BIG DATA analysis
Big Data usually use to to capture, curate, manage, and process the huge data within a tolerable elapsed time. Big data sizes are a constantly moving target from a few dozen terabytes to many petabytes of data in a single data set. The target moves due to constant improvement in traditional DBMS technology as well as new databases like NoSQL and their ability to handle larger amounts of data. With this difficulty, new platforms of "big data" tools are being developed to handle various aspects of large quantities of data.
- Business Intelligence uses descriptive statistics with data with a High information density to measure things, detect trends.
- Big Data uses inductive statistics with data with a Low information density whose huge volume allow to infer laws (regressions) and thus giving (with the limits of inference reasoning) to Big Data some predictive capacities .
Examples include Big Science, RFID, sensor networks, social networks, big social data analysis (due to the social data revolution), Internet text and documents, Internet search indexing, call detail records, astronomy, atmospheric science, genomics, biogeochemical, biological, and other complex and often interdisciplinary scientific research, military surveillance, forecasting drive times for new home buyers, medical records, photography archives, video archives, and large-scale e-commerce.
