Real Time Distributed Data Mining for Sensor Networks

数据挖掘是半自动地发现数据中的模式、关联、变化、异常、规则以及具有统计意义的结构和事件。因此，数据挖掘旨在从传感器数据中提取知识，是一个具有众多商业应用的新兴领域。数据挖掘的工作范围从学习原理和数据的数学表示的理论工作到建立先进的工程系统，这些系统在网络上执行信息过滤，在DNA序列中发现基因，帮助理解经济和医学中的趋势和异常，并检测网络入侵。在分布式系统中，问题变得更加复杂，在分布式系统中，数据本身位于不同的站点，并跨从分布式数据挖掘信息所需的站点进行协调。分布式数据挖掘(DDM)出现在各种不同的领域，如卫星产生的传感器流数据等。目前，已提出的分布式数据挖掘(DDM)方法有几种特定的方案，但大多数都没有针对不同类型的分布式数据挖掘问题进行实时分析。这个项目将非常明确地解决这一问题，新框架的优点。

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.