Civil and Environmental Engineering

1
Sensors Knowledge Discovery (a.k.a. Data
Mining)

• H. Scott Matthews
• April 6, 2004
• (originally presented by Rebecca Buchheit)

2
Recap

• Sensors - what are they?
• Sensor Networks - how they help us
• Sensor Signal Acquisition and Use
• Next - how to use the data!

3
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

4
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 monitoring
• e.g. 50-100 years for bridges (to manage LCC)

5
A Knowledge Discovery Framework for Civil
Infrastructure Contexts

• Rebecca Buchheit
• Department of Civil and Environmental Engineering
• Carnegie Mellon University

6
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

7
Motivation

• amount of data, relationships between attributes,
  context-sensitivity, observational collection
  methods gt 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

8
Databases Statistics and Machine Learning
Data Mining
statistics
databases
data mining
machine learning
9
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

10
Knowledge Discovery Process Steps

• domain understanding
• data understanding
• data preparation
• data modeling (a.k.a data mining)
• results evaluation
• deployment

11
CRISP-DM

• CRoss-Industry Standard Process for Data Mining
• high-level, hierarchical, iterative process model
  for KDD
• provides framework for applying KDD consistently

12
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?

13
Data Understanding

• summary statistics
• plotting and visualization
• missing 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?

14
Data Preparation

• most time-consuming part of KDD
• data selection
• which records (rows) to use
• which attributes (columns) to use
• data cleaning
• do something to bad and missing data
• integrate data from different sources
• transform data

15
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

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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

17
Data Mining Tasks

• Classification
• Regression
• Clustering
• Deviation detection
• Summarization
• Dependency modeling

18
Classification

• learn how to classify data items into predefined
  groups

19
Regression

• map a real-valued dependent variable to one or
  more independent variables

20
Clustering

• learn natural classes or clusters of data

21
Deviation Detection

• detect changes or deviations from normal or
  baseline state

22
Summarization

• summarize subsets of data set

computer industry mean salary 65k service
industry mean salary 20k
23
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.
24
Data Mining in the IW

• concept description using classification
• environmental 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 gt50 instances in energy usage range

25
Results Evaluation

• do results meet clients criteria?
• novel?
• understandable?
• valid (modeling phase)?
• useful?

26
Results Deployment

• explain results to client
• improvements to data collection?
• ongoing process applied to new data?

27
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

28
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

29
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

30
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?

31
Summary

• increased data collection gt 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