Building environment analysis based on clustering methods from sensor data on top of the Hadoop platform
Martin Sarnovský, David Bajus · 2017
Presented paper describes the use of clustering methods in building environment analysis task. The presented approach is based on modeling of the sensor data containing information about humidity and temperature. Such models are then used to describe the level of the comfort of particular environment. K-means clustering algorithm was used to create those models. The paper then presents and describes a method of user interaction with the environment model. User feed-back represents how the user feels in the current environment. Feedback is then collected and evaluated. Based on the feedback, models can trigger the change of current environment or during the time, re-compute themselves in order to pro-vide more precise building environment representation. Our solution was based on real sensor data obtained from university buildings and presented solution was implemented on top of Hadoop cluster using Mahout library for machine learning.