15 January 2018

Data Science: Semi-Structured Data (Definitions)

"Data that has flexible metadata, such as XML." (Marilyn Miller-White et al, "MCITP Administrator: Microsoft® SQL Server™ 2005 Optimization and Maintenance 70-444", 2007)

"'Text' documents, such as e-mail, word processing, presentations, and spreadsheets, whose content can be searched." (David G Hill, "Data Protection: Governance, Risk Management, and Compliance", 2009)

"Data that, although unstructured, still has some degree of structure. A good example is e-mail: Even though it is predominantly text, it has logical blocks with different purposes." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Data that have already been processed to some extent." (Carlos Coronel & Steven Morris, "Database Systems: Design, Implementation, & Management" 11th Ed., 2014)

"A structured data type that does not have a formal definition, like a document. It has tags or other markers to enforce a hierarchy of records within a particular object, but may be different from another object." (Jason Williamson, Getting a Big Data Job For Dummies, 2015)

"Semi-structured data has some structures that are often manifested in images and data from sensors." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"a form a structured data that does not have a formal structure like structured data. It does however have tags or other markers to enforce hierarchy of records." (Analytics Insight)

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IT Professional with more than 24 years experience in IT in the area of full life-cycle of Web/Desktop/Database Applications Development, Software Engineering, Consultancy, Data Management, Data Quality, Data Migrations, Reporting, ERP implementations & support, Team/Project/IT Management, etc.