Title :
Using R, WEKA and RapidMiner in Time Series Analysis of Sensor Data for Structural Health Monitoring
Author :
Kosorus, Hilda ; Hönigl, Jürgen ; Küng, Josef
Author_Institution :
Inst. of Appl. Knowledge Process., Johannes Kepler Univ., Linz, Austria
fDate :
Aug. 29 2011-Sept. 2 2011
Abstract :
In the past years research done in the field of structural health monitoring has been focusing on the development of a robust and cost-effective monitoring solution by integrating and extending technologies from various engineering and information technology disciplines. There are many data mining, statistical computing and graphics tools available that can be successfully applied and integrated into the analysis and monitoring processes. In this paper we present and compare some of the main functionalities of three well-known data mining tools and programming languages (R, WEKA and Rapid Miner) and how they can be successfully used in the domain of time series analysis of sensor data in structural health monitoring.
Keywords :
computer graphics; condition monitoring; data analysis; data mining; programming languages; statistical analysis; structural engineering computing; time series; R programming language; Rapid Miner programming language; WEKA programming language; data mining tools; graphics tools; information technology; sensor data; statistical computing; structural health monitoring; time series analysis; Analytical models; Autoregressive processes; Data mining; Data visualization; Monitoring; Stochastic processes; Time series analysis; sensor data; structural health monitoring; time series analysis;
Conference_Titel :
Database and Expert Systems Applications (DEXA), 2011 22nd International Workshop on
Conference_Location :
Toulouse
Print_ISBN :
978-1-4577-0982-1
DOI :
10.1109/DEXA.2011.88