1. Civil and Environmental Engineering Carnegie Mellon University Sensors & Knowledge Discovery (a.k.a. Data Mining) H. Scott Matthews April 14, 2003

2. 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!

3. 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

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

5. Civil and Environmental Engineering Carnegie Mellon University A Knowledge Discovery Framework for Civil Infrastructure Contexts Rebecca Buchheit Department of Civil and Environmental Engineering Carnegie Mellon University

6. 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

7. 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

8. Civil and Environmental Engineering Carnegie Mellon University Databases + Statistics + and Machine Learning = Data Mining databases statistics machine learning data mining

9. 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

10. 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

11. 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

12. 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?

13. Civil and Environmental Engineering Carnegie Mellon University 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. Civil and Environmental Engineering Carnegie Mellon University 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. 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

16. 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

17. Civil and Environmental Engineering Carnegie Mellon University Data Mining Tasks Classification Regression Clustering Deviation detection Summarization Dependency modeling

18. Civil and Environmental Engineering Carnegie Mellon University Classification learn how to classify data items into predefined groups

19. Civil and Environmental Engineering Carnegie Mellon University Regression map a real- valued dependent variable to one or more independent variables

20. Civil and Environmental Engineering Carnegie Mellon University Clustering learn “natural” classes or clusters of data

21. Civil and Environmental Engineering Carnegie Mellon University Deviation Detection detect changes or deviations from “normal” or baseline state

22. Civil and Environmental Engineering Carnegie Mellon University Summarization summarize subsets of data set computer industry mean salary = $65k service industry mean salary = $20k

23. 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.

24. Civil and Environmental Engineering Carnegie Mellon University 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 >50% instances in energy usage range

25. Civil and Environmental Engineering Carnegie Mellon University Results Evaluation do results meet client’s criteria? novel? understandable? valid (modeling phase)? useful?

26. Civil and Environmental Engineering Carnegie Mellon University Results Deployment explain results to client improvements to data collection? ongoing process applied to new data?

27. 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

28. 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

29. 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

30. 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?

31. 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