Data extraction

Source: Wikipedia, the free encyclopedia.

Data extraction is the act or process of retrieving

export to another stage in the data workflow
.

Usually, the term data extraction is applied when (

electronic devices will usually present an electrical connector (e.g. USB) through which 'raw data' can be streamed into a personal computer
.

Data sources

Typical unstructured data sources include web pages, emails, documents, PDFs, scanned text, mainframe reports, spool files, classifieds, etc. which is further used for sales or marketing leads. Extracting data from these unstructured sources has grown into a considerable technical challenge where as historically data extraction has had to deal with changes in physical hardware formats, the majority of current data extraction deals with extracting data from these unstructured data sources, and from different software formats. This growing process of data extraction from the web is referred to as "Web data extraction" or "Web scraping".

Imposing structure

The act of adding structure to unstructured data takes a number of forms

  • Using text pattern matching such as regular expressions to identify small or large-scale structure e.g. records in a report and their associated data from headers and footers;
  • Using a table-based approach to identify common sections within a limited domain e.g. in emailed resumes, identifying skills, previous work experience, qualifications etc. using a standard set of commonly used headings (these would differ from language to language), e.g. Education might be found under Education/Qualification/Courses;
  • Using
    text analytics
    to attempt to understand the text and link it to other information

See also

  • Data mining, discovery of patterns in large data sets using statistics, database knowledge or machine learning
  • Data retrieval, obtaining data from a database management system, often using a query with a set of criteria
  • Extract, transform, load (ETL), procedure for copying data from one or more sources, transforming the data at the source system, and copying into a destination system
  • Information extraction, automated extraction of structured information from unstructured or semi-structured machine-readable data, like for example using natural language processing to extract content from images, audio or documents