Data Curation at Scale: the Data Tamer System (Other (Not Listed) Sample)

DATA CURATION AT SCALE: THE DATA TAMER SYSTEM
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Summary of DATA CURATION AT SCALE: THE DATA TAMER SYSTEM
This Research paper is based on date tamer, which is a system that helps curate the date and make it more organized and cleaned. The paper is divided into multiple parts and all of the parts are further summarized below:
The initial part of the paper explains about different data techniques. This section explains about the tamer, data curation and the purpose of the paper. This section explains about following four attributes:
* Scalability via automation
* Data cleaning
* Non-Programmer orientation
* Instrumentation
The second section compares the different application and argues that Tamer system is better than the current system and it can be implemented in different applications. Following are some of the comparing that is mentioned in this paper:
* Web aggregation Comparison
* Biological application comparison
* Health service application comparison
The third part of the paper explains about the techniques that are used in tamer system and how it will react on different sections. This section is divided in two parts, one is the human factor and other is how tamer will react. Following are the two attributes discussed in section three.
* Human involvement in the tamer system
* Data administrator
* Data expert
* Data tamer working principle
* Sites and schemes
* Console actions
* Updating and training
Section four which is one of the most crucial parts of the paper, it explains about the different methods that tamer system follows with the help of its components. Fourth section also carries the human interaction and involvement in the tamer processes. Following are some of the methods that it follows:
Tamer processes
* Schema integration (it compares the attributes)
* Attribute mapping (It’s about the information available for data acuration)
* Entity consolidation (It finds duplicate entities)
* Bootstrapping the process
* Records categorizing
* Duplicate rule identifying
* Similarity joining
* Clustering and consolidation
Human interaction
* Manual mode
* Crowdsourcing m...