Real Time Distributed Data Mining for Sensor Networks

Louisiana State University, Baton Rouge LA

Investigators

Abstract

Data mining is the semi-automatic discovery of patterns, associations, changes, anomalies, rules, and statistically significant structures and events in data. Thus, data mining aims to extract knowledge from sensor data, and is an emerging area with numerous commercial applications. Work in data mining ranges from theoretical work on the principles of learning and mathematical representations of data to building advanced engineering systems that perform information filtering on the web, find genes in DNA sequences, help understand trends and anomalies in economics and medicine, and detect network intrusion. The problem gets more complicated in a distributed system where the data itself is positioned across different sites and coordination across the sites needed to mine information from the distributed data. Distributed Data Mining (DDM) arises in diverse areas such as sensor stream data generated from satellite etc. Presently, there are several proposed ad-hoc approaches to distributed data mining (DDM) that deal with specific schemes and most of them do not address any real time analysis for solving different classes of distributed data mining problems. This project will address this very explicitly the merits of the new framework.