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FME Enabled ETL Processes for Spatial and Attribute Data Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 169 - Number 5 |
| Year of Publication: 2017 |
| Authors: Farhad Alam, Sanjay Pachauri |
10.5120/ijca2017914754
|
Farhad Alam, Sanjay Pachauri . FME Enabled ETL Processes for Spatial and Attribute Data Analysis. International Journal of Computer Applications. 169, 5 ( Jul 2017), 31-35. DOI=10.5120/ijca2017914754
Abstract
ETL is a type of data integration that refers to the three steps (extract, transform, and load) used to blend data from multiple sources. It's often used to build a data warehouse. During this process, data is taken (extracted) from a source system, converted (transformed) into a format that can be analyzed, and stored (loaded) into a data warehouse or other system. FME has a rich data model designed implement ETL. FME provides tremendous transformation functionality, resulting in output that can be much greater than the sum of the inputs, and allowing data to be transformed from one type to another. The current paper uses FME workbench and implement the concept of ETL using a case study where a private firm wants to integrate attribute and spatial information regarding its employee, filter the unnecessary information and finally implement business query regarding Monthly Travelling Allowance. The results establish ETL and FEM as interdisciplinary technological domain and backbone of the data warehouse architecture.
References
- Jukic, N., 2006. Modeling strategies and alternatives for data warehousing projects. Communications of the ACM, 49(4), pp.83-88.
- Kimball, R. and Ross, M., 2011. The data warehouse toolkit: the complete guide to dimensional modeling. John Wiley & Sons.
- Cuzzocrea, A., Song, I.Y. and Davis, K.C., 2011, October. Analytics over large-scale multidimensional data: the big data revolution!. In Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP (pp. 101-104). ACM.
- Eckerson, W. and White, C., 2003. Evaluating ETL and data integration platforms. Seattle: The DW Institute.
- Golfarelli, M., Rizzi, S. and Cella, I., 2004, November. Beyond data warehousing: what's next in business intelligence?. In Proceedings of the 7th ACM international workshop on Data warehousing and OLAP (pp. 1-6). ACM.
- Karakasidis, A., Vassiliadis, P. and Pitoura, E., 2005, June. ETL queues for active data warehousing. In Proceedings of the 2nd international workshop on Information quality in information systems (pp. 28-39). ACM.
- Jarke, M., Lenzerini, M., Vassiliou, Y., Vassiliadis, P., 2003. Fundamentals of Data Warehouses, second ed. Springer-Verlag.
- Suresh, S., Gautam, J.P., Pancha, G., DeRose, F.J. and Sankaran, M., Informatica Corporation, 2001. Method and architecture for automated optimization of ETL throughput in data warehousing applications. U.S. Patent 6,208,990.
- Berson, A., Smith, S.J., 1997. Data Warehousing, Data Mining, and OLAP. McGraw-Hill.
- Moss, L.T., 2005. Moving Your ETL Process into Primetime. (visited June 2005).
- Feature Manipulation Engine (FME): https://www.safe.com
- Microsoft Office Home: https://www.office.com
- Google Earth: http://earth.google.com
- KML OGC:www.opengeospatial.org/standards/ kml
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