Using a Business Activity Monitoring and SOA for a Real-Time ETL

Data that is obtained from various information sources needs excessively handling for managing, analyzing and monitoring. Data warehouse consolidates data coming from different data sources. Data warehousing technology has made a huge effect in the world of business; it transforms information into data that helps analysts to make strategic decisions. A real-time data warehouse is used same purpose as data warehouse, in addition to these, data streams into real time data warehouse on time. In this paper, propose a real-time framework to support the up-to-date process. Our framework is based on using a Business Activity Monitoring (BAM) that provides a real-time business intelligence by capturing data as it flows through a business system. Using service-oriented architecture (SOA) and Windows Communication Foundation (WCF) to capture a real-time data and load easily. By using BAM, it can monitor a business process in real time.

[1]  Frank Leymann,et al.  Monitoring and Analyzing Influential Factors of Business Process Performance , 2009, 2009 IEEE International Enterprise Distributed Object Computing Conference.

[2]  Michael zur Muehlen,et al.  Business Process Analytics , 2015, Handbook on Business Process Management.

[3]  Owen Molloy,et al.  Towards a Semantic Framework for Business Activity Monitoring and Management , 2008, AAAI Spring Symposium: AI Meets Business Rules and Process Management.

[4]  Ghizlane Orhanou,et al.  Data Integrity in Real-time Datawarehousing , 2013 .

[5]  Raghu Ramakrishnan,et al.  Database Management Systems , 1976 .

[6]  Ghizlane Orhanou,et al.  An Integration Adaptation for Real-Time Datawarehousing , 2014 .

[7]  Zeki Erdem,et al.  A real time data warehouse approach for data processing , 2013, 2013 21st Signal Processing and Communications Applications Conference (SIU).

[8]  Ralph Kimball,et al.  The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data , 2004 .

[9]  Pedro Furtado,et al.  Near real-time with traditional data warehouse architectures: factors and how-to , 2013, IDEAS '13.

[10]  Ning Wang,et al.  Based on event-driven and service-oriented architecture business activity monitoring design and implementation , 2011, 2011 International Conference on System science, Engineering design and Manufacturing informatization.

[11]  Erhard Rahm,et al.  Data Cleaning: Problems and Current Approaches , 2000, IEEE Data Eng. Bull..

[12]  David J. Evans,et al.  True Real-Time Change Data Capture with Web Service Database Encapsulation , 2010, 2010 6th World Congress on Services.

[13]  Stephen R. Gardner Building the data warehouse , 1998, CACM.

[14]  Ardianto Wibowo,et al.  Problems and available solutions on the stage of Extract, Transform, and Loading in near real-time data warehousing (a literature study) , 2015, 2015 International Seminar on Intelligent Technology and Its Applications (ISITIA).