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Semi-structured data. Semi-structured data[1] is a form of structured data that does not obey the tabular structure of data models associated with relational databases or other forms of data tables, but nonetheless contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data ...
Unstructured data. Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text -heavy, but may contain data such as dates, numbers, and facts as well.
Document-oriented databases are one of the main categories of NoSQL databases, and the popularity of the term "document-oriented database" has grown [2] with the use of the term NoSQL itself. XML databases are a subclass of document-oriented databases that are optimized to work with XML documents. Graph databases are similar, but add another ...
A data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs), and binary data (images, audio, video). [3] A data lake can be established on premises (within an organization's data centers) or in the cloud (using cloud services).
Because of the way it is produced and stored, this information is either unstructured or semi-structured. The management of semi-structured data is an unsolved problem in the information technology industry. [17] According to projections from Gartner (2003), white-collar workers spend 30–40% of their time searching, finding, and assessing ...
Data-Flow Diagram example [19] A data-flow diagram (DFD) is a graphical representation of the "flow" of data through an information system. It differs from the flowchart as it shows the data flow instead of the control flow of the program. A data-flow diagram can also be used for the visualization of data processing (structured design).
Information extraction. Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. Typically, this involves processing human language texts by means of natural language processing (NLP). [1]
Knowledge extraction. Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing.