析師觀點.