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Wikipedia

Data mapping

August 25, 2023

In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including:[1]

  • Data transformation or data mediation between a data source and a destination
  • Identification of data relationships as part of data lineage analysis
  • Discovery of hidden sensitive data such as the last four digits of a social security number hidden in another user id as part of a data masking or de-identification project
  • Consolidation of multiple databases into a single database and identifying redundant columns of data for consolidation or elimination

For example, a company that would like to transmit and receive purchases and invoices with other companies might use data mapping to create data maps from a company's data to standardized ANSI ASC X12 messages for items such as purchase orders and invoices.

Standards Edit

X12 standards are generic Electronic Data Interchange (EDI) standards designed to allow a company to exchange data with any other company, regardless of industry. The standards are maintained by the Accredited Standards Committee X12 (ASC X12), with the American National Standards Institute (ANSI) accredited to set standards for EDI. The X12 standards are often called ANSI ASC X12 standards.

The W3C introduced R2RML as a standard for mapping data in a relational database to data expressed in terms of the Resource Description Framework (RDF).

In the future, tools based on semantic web languages such as RDF, the Web Ontology Language (OWL) and standardized metadata registry will make data mapping a more automatic process. This process will be accelerated if each application performed metadata publishing. Full automated data mapping is a very difficult problem (see semantic translation).

Hand-coded, graphical manual Edit

Data mappings can be done in a variety of ways using procedural code, creating XSLT transforms or by using graphical mapping tools that automatically generate executable transformation programs. These are graphical tools that allow a user to "draw" lines from fields in one set of data to fields in another. Some graphical data mapping tools allow users to "auto-connect" a source and a destination. This feature is dependent on the source and destination data element name being the same. Transformation programs are automatically created in SQL, XSLT, Java, or C++. These kinds of graphical tools are found in most ETL (extract, transform, and load) tools as the primary means of entering data maps to support data movement. Examples include SAP BODS and Informatica PowerCenter.

Data-driven mapping Edit

This is the newest approach in data mapping and involves simultaneously evaluating actual data values in two data sources using heuristics and statistics to automatically discover complex mappings between two data sets. This approach is used to find transformations between two data sets, discovering substrings, concatenations, arithmetic, case statements as well as other kinds of transformation logic. This approach also discovers data exceptions that do not follow the discovered transformation logic.

Semantic mapping Edit

Semantic mapping is similar to the auto-connect feature of data mappers with the exception that a metadata registry can be consulted to look up data element synonyms. For example, if the source system lists FirstName but the destination lists PersonGivenName, the mappings will still be made if these data elements are listed as synonyms in the metadata registry. Semantic mapping is only able to discover exact matches between columns of data and will not discover any transformation logic or exceptions between columns.

Data lineage is a track of the life cycle of each piece of data as it is ingested, processed, and output by the analytics system. This provides visibility into the analytics pipeline and simplifies tracing errors back to their sources. It also enables replaying specific portions or inputs of the data flow for step-wise debugging or regenerating lost output. In fact, database systems have used such information, called data provenance, to address similar validation and debugging challenges already.[2]

See also Edit

References Edit

  1. ^ Shahbaz, Q. (2015). Data Mapping for Data Warehouse Design. Elsevier. p. 180. ISBN 9780128053355. Retrieved 29 May 2018.
  2. ^ De, Soumyarupa. (2012). Newt : an architecture for lineage based replay and debugging in DISC systems. UC San Diego: b7355202. Retrieved from: https://escholarship.org/uc/item/3170p7zn

August 25, 2023
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This article needs attention from an expert in Information Architecture The specific problem is The information appears outdated and requires sources both historical history of data mapping and current how is data mapping performed today WikiProject Information Architecture may be able to help recruit an expert May 2018 In computing and data management data mapping is the process of creating data element mappings between two distinct data models Data mapping is used as a first step for a wide variety of data integration tasks including 1 Data transformation or data mediation between a data source and a destination Identification of data relationships as part of data lineage analysis Discovery of hidden sensitive data such as the last four digits of a social security number hidden in another user id as part of a data masking or de identification project Consolidation of multiple databases into a single database and identifying redundant columns of data for consolidation or eliminationFor example a company that would like to transmit and receive purchases and invoices with other companies might use data mapping to create data maps from a company s data to standardized ANSI ASC X12 messages for items such as purchase orders and invoices Contents 1 Standards 2 Hand coded graphical manual 3 Data driven mapping 4 Semantic mapping 5 See also 6 ReferencesStandards EditX12 standards are generic Electronic Data Interchange EDI standards designed to allow a company to exchange data with any other company regardless of industry The standards are maintained by the Accredited Standards Committee X12 ASC X12 with the American National Standards Institute ANSI accredited to set standards for EDI The X12 standards are often called ANSI ASC X12 standards The W3C introduced R2RML as a standard for mapping data in a relational database to data expressed in terms of the Resource Description Framework RDF In the future tools based on semantic web languages such as RDF the Web Ontology Language OWL and standardized metadata registry will make data mapping a more automatic process This process will be accelerated if each application performed metadata publishing Full automated data mapping is a very difficult problem see semantic translation Hand coded graphical manual EditData mappings can be done in a variety of ways using procedural code creating XSLT transforms or by using graphical mapping tools that automatically generate executable transformation programs These are graphical tools that allow a user to draw lines from fields in one set of data to fields in another Some graphical data mapping tools allow users to auto connect a source and a destination This feature is dependent on the source and destination data element name being the same Transformation programs are automatically created in SQL XSLT Java or C These kinds of graphical tools are found in most ETL extract transform and load tools as the primary means of entering data maps to support data movement Examples include SAP BODS and Informatica PowerCenter Data driven mapping EditThis is the newest approach in data mapping and involves simultaneously evaluating actual data values in two data sources using heuristics and statistics to automatically discover complex mappings between two data sets This approach is used to find transformations between two data sets discovering substrings concatenations arithmetic case statements as well as other kinds of transformation logic This approach also discovers data exceptions that do not follow the discovered transformation logic Semantic mapping EditSemantic mapping is similar to the auto connect feature of data mappers with the exception that a metadata registry can be consulted to look up data element synonyms For example if the source system lists FirstName but the destination lists PersonGivenName the mappings will still be made if these data elements are listed as synonyms in the metadata registry Semantic mapping is only able to discover exact matches between columns of data and will not discover any transformation logic or exceptions between columns Data lineage is a track of the life cycle of each piece of data as it is ingested processed and output by the analytics system This provides visibility into the analytics pipeline and simplifies tracing errors back to their sources It also enables replaying specific portions or inputs of the data flow for step wise debugging or regenerating lost output In fact database systems have used such information called data provenance to address similar validation and debugging challenges already 2 See also EditData integration Data wrangling Identity transform ISO IEC 11179 The ISO IEC Metadata registry standard Metadata Metadata publishing Schema matching Semantic heterogeneity Semantic mapper Semantic translation Semantic web Semantics XSLT XML Transformation LanguageReferences Edit Shahbaz Q 2015 Data Mapping for Data Warehouse Design Elsevier p 180 ISBN 9780128053355 Retrieved 29 May 2018 De Soumyarupa 2012 Newt an architecture for lineage based replay and debugging in DISC systems UC San Diego b7355202 Retrieved from https escholarship org uc item 3170p7zn Retrieved from https en wikipedia org w index php title Data mapping amp oldid 1171305135, wikipedia, wiki, book, books, library,

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