Machine Learning
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Dependable Real-Time Data Mining
ISORC '05 Proceedings of the Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
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Distributed PRocessing in Mobile Environments (DPRiME) is a framework for processing large data sets across an ad-hoc network. Developed to address the shortcomings of Google's MapReduce outside of a fully-connected network, DPRiME separates nodes on the network into a master and workers; the master distributes sections of the data to available one-hop workers to process in parallel. Upon returning results to its master, a worker is assigned an unfinished task. Five data mining classifiers were implemented to process the data: decision trees, k-means, k-nearest neighbor, Naïve Bayes, and artificial neural networks. Ensembles were used so the classification tasks could be performed in parallel. This framework is well-suited for many tasks because it handles communications, node movement, node failure, packet loss, data partitioning, and result collection automatically. Therefore, DPRiME allows users with little knowledge of networking or distributed systems to harness the processing power of an entire network of single- and multi-hop nodes.