物联网中的数据挖掘:互联网新范式的数据分析

Peter Wlodarczak, Mustafa A. Ally, J. Soar
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引用次数: 14

摘要

本文概述了物联网(IoT)的数据挖掘(DM)技术。物联网已经成为一个活跃的研究领域,因为物联网有望改善智慧城市的生活质量和安全,提高资源供应和废物管理的效率,并优化交通。DM是高度特定于领域的,并且取决于所挖掘的内容。例如,如果使用物联网来优化智能城市的交通以减少交通拥堵并更快地找到停车位,则需要从电子健康解决方案收集和分析不同类型的数据,其中物联网用于智能家居以监测患者或老年人的健康状况。物联网连接的东西可以从智能传感器收集数字数据,从摄像头收集流数据或地图上的路线信息。根据数据的类型,需要采用不同的技术来分析它们。此外,许多物联网应用程序分析来自不同设备的数据,并将它们关联起来,以预测生产现场可能出现的机器故障或家庭安全应用中智能建筑中迫在眉睫的紧急情况。DM技术需要处理物联网数据的异构性、大量数据和生成速度。本文探讨了物联网DM技术的最新进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data mining in IoT: data analysis for a new paradigm on the internet
This paper provides an overview on Data Mining (DM) technologies for the Internet of Things (IoT). IoT has become an active area of research, since IoT promises among other to improve quality of live and safety in Smart Cities, to make resource supply and waste management more efficient, and optimize traffic. DM is highly domain specific and depends on what is being mined for. For instance, if IoT is used to optimize traffic in a Smart City to reduce traffic jams and to find parking spaces quicker, different types of data needs to be collected and analysed from an eHealth solution, where IoT is used in a Smart Home to monitor the well being of patients or elderly people. IoT connects things that can collect numeric data from smart sensors, streaming data from cameras or route information on maps. Depending on the type of data, different techniques need to be adopted to analyse them. Also, many IoT applications analyse data from different devices and correlate them to make predictions about possible machine failures in production sites or looming emergency situations in Smart Buildings in a home security application. DM techniques need to handle the heterogeneity of IoT data, the large volumes of data and the speed at which they are produced. This paper explores the state of the art DM techniques for IoT.
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