Mining in a Mobile Environment

Authors: 
Sean McRoskey, Jim Notwell, Nitesh V. Chawla, and Christian Poellabauers
Citation: 
McRoskey, Sean, et al. "Mining in a mobile environment." Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data. ACM, 2009.
Publication Date: 
June, 2009

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.