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Monitoring Business Transactions for a Real-time Data Warehouses

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Abdelmgeid A. Ali, Waleed M. Mohamed

Abdelmgeid A Ali and Waleed M Mohamed. Monitoring Business Transactions for a Real-time Data Warehouses. International Journal of Computer Applications 146(8):8-11, July 2016. BibTeX

	author = {Abdelmgeid A. Ali and Waleed M. Mohamed},
	title = {Monitoring Business Transactions for a Real-time Data Warehouses},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2016},
	volume = {146},
	number = {8},
	month = {Jul},
	year = {2016},
	issn = {0975-8887},
	pages = {8-11},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2016910828},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Real-time business intelligence (RTPI) is an approach to data analytics that enables business users to get up-to-the-minute data by directly accessing operational systems or feeding business transactions into a real-time data warehouse and business intelligence (BI) system. Business Intelligence (BI) technology has helped many organizations to make better and faster decisions and improve its performance. RTBI allows organizations to evaluate business processes and take strategic action on the current overall business environment. The ability to manage and effectively present the volume of data tracked in today’s business is the cornerstone of data warehousing, but when a business users require up-to-date or real-time data for the purpose of analysis, which presuppose the building of a real-time data warehouse (RTDW). In this paper we propose a real-time framework to support this up-to-date process. Our framework is based on reading transaction log file of external data sources to determine that data changed using changed data capture, then load this data to data warehouse. This framework minimizes impact to the source system and the target data warehouse system.


  1. Sanjay, K Reddy V. Mallikarjuna Jena. "Active Data Warehouse Loading by Tool Based ETL Procedure." International Conference on Information and Knowledge Engineering, 2010: 196-201.
  2. Kobielus, j. "The Forrester Wave: Enterprise Data Warehousing Platforms." Forrester Research, Q1, 2009.
  3. Tanvi Jain, Rajasree S, Shivani Saluja. "Refreshing Data warehouse in Near Real-Time." International Journal of Computer Applications 46, no. 18 (May 2012): 24-28.
  4. R., Cacerta, J. Kimball. The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Wiley, 2004.
  5. C. R. Valencio, M. H. Marioto, G. F. Zafalon, J. M. Machado. "Real Time Delta Extraction Based on Triggers to Support Data Warehousing." The International Conference on Parallel and Distributed Computing, Application, and Technologies (PDCAT). 2013.
  6. R. Kimball, J. Caserta. The Data Warehouse ETL Toolkit:Practical Techniques for Extracting, Cleaning. John Wiley & Sons, 2004.
  7. Abdelmgeid A. Ali, Tarek A. Abdelrahman, Waleed M. Mohamed. "Using Schema Matching in Data Transformation For Warehousing Web Data." International Journal of Information Technologies and Knowledge 7, no. 3 (2013): 230-240.
  8. Erhard Rahm, Hong Hai Do. "Data Cleaning: Problems and Current Approaches." IEEE Data Engineering Bulletin 23, no. 4 (2000): 3-13.


Change Data Capture (CDC), Extract, Transform and Load (ETL), Replicate, Extract, Transform and Load (RETL), Real-Time Data Warehouse (RTDW)