@Article{jiot.2021.013163,
AUTHOR = {S. Kalyani, A. Mary Sowjanya, K. Venkat Rao},
TITLE = {A Novel Integrated Machine \& Business Intelligence Framework for Sensor  Data Analysis},
JOURNAL = {Journal on Internet of Things},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {27--38},
URL = {http://www.techscience.com/jiot/v3n1/41736},
ISSN = {2579-0080},
ABSTRACT = {Increased smart devices in various industries are creating numerous 
sensors in each of the equipment prompting the need for methods and models for 
sensor data. Current research proposes a systematic approach to analyze the data 
generated from sensors attached to industrial equipment. The methodology 
involves data cleaning, preprocessing, basics statistics, outlier, and anomaly 
detection. Present study presents the prediction of RUL by using various 
Machine Learning models like Regression, Polynomial Regression, Random 
Forest, Decision Tree, XG Boost. Hyper Parameter Optimization is performed to 
find the optimal parameters for each variable. In each of the model for RUL 
prediction RMSE, MAE are compared. Outcome of the RUL prediction should 
be useful for decision maker to drive the business decision; hence Binary
cclassification is performed, and business case analysis is performed. Business 
case analysis includes the cost of maintenance and cost of non-maintaining a 
particular asset. Current research is aimed at integrating the machine intelligence 
and business intelligence so that the industrial operations optimized both in 
resource and profit.},
DOI = {DOI:10.32604/jiot.2021.013163}
}