CIS 452/552, Database Issues

CIS 452/552, Winter 2017

Database Issues (Big Data)

[ Assignments | CIS | News]

Course Description: This course will introduce the state-of-arts database issues with the focus on how to utilize them in processing (managing and analyzing) big datasets. Big data becomes a well-used term due to collections of real life data in both research and industry are so large and so complex that it is difficult to process them using traditional database and data analysis tools. The challenges include capture, curation, storage, search, sharing, transfer, integration, analysis, and visualization. In this class, we will focus on parallel databases, distributed databases, data integration, data warehousing, NoSQL, MapReduce (Hadoop), and data mining.
Time and Place: Wednesdays and Fridays at 2:00pm - 3:20pm in 191 Anstett Hall.
Instructor: Dejing Dou, 303 Deschutes, phone 6-4572, email dou@cs.uoregon.edu.
Office hours:
- Dejing Mondays 4-5pm or by appointment (I like to talk with students).
Directory: The (unix) directory for the class is /cs/classes/www/17W/cis452.
News: Keep an eye on the course news for miscellaneous announcements.
Evaluation:
- The homework will be a combination of written and programming work.
- The Course Project will be due in parts: Part 1 (proposal) is due Friday Feb 24 and Part 2 (final report) on Friday Mar 24. Project presentation will be hold on Tuesday Mar 21. We assume that you will work on it individually. If you wish to work in a group, please check with the instructor.
The approximate weighting of the work done will be

Homework Assignments 40%

Midterm (March 10) 30%

Course Project 30%
Texts:
- (Required) Silberschatz, Korth and Sudarchan Database System Concepts, 6th Edition, McGraw-Hill, 2010. (text website)
References:
- Ramakrishnan and Gehrke Database Management Systems, 3rd Edition, McGraw-Hill, 2003. (text website)
- Elmasri/Navathe, Fundamentals of Database Systems (Third Edition), Addison-Wesley, 2000.
- Sadalage (blog), Fowler (blog) NoSQL Distilled, Addison-Wesley, 2013
- Han, Kamber, and Pei Data Mining: Concepts and Techniques, Third Edition, Morgan Kaufmann, 2011 (see text website.)

Approximate Syllabus and Lecture Slides

Week of	Chapter	Description and Slides	Events and Readings

Jan 9		intro, big data management and analysis
Jan 16	18	parallel databases
Jan 23	19	distributed databases
Jan 30	19	heterogeneous DBs, data integration
Feb 6	20, Han et al.	data warehousing, OLAP
Feb 13	Sadalage & Fowler	NoSQL, MapReduce
Feb 20	Sadalage & Fowler	MapReduce (Hadoop), data mining	Course Project Proposal: Friday, February 24
Feb 27	20, Han et al.	data mining, midterm review (Friday)
March 6		no lecture (Wednesday), midterm (Friday)	Midterm: Friday, March 10
Mar 13		project implementation, no lecture
Mar 20		finals week;	Project Presentation, Tuesday, 14:45pm - 16:45pm, Mar 21; Project Report Friday, Mar 24

Late policy: All assignments are due by 11:59pm of the stated due date, though it is much safer to turn them in at class time. Late papers are subject to a 10% per day penalty. Late assignments will not be accepted once the graded papers are returned or once sample solutions are handed out. It is your responsibility to turn in the assignments on time. Make sure that electronically submitted assignments are actually delivered.
Course resources (to be updated):
- Text Website
- Big data repository

Homework Assignments	40%
Midterm (March 10)	30%
Course Project	30%