Civil and Environmental Engineering Carnegie Mellon University Sensors & Knowledge Discovery...

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

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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

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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

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Knowledge Discovery Process Steps

domain understanding

data understanding

data preparation

data modeling (a.k.a “data mining”)

results evaluation

deployment

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CRISP-DM

CRoss-Industry Standard Process for Data Mining

high-level, hierarchical, iterative process model for KDD

provides framework for applying KDD consistently

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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?

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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

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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

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Data Mining Tasks

• Classification

• Regression

• Clustering

• Deviation detection

• Summarization

• Dependency modeling

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Classification

learn how to classify data items into predefined groups

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Regression

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

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Clustering

learn “natural” classes or clusters of data

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Deviation Detection

detect changes or deviations from “normal” or baseline state

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Summarization

summarize subsets of data set

computer industrymean salary = $65kservice industrymean salary = $20k

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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

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Results Evaluation

do results meet client’s criteria?

novel?

understandable?

valid (modeling phase)?

useful?

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Results Deployment

explain results to client

improvements to data collection?

ongoing process applied to new data?

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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

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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