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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
Year of Publication: 2017
Farhad Alam, Sanjay Pachauri

Farhad Alam and Sanjay Pachauri. FME Enabled ETL Processes for Spatial and Attribute Data Analysis. International Journal of Computer Applications 169(5):31-35, July 2017. BibTeX

	author = {Farhad Alam and Sanjay Pachauri},
	title = {FME Enabled ETL Processes for Spatial and Attribute Data Analysis},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {169},
	number = {5},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {31-35},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017914754},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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Extract, Transform, and Load (ETL), Feature Manipulation Engine (FME), Keyhole Markup Language (KML), Attribute.