Difference between revisions of "Big data"
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* [[Informatics]] | * [[Informatics]] | ||
* [[Information]] | * [[Information]] | ||
| + | * [[Machine learning]] - a subfield of [[computer science]] (more particularly [[soft computing]]) that evolved from the study of [[pattern recognition]] and computational learning theory in artificial intelligence. | ||
* [[Web scraping]] | * [[Web scraping]] | ||
Revision as of 09:44, 18 August 2016
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate.
Challenges
Challenges include:
- Analysis
- Capture
- Data curation
- Search
- Sharing
- Storage
- Transfer
- Visualization
- Information privacy
The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set.
Accuracy in big data may lead to more confident decision making. Better decisions can mean greater operational efficiency, cost reduction and reduced risk.
Analysis of data sets can find new correlations, to "spot business trends, prevent diseases, combat crime and so on."
Scientists, business executives, practitioners of media and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics.
Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, and biological and environmental research.
See also
- Data
- Data (computing)
- Data mining
- Data processing
- Informatics
- Information
- Machine learning - a subfield of computer science (more particularly soft computing) that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.
- Web scraping
