[DSC 4.0] Conceptual Framework for entity integration from multiple data sources - Drazen Orescanin

With increasing number of data sources that should be addressed in digital transformation process, growing data volumes and data privacy, entity matching and entity resolution are becoming more important disciplines in data management.

Data matching process is also called record linkage, entity matching or entity resolution in some published works.

For long time research about the process was focused on matching entities from same dataset (i.e. deduplication) or from two datasets. Different algorithms used for matching different types of attributes were described in the literature, developed and implemented in data matching and data cleansing platforms. Entity resolution is element of larger entity integration process that include data acquisition, data profiling, data cleansing, schema alignment, data matching and data merge (fusion).

This talk was presented by Mr. Drazen Orescanin, President of the Board of Poslovna Inteligencija, during Data Science Conference 4.0, as a part of Business Intelligence track.

You can find this talk presentation on the following link: https://www.slideshare.net/Insitute_o...

More info about Data Science Conference:
Website: http://datasciconference.com
Instagram:   / datasciconf  
Facebook:   / datasciconference  
Twitter:   / datasciconf  
Flickr: https://www.flickr.com/photos/data-sc...

To watch more new videos regarding Data Science - click subscribe to our YouTube Channel.