Virtual Data Mart for Measuring Organizational Achievement Using Data Virtualization Technique (KPIVDM)

Authors

  • Ayad Hameed Mousa Computer Science, University of Karbala, Iraq
  • Norshuhada Shiratuddin School of Multimedia and Communication Technology, Universiti Utara Malaysia, Malaysia
  • Muhamad Shahbani Abu Bakar School of Computing, Universiti Utara Malaysia, Malaysia

DOI:

https://doi.org/10.11113/jt.v68.2932

Keywords:

Data integration, data virtualization, data mart, data warehouse, KPI and organizational performance

Abstract

Currently in the dynamic environment, organizations are confronted with new and growingly vital decisions which can impact their very survival. In fact, these demands are increasing the pressure on Information Technology in order to ensure that data will be delivered properly at the right time and faster rate. In this paper, we propose to build a virtual data mart, especially for Organizational KPIs by using data virtualization technology, which can be used to help KPI developers to build and update performance management system quickly and make these systems work in real time. In this paper, we  present a way of identifying and building virtual data marts for Organizational KPIs. The basic principle underlying the proposed approach is that the design of virtual data marts should be driven by the business needs and organizational requirements that each virtual data mart is expected to address. As a consequence, the virtual data mart design process must be based on a deep understanding of the top management’s need and users' expectations. A prototype is recommended to validate the use of the proposed method.

References

B. Sahay, and J. Ranjan. 2008. Real Time Business Intelligence in Supply Chain Analytics. Information Management & Computer Security. 16(1): 28–48.

C. Ramanigopal, G. Palaniappan, and N. Hemalatha. 2012. Business Intelligence for Infrastructure and Construction Industry. ZENITH International Journal of Business Economics & Management Research. 2(6): 71–86.

S. Jain, K. P. Triantis, and S. Liu. 2011. Manufacturing Performance Measurement and Target Setting: A Data Envelopment Analysis Approach. European Journal of Operational Research. 214(3): 616–626.

X. Ke, W. Li, L. Rui et al. 2010. WCDMA KPI Framework Definition Methods and Applications. In 2010 2nd International Conference on Computer Engineering and Technology. 5.

V. Masayna, A. Koronios, J. Gao et al. Data Quality and KPI’s: A link to be Established. 11–14.

S. Bergamaschi, S. Castano, and M. Vincini. 1999. Semantic Integration of Semistructured and Structured Data Sources. ACM Sigmod Record. 28(1): 54–59.

M. H. Bateni, L. Golab, M. T. Hajiaghayi et al. 2011. Scheduling to Minimize Staleness and Stretch in Real-time Data Warehouses. Theory of Computing Systems. 49(4): 757–780.

M. Bouzeghoub, F. Fabret, and M. Matulovic. Modeling Data Warehouse Refreshment Process as a Workflow Application.

M. Castellanos, A. Simitsis, K. Wilkinson et al. Automating the Loading of Business Process Data Warehouses. 612–623.

G. Cavalheiro, A. Dahanayake, and R. J. Welke. Combining Business Activity Monitoring with the Data Warehouse for Event-Context Correlation-Examining the Practical Applicability of this BAM Approach. 263–268.

B. Devlin, and L. D. Cote. 1996. Data Warehouse: From Architecture to Implementation. Addison-Wesley Longman Publishing Co., Inc.,

J. Eder, G. E. Olivotto, and W. Gruber. 2002. A Data Warehouse for Workflow Logs. Engineering and Deployment of Cooperative Information Systems. Springer. 1–15.

D. Fasel, and K. Shahzad. A Data Warehouse Model for Integrating Fuzzy Concepts in Meta Table Structures. 100–109.

E. Franconi, and U. Sattler. 1999. A Data Warehouse Conceptual Data Model for Multidimensional Aggregation:a Preliminary Report.

M. Preis, and J. Seitz. 2012. A Hybrid Approach of Data Warehouse Integration Based on New Storage Technologies. International Journal of Advances in Computing and Management (IJACM).1(1): 40–46.

Munawar, N. Salim, and R. Ibrahim. 2011. Towards Data Quality into the Data Warehouse Development. In 2011 Ninth IEEE International Conference on Dependable, Autonomic and Secure Computing. 8.

J. Nasina, and S. Kondabolu. 2011. Creating a Virtual Data Warehouse for Manufacturing Industry. The IUP Journal of Operations Management. 10(2): 47–59.

J. Nasir, and M. K. Shahzad. 2007. Architecture for Virtualization in Data Warehouse. Innovations and Advanced Techniques in Computer and Information Sciences and Engineering. Springer. 243–248.

H. Plattner, and A. Zeier. 2011. In-memory Data Management: An Inflection Point for Enterprise Applications. Springer.

L. Weng, G. Agrawal, U. Catalyurek et al. An Approach for Automatic Data Virtualization. 24–33.

R. Eve, and J. R. Davis. 2011. Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility: Composite Software.

M. Ferguson. 2011. Succeeding with Data Virtualization High Value Use Cases for Analytical Data Services.

B. Hopkins. 2011. Data Virtualization Reaches the Critical Mass.

R. F. v. d. Lans. 2012. Data Virtualization for Business Intelligence Systems: Revolutionizing Data Integration for Data Warehouses.

J. Richter, L. McFarland, and C. Bredfeldt. 2012. CB4-03: An Eye on the Future: A Review of Data Virtualization Techniques to Improve Research Analytics. Clinical medicine & research 10(3): 166–166.

R. Van der Lans. 2012. Data Virtualization for Business Intelligence Systems: Revolutionizing Data Integration for Data Warehouses. Morgan Kaufmann,

J. Hammer, M. Schneider, and T. Sellis. Data warehousing at the Crossroads.

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Published

2014-05-01

How to Cite

Virtual Data Mart for Measuring Organizational Achievement Using Data Virtualization Technique (KPIVDM). (2014). Jurnal Teknologi, 68(3). https://doi.org/10.11113/jt.v68.2932