(Big-)Data Analytics: verschil tussen versies
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Big data is a recent phenomenon. Traditional data analytics are not able to handle large quantities of data, so specific algorithms have been developed: Big Data Analytics. | Big data is a recent phenomenon. Traditional data analytics are not able to handle large quantities of data, so specific algorithms have been developed: Big Data Analytics. | ||
Huidige versie van 16 dec 2019 om 08:55
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Why
Big data is a recent phenomenon. Traditional data analytics are not able to handle large quantities of data, so specific algorithms have been developed: Big Data Analytics.
How
Advanced machine learning algorithms are used to analyse massive data sets. These algorithms have an iterative character and are error-tolerant. Examples of presently used methods are MapReduce and Hadoop Distributed File System.
Ingredients
- Large volumes of data.
- Hypothesis to test.
- A dedicated storage system: user friendly, open, with fast access times.
- Appropriate analysis tools.
Practice
Big data has increasing benefits for both research institutes and industry, such as healthcare, financial services, and commerce. Big data is used primarily to predict transient power and stock prices, amongst others. In a Smart City application, traffic data from the past and present are used to analyse and visualise city information on a 3-D platform.
