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University of Southern California
Department of Civil and Environmental Engineering
CE 599
Data Management
Fall, 2014
Thursdays
3:30PM – 6:10PM
Classroom:
Instructor
Office
Phone
Fax
E-mail
Office Hours
Teaching Assistant:
E-Mail:
Office and Office Hours:
Lucio Soibelman
KAP 210A
(213) 740-0609
(213) 744-1426
[email protected]
Tuesday 3:00 PM to 5:00 PM
Course Motivation
Currently, Civil and Environmental Engineers are experiencing explosive growths in its
capabilities to both generate and collect data. Advances in scientific data collection, the
introduction of bar codes for almost all commercial products, sensors, radio frequency
identification tags (RFIDs), laser scanners, and computerization have generated a flood of
data. All this instrumentation allows today's practitioners to push the boundaries of
traditional design, operation and management of the natural and built environment
through faster and more precise measurement and control at the same time that advances
in data storage technology, such as faster, higher capacity, and less expensive storage
devices (e.g. magnetic disks, CD-ROMS), better database management systems, and data
warehousing technology, have allowed the transformation of this enormous amount of
data into computerized database systems. As the Civil and Environmental Engineers are
adapting to new computer technologies in terms of hardware and software, computerized
data are becoming more and more available. However, in most cases, these data may not
be used, or even properly stored.
This explosive growth in stored data has generated an urgent need for civil and
environmental engineers to design and manage novel data acquisition tools, advanced
database systems, and to develop new techniques and automated tools that can
intelligently assist them in transforming the vast amounts of data into useful information
and knowledge
Course Description
This course introduces:
 The basics of data acquisition hardware and software.
 The basics of databases and database management systems as applied to
engineering problems in general and civil and environmental engineering
problems in specific. The focus is on the relational data model, with an
introduction to object modeling. The course lectures emphasize database
concepts and theory.
 Introduction to data mining concepts and techniques and knowledge discovery in
databases principles applied to engineering problems/data in general and civil
engineering problems/data in specific. The course lectures emphasizes on issues
relating to the feasibility, usefulness, efficiency and scalability of techniques for
the discovery of patterns hidden in engineering databases.
This course introduces the following concepts:
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Data Acquisition hardware
Data Acquisition software
Sampling rate
DBMS basic concepts
Relational model
Database systems architecture
Database design
Entity-Relationship model
Normalization
Constraints and integrity
Introduction to engineering databases and web databases
Data warehouse
Data mining tasks
Introduction to classification, prediction, and clustering
Course Objectives
The course is to prepare graduate students for design and development of simple data
acquisition systems, engineering databases learning how to extract knowledge from
hidden patterns on large engineering databases.
Readings (optional)
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Garcia-Molina, H., Ullman, J.D., and Widom, J., “Database Systems: The Complete Book”,
Prentice Hall, Upper Saddle River, NJ, 2002.
Jiawei Han and Micheline Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann
Publishers, San Francisco, 2001.
Jeffrey Travis and Jim Kring, LabVIEW for Everyone: Graphical Programming Made Easy and
Fun, Prentice Hall, 3rd Edition, ISBN: 0-13-185672-3.
Jacob Fraden, Handbook of Modern Sensors: Physics, Designs, and Applications, 4th Edition,
Springer, 2010. ISBN: 1441964657.
Most of the readings and the class notes will be available in PowerPoint format in the
USC Blackboard. The student is responsible for reading the assigned material and is
expected to come prepared for participation in class.
Course Requirements
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(5% of total grade) Participation and reading assignments
(65% of total grade) Written assignments (PS)
(30% of total grade) Short Quizzes (SQ)
Related Administrative Information
Cheating, Plagiarism, Computer Use
Everyone is required to do individual work on individual assignments. Discussions with
other students about concepts and overall approaches to solving individual assignments
are permitted. Please read these sentences very carefully: You all can help each other
learn the material better if you are permitted to ask each other clarifying questions and
discuss concepts. However, copying another student’s spreadsheet or directly copying
another student’s answers is clearly plagiarism.
When your “discussion” is
unidirectional, you are on shaky ground. Each student must submit their own work and
understand what they did on that assignment.
Any occurrence of inappropriate collaboration, cheating or plagiarism will be dealt with
in accordance with University policy
Statement of Academic Integrity
USC seeks to maintain an optimal learning environment. General principles of academic
honesty include the concept of respect for the intellectual property of others, the
expectation that individual work will be submitted unless otherwise allowed by an
instructor, and the obligations both to protect one’s own academic work from misuse by
others as well as to avoid using another’s work as one’s own.
All students are expected to understand and abide by these principles.
SCampus, the Student Guidebook, contains the Student Conduct Code in Section 11.00,
while the recommended sanctions are located in Appendix A:
http://www.usc.edu/dept/publications/SCAMPUS/gov/.
Students will be referred to the Office of Student Judicial Affairs and Community
Standards for further review, should there be any suspicion of academic dishonesty. The
Review process can be found at:
http://www.usc.edu/student-affairs/SJACS/.
Statement for Students with Disabilities
Any student requesting academic accommodations based on a disability is required to
register with Disability Services and Programs (DSP) each semester. A letter of
verification for approved accommodations can be obtained from DSP. Please be sure the
letter is delivered to me as early in the semester as possible.
DSP Contact Information
Office location: STU 301
Hours open: 8:30 a.m. until 5:00 p.m., Monday through Friday.
Phone number: (213) 740-0776
Class participation
Students are expected to be in class on-time and participate in class discussions. If you
cannot make class, please inform your instructors and group members ahead of time. In
class, students are expected to be courteous and respectful of the views and needs of other
students and instructors.
Assignments
Written assignments are due in class as indicated in each assignment. Partial credit will
be given for assignments that are late. 10% will be subtracted for each day that the
assignment is late for up to three days. In case a major problem is anticipated, please talk
to the instructor.
Date
08/28
09/04
09/11
09/18
09/25
10/02
10/09
10/16
10/23
10/30
11/06
11/13
11/20
11/27
12/04
Class
Introduction
Principle of Sensors
Fundamentals
of
Data
Acquisition
Hands-on: LabView and Data
Acquisition
Introduction
to
Signal
Processing
Entity-Relationship
Data
Model
The Relational Model
Normalization
Database Design
SQL
Introduction to Data Mining
and CE Examples
Data Warehouse
Computer Learning
Data Cleaning
Holiday-No class
Prediction
Classification
Thanksgiving Holiday
Clustering
Mini Quiz
Assignment
PS1 out
PS1 due/PS2 out
PS2 due/PS3 out
MQ1
PS3 due/PS4 out
PS4 due / PS5 out
MQ2
PS5 due