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Civil and Environmental Engineering Carnegie Mellon University
Sensors & Knowledge Discovery (a.k.a. Data Mining)
H. Scott Matthews
April 14, 2003
Civil and Environmental Engineering Carnegie Mellon University
Recap of Last Week
Sensors - what are they?
Sensor Networks - how they help us
Sensor Signal Acquisition and Use
Effects of Digital, analog conversions
Range, power, frequency, other constraints
Next - how to use the data!
Civil and Environmental Engineering Carnegie Mellon University
Life Cycles of Sensor Networks
Currently, sensors and sensor systems are fairly proprietary
e.g. a ‘Johnson Controls’ HVAC sensor system uses only their equipment
Need to design more robust networks that are standards-driven and open
Civil and Environmental Engineering Carnegie Mellon University
Life Cycles (2)
In addition, sensor networks then to have very short ‘lifetimes’
i.e. We build one, use it for a few years, and then replace it with a newer/better one
Need to plan for, and design architectures for sensor networks that will last the life of the infrastructure we are monitoringe.g. 50-100 years for bridges (to manage LCC)
Civil and Environmental Engineering Carnegie Mellon University
A Knowledge Discovery Framework for Civil Infrastructure Contexts
Rebecca BuchheitDepartment of Civil and Environmental Engineering
Carnegie Mellon University
Civil and Environmental Engineering Carnegie Mellon University
Motivation• condition and usage patterns of critical
infrastructure attracting increased attention
• deteriorating infrastructure + cheap data collection methods = health monitoring, transportation management, other data intensive civil infrastructure techniques
Civil and Environmental Engineering Carnegie Mellon University
Motivation
• amount of data, relationships between attributes, context-sensitivity, observational collection methods => data mining and knowledge discovery in databases (KDD) process
• our ability to collect data far outstrips our ability to analyze and understand the data at a high level of abstraction
Civil and Environmental Engineering Carnegie Mellon University
Databases + Statistics + and Machine Learning = Data Mining
databasesstatistics
machine learning
data mining
Civil and Environmental Engineering Carnegie Mellon University
Definitions
Data Mining• algorithms to extract patterns from large
data sets
Knowledge Discovery in Databases• “... the non-trivial process of identifying
valid, novel, potentially useful, and ultimately understandable patterns in data.” [Fayyad, et al]
• Uses observational, not controlled, data
Civil and Environmental Engineering Carnegie Mellon University
Knowledge Discovery Process Steps
domain understanding
data understanding
data preparation
data modeling (a.k.a “data mining”)
results evaluation
deployment
Civil and Environmental Engineering Carnegie Mellon University
CRISP-DM
CRoss-Industry Standard Process for Data Mining
high-level, hierarchical, iterative process model for KDD
provides framework for applying KDD consistently
Civil and Environmental Engineering Carnegie Mellon University
Domain Understanding
evaluate fit between KDD and the problem• how much data?• what type of data?• perceived quality of data?• what is being measured?• right data to answer the question?• organizational support?
Civil and Environmental Engineering Carnegie Mellon University
Data Understanding
summary statisticsplotting and visualizationmissing values
• randomly missing• influenced by a measured factor• influenced by an unmeasured factor
evaluate quality of existing data• what is “good” data?• what do we do with “bad” data?
Civil and Environmental Engineering Carnegie Mellon University
Data Preparation
most time-consuming part of KDDdata selection
• which records (“rows”) to use• which attributes (“columns”) to use
data cleaning• do something to bad and missing data
integrate data from different sourcestransform data
Civil and Environmental Engineering Carnegie Mellon University
Data Modeling/Data Mining
choose an algorithm• choose parameters for that algorithm• apply algorithm to data• evaluate results
– predictive accuracy– descriptive coverage
• repeat as necessary
repeat as necessary
Civil and Environmental Engineering Carnegie Mellon University
Data Mining Goals
Prediction• predict the value of one or more variables
based on the values of other variables
Description• describe the data set in a compact, human-
understandable form
Civil and Environmental Engineering Carnegie Mellon University
Data Mining Tasks
• Classification
• Regression
• Clustering
• Deviation detection
• Summarization
• Dependency modeling
Civil and Environmental Engineering Carnegie Mellon University
Classification
learn how to classify data items into predefined groups
Civil and Environmental Engineering Carnegie Mellon University
Regression
map a real-valued dependent variable to one or more independent variables
Civil and Environmental Engineering Carnegie Mellon University
Clustering
learn “natural” classes or clusters of data
Civil and Environmental Engineering Carnegie Mellon University
Deviation Detection
detect changes or deviations from “normal” or baseline state
Civil and Environmental Engineering Carnegie Mellon University
Summarization
summarize subsets of data set
computer industrymean salary = $65kservice industrymean salary = $20k
Civil and Environmental Engineering Carnegie Mellon University
Dependency Modeling
learn relationships between attributes or between items in the data set• pattern recognition• time series analysis• association rules
In 80% of the cases, an engineer with a PE and 10 years experience is a project manager.
Civil and Environmental Engineering Carnegie Mellon University
Data Mining in the IW
concept description using classificationenvironmental conditions affect hot water
energy consumption • used outside temperature, solar radiation and wind
speed • solar radiation and wind speed not significant
above 80F and below 50F • IF temperature between 20F and 30F THEN energy usage between 47,393 kJ and
131,875 kJ • describes >50% instances in energy usage range
Civil and Environmental Engineering Carnegie Mellon University
Results Evaluation
do results meet client’s criteria?
novel?
understandable?
valid (modeling phase)?
useful?
Civil and Environmental Engineering Carnegie Mellon University
Results Deployment
explain results to client
improvements to data collection?
ongoing process applied to new data?
Civil and Environmental Engineering Carnegie Mellon University
Benefits of KDD
Intelligent Workplace• confirmation that system is (not) working• continue to monitor control system• in future, predict missing values to complete
energy studies
Civil and Environmental Engineering Carnegie Mellon University
Apply Data Mining to Civil Infrastructure?
• civil infrastructure meets guidelines for selecting potential data mining problems• significant impact• no good alternatives exist• prior/domain knowledge• effects of noisy data are mitigated• sufficient data• relevant attributes are being measured
Civil and Environmental Engineering Carnegie Mellon University
Background• sporadic use of KDD techniques in civil
infrastructure• relative youth of data mining research• difficult to systematically apply KDD process • KDD process tools (CRISP-DM) still under
development• KDD process highly domain dependent• time consuming to teach data mining analysts
domain knowledge
Civil and Environmental Engineering Carnegie Mellon University
Research Objectives• develop a framework for systematically
applying KDD process to civil infrastructure data analysis needs• set of guidelines for inexperienced analysts• checklist for more experienced analysts
• describe intersection of KDD process characteristics and civil infrastructure• what problems are well-suited to KDD?• what characteristics are unique to
infrastructure?
Civil and Environmental Engineering Carnegie Mellon University
Summary
• increased data collection => increased need to intelligently analyze data
• KDD process as a “power tool” for analyzing data for high-level knowledge
• civil infrastructure problems are well-suited to data mining but will need to apply entire KDD process to get good results
• proposed framework will help researchers to systematically apply KDD process to their data analysis problems