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Volgenau School of Engineering
Daniel Barbará
CS
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504
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Title:
Computer Science
Email: [email protected]
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2013
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Principles of Data Management and Mining
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3
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Description (No more than 60 words, use verb phrases and present tense)
Notes (List additional information for the course)
Techniques to store, manage, and use data including databases, relational model,
This course cannot be taken for credit by students of the MS CS, MS ISA,
schemas, queries and transactions. On Line Transaction Processing, Data
MS SWE, MS IS, CS PhD or IT PhD programs.
Warehousing, star schema, On Line Analytical Processing. MOLAP, HOLAP, and
hybrid systems. Overview of Data Mining principles, models, supervised and
unsupervised learning, pattern finding. Massively parallel architectures and Hadoop.
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3
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Approval Signatures
Daniel Menasce
11/30/2012
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Syllabus
revised 11/8/11
CS504 Principles of Data Management and Mining
Description
An introductory course to the techniques to store, manage, and use data. Topics include: Databases, relational model, schemas, queries
and transactions. On Line Transaction Processing (OLTP), Data Warehousing, star schema, On Line Analytical Processing (OLAP),
differences with OLTP. MOLAP, HOLAP, and hybrid systems. Overview of Data Mining, principles, models, supervised and
unsupervised learning, pattern finding. Massively parallel architectures and Hadoop.
Texts
Chapters from several books, such as:
Teory, Lighstone, Nadeau, Jagadish: Database Modeling and Design
Gray and Reuter : Transaction Processing
Kimball and Ross: The Data Warehousing Toolkit
Larose: Data Mining Methods and Models
The selected chapters will be turned into a single text book for the course.
Instructor
Daniel Barbará
Computer Science Department
[email protected], (703) 993-1627
Grading
Homework 30%
Midterm Exam 30%
Final Exam 40%
Schedule
Week
Topic
1
Databases, ER modeling
2
Relational Model, schemas and queries
3
Principles of database languages: SQL examples
4
On-Line Transaction Processing principles, ACID properties, transaction protocols (2 and 3-phase commit)
5
Recovery. Stable Storage. Disaster Recovery principles.
6
Data Warehousing: OLAP and its differences with respect to OLTP, Star Schema
7
Molap, Rolap, and Hybrid systems
8
9
10
11
12
13
14
15
Midterm
Parallel databases
Data Mining: definitions, tasks, models
Supervised Modeling
Unsupervised Modeling: Clustering, Association rule mining
Massive dataset mining: Hadoop
Mahout and Hadoop
Exam