Data Mapping is one of the first steps used in data integration tasks. Building a data map will help the users to avoid any potential issues. In this article, we mentioned 5 well-known Data mapping tools and applications.
1.Python Record Linkage Toolkit
With its abundant libraries and toolkits, Python offers a special package called Python Record Linkage Toolkit. The toolkit helps in record linking within or external data sources and provides maximum accessories needed for record linkage and deduplication. The toolkit was specially designed for analysis and the linking of small or average sized files. Inspired by the FEBRL project the toolkit has an advanced feature of data manipulation tools. However, this feature is not supported by FEBRL. This advanced feature is used to integrate record linkage directly with available data manipulation projects.
The key objective of the toolkit is to develop an extensible record linkage structure.
The toolkit helps clean and regulate data with simple techniques, performing recording pairs with intelligent indexing methods, impair records with a great number of correlating and similarity measures for various types of variables such as strings, numbers and date. The toolkit has a number of supervised and unsupervised classified algorithms, boosted with record linkage evaluation with various built-in datasets.
Record Linkage (R)
The Record Linkage package is developed to promote the application of record linkage in R. The package emerged while using R for record linkage of streaming data. It provides an interpretation of various designs which lead to abundant availability of functions and data structures. Combination of these functions and data structures as an R package facilitates the application of record linkage techniques to different datasets.
- The tool builds comparison patterns by providing compare. dedup function for deduplication and compare.linkage function for linking two or more data sets together
- The ReLinkData class includes other components which help in the process of Data Linkage
- The package helps in blocking, which reduces the number of data pairs by focusing on specified patterns
- The kit supports the phonetic functions and string comparators which deal with typographical errors in character strings
2.FRIL(A Fine-Grained Record Integration And Linkage Tool)
FRIL tool boosts the classical linkage tools with a loaded set of parameters. Users can systematically and iteratively explore the best combination of parameter values which improve linking performances with accuracy. The tool has the potential to boost the accuracy of data linkage throughout all the suggested record linkage.
FRIL uses some algorithms which are user-controlled parameters that are naturally stored in common linkage tools such as Link King10, Link Plus 11 and many more. The tool includes the standard process of record mapping.
- Association of graphical tools for adapting schema discrepancy and for analyzing, validating and summarizing results
- Development of computerized learning tools to enable suggestion of natural parameters
- Implementing search methods namely, nested loop join(NLJ) and sorted neighbourhood method (SNM) for comparing small and average data files
Dedupe is a Web API library which uses machine learning to implement de-duplication and entity resolution instantly on structured data. The library aids in removing duplicate entries from a spreadsheet of names and addresses. It links a list with user information to another list with organisational history without individual customer ids. Dedupe processes instruction data fed and drums up rules for the user dataset to facilitate a quick and automatic search for similar records with enormous databases.
- A machine learning technique which reads the human labelled data and naturally creates best weights and blocking rules
- Runs on personal computers and makes smart comparisons which don’t require the advanced server to run the tool as a library, this is possible as the library integrates to user applications
- Allows extensions by adding designed data types, string comparators and blocking rules
The application is built to automatically recognise identical records and eliminate the redundant data, which reduces the storage needs for files and backups considerably. The application helps people working with virtual machines or sharing large files with disorganised data across the servers by performing regular backups.
- Finds duplicate values by using records linkage and fuzzy match analysis
- Allows users to define how the duplicates and handle them
- Minimizes storage needs and operational costs