Data extraction is the act or process of retrieving data out of (usually unstructured or poorly structured) data sources for further data processing or data storage (data migration). The import into the intermediate extracting system is thus usually followed by data transformation and possibly the addition of metadata prior to export to another stage in the data workflow.
Usually, the term data extraction is applied when (experimental) data is first imported into a computer from primary sources, like measuring or recording devices. Today's electronic devices will usually present an electrical connector (e.g. USB) through which 'raw data' can be streamed into a personal computer.
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".
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.
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This course will take place from 3rd to 7th June 2024.It will introduce the workflows and techniques that are used for the analysis of bulk and single cell RNA-seq data. It will empower students to
This course will cover the latest technologies, platforms and research contributions in the area of machine learning systems. The students
will read, review and present papers from recent venues acros
Data scraping is a technique where a computer program extracts data from human-readable output coming from another program. Normally, data transfer between programs is accomplished using data structures suited for automated processing by computers, not people. Such interchange and protocols are typically rigidly structured, well-documented, easily parsed, and minimize ambiguity. Very often, these transmissions are not human-readable at all.
Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites. Web scraping software may directly access the World Wide Web using the Hypertext Transfer Protocol or a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a bot or web crawler. It is a form of copying in which specific data is gathered and copied from the web, typically into a central local database or spreadsheet, for later retrieval or analysis.
In computing, data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration and data management tasks such as data wrangling, data warehousing, data integration and application integration. Data transformation can be simple or complex based on the required changes to the data between the source (initial) data and the target (final) data. Data transformation is typically performed via a mixture of manual and automated steps.
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