It is based on an open-source project called Hadoop that manages computing resources across large clusters of computers.
In this report, we will present the heavy-ion trigger strategy developed for the unique two-layer trigger system of the CMS experiment which consists of a "Level-1" trigger based on custom electronics and a High Level Trigger (HLT) implemented using a large cluster of commodity computers.
These algorithms are generally very computationally intensive, some requiring a large cluster of parallel computers.
Hadoop supports distributed applications running on large clusters of commodity computers processing enormous amounts of data.
Hadoop supports distributed applications running on large clusters of commodity computers processing enormous amounts of data, technology that's being put to use by Internet juggernauts like Google, Yahoo, Facebook, Amazon, AOL, Baidu, The NY Times, Joost and many more.
Third, MapReduce enables scaling of applications across large clusters of lower-cost commodity computers comprising thousands of nodes, with fault-tolerance built in for ultra-fast performance.
The software is optimized for fast performance on standard stand-alone computers, multi-core processors, and large clusters of identical nodes.
The technical reason is that the likelihood value "underflows" (becomes too small for the computer to represent it) for large clusters of sub-observations, such as major cities.
Anything with large clusters of diamonds will do nicely.
Recent cloud-based intrusion detection techniques have predominantly employed the MapReduce model, an abstract programming model that processes large datasets on clusters of computers.
