Learn More
The MapReduce framework has become the de-facto framework for large-scale data analysis and data mining. One important area of data analysis is graph analysis. Many graphs of interest, such as the Web graph and Social Networks, are very large in size with millions of vertices and billions of edges. To cope with this vast amount of data, researchers have(More)
In our previous work, we described various aspects of our approach in converting big raw rainfall data into meaningful storm concepts. Three concepts were defined: local, hourly, and overall storms. The latter describes overall spatio-temporal characteristics of a storm as it progresses over time. We previously described MapReduce-based algorithms for local(More)
Distributed frameworks, such as MapReduce and Spark, have been developed by industry and research groups to analyze the vast amount of data that is being generated on a daily basis. Many graphs of interest, such as the Web graph and Social Networks, increase their size daily at an unprecedented scale and rate. To cope with this vast amount of data,(More)
In recent time data mining on big data is very tedious task in current scenario because huge data cannot be handling in the memory with different format type of data. In distributed environment web pages access by the user having some patterns, these patterns are merging and finding closed frequent set of web pages. Now do the Fuzzy C-Means clustering of(More)
This paper extends our previous work on deriving meaningful storm patterns from very large rainfall data. In an earlier work, we described MapReduce-based algorithms to identify three types of the storms: local, hourly and overall storms. In general, local storms have temporal characteristics of the storms at a particular site, hourly storms have spatial(More)
  • 1