Transcript Query Processing, Resource Management and Approximate in a

Syllabus
CS765: Intro to Database Systems 3208 F07
[email protected] course web site: http://www.cs.ndsu.nodak.edu/~perrizo/classes/#1
Text Database Management Systems Ramakrishnan/Gehrke, 3rd edition.
Office Hours:
T-Th 2-3:15, in IACC A1
(others by appointment)
Please use email for questions that can be emailed. If you have a question that cannot
be adequately stated or answered by email, please use the office hours. But please
do not come in to office hours if you have a cold or flu or another infection (until it
is non-infectuous). Thank you for your cooperation on this matter.
All assignments and your term paper must be SUBMITED THROUGH
BLACKBOARD. (DO NOT email to [email protected] as previously
instructed). All records will be kept on the Blackboard system and will be
available to you from there.
When submitting your assignments and term paper through BLACKBOARD, please
identify your work by using your first_name.last_name just as it appears in your
NDSU email address (e.g., william.perrizo).
Section notes and Section assignment descriptions are available on the website,
http://www.cs.ndsu.nodak.edu/~perrizo/classes/#1, and also from the BLACKBOARD system. Other
additional materials are available on the website also.
COURSE DESCRIPTION
Topics: Intro. to DBMSs, Data Sets, DataMining, Retrieval, Relational Data Structures,
Transaction Processing, Recovery, Distributed DBMS, Querying, Normalization, Security.
COURSE OBJECTIVES: Understand the fundamentals of database systems. Gain
experience in database research and in the written reporting of it.
TERM PAPER (150 points): Each student will pick a topic (some example topics and
topic areas in html are at Possible Topics and in powerpoint at Possible Topics ) or
your own RESEARCH topic - but must be a new RESEARCH idea of yours, NOT
A PAPER written by someone else). Included in the Possible Topics files is a
complete set of guidelines on what to include in your paper and what format to use.
Note that the guidelines are also available from the Blackboard system.
Research the topic, write a quality (publishable in archival media?) paper.
Topics will to be approved 1st-Come-1st-Serve (email the title and abstract to
[email protected])
Papers will be judged on contribution, level of current research interest, depth,
correctness, clarity, and insight.
COURSE Assignments:
Assignment 0
is due
December
Assignment 1
is due
Assignment 2
Course website: http://www.cs.ndsu.nodak.edu/~perrizo/classes/#1
11
5PM (Text exercises):
(30 points)
September 13
5PM (Age of infinite storage)
(10 points)
is due
September 20
5PM (Horizontal data)
(10 points)
Assignment 3
is due
September 27
5PM (Vertical data)
(10 points)
Assignment 4
is due
October
4
5PM (Relational)
(10 points)
Assignment 5
is due
October
11
5PM (Disks, pages, buffers)
(10 points)
Assignment 6
is due
October
18
5PM (Files)
(10 points)
Assignment 7
is due
October
25
5PM (Indexes)
(10 points)
Assignment 8
is due
November
1
5PM (Transactions)
(10 points)
Assignment 9
is due
November
8
5PM (Query Processing)
(10 points)
Assignment 10 is due
November
15
5PM (Data Mining)
(10 points)
Assignment 11 is due
November
29
5PM (Normalization)
(10 points)
Assignment 12 is due
December
6
5PM (Recovery)
(10 points)
The Term Paper is due December
11
5PM
Grades will be based on a grade curve of your total points out of
(150 points)
300 points
On all assignments, you must work alone. Please do not share your work with anyone or
be shared with by anyone else. Submit assignments and paper through BLACKBOARD.
COURSE DESCRIPTION continued
REQUIRED MATERIALS: The text, email, WWW access are required.
STUDENTS NEEDING SPECIAL ACCOMMODATIONS or who have special
needs are invited to share that information with the instructor.
PREREQUISITES: CS366 or equiv. Student must be able to read and follow
technical, detailed instructions and adapt solutions.
ACADEMIC HONESTY: Work must be completed in a manner consistent with
NDSU Senate Policy 335: Code of Academic Responsibility and Conduct.
The goals of this course include to initiate graduate student's into data and
database systems research and to enhance graduate student's written
presentation skills of their research.
Additional reference material on all topics in this course can be found on the web by
doing a Google (or Yahoo or Ask) search on the appropriate keyword(s) and also
by using the NDSU library.
Good luck in your 765 course!
Term Paper topics chosen so far (continued on next slide)
Date
Name
Title
Aug 29 Arijit.Chatterjee
Business Intelligence Classification Related to the Netflix Contest (abstract to follow)
Sep 02 Noah Addy
Automatic Alerter for Software Development Shop Coding Rule Violations
Sep 03 Vasanth Narayanan
Link Analysis in Wikipedia (automation of linking?)
Oct 01 Kavita Khanchandani
Interaction analyzer between software applications
Oct 08 Sandeep Raavi
A Specific Alerter for highly risky situations in a code database.
Oct 11 Dibakar Bhowmick
Vertical Database of Music and Musical instrumental notes analysis based on P-trees.
Oct 12 Rajeev Sachdev
Genetic Algorithm and data mining
Oct 16 Jed Limke
User Interface Optimization using Data Mining Techniques
Oct 16 Sunil Maddi
A new method of K-Medoids Clustering and comparison to known methods.
Oct 19 Rajani Garimedi
A Websites interactions analyzer and a study of strength of reference between websites
Oct 23 Huma Rizvi
Aggregation and Querying Model for Heterogeneous Wireless Sensor Networks
Oct 24 Szymon Woznica
New wireless sensors data-based web portal for real-time monitoring of sensorial state
Oct 30 Manu Bhogadi
Some new aspect of Sales Analysis.
Oct 30 Omar El Ariss
A comparison of K-means vector quantization and the LBG algorithm, or the splitting technique.
Oct 30 Loai Alnimeer
Speaker Recognition using histogram techniques.
Oct 31 Suresh Paturu
R-Trees: A variation on the basic R-Tree index structure
Nov 1 Mridula Sarker
An effort to increase flexibility of kNN classification with the use of Genetic Algorithm
Nov 2 Siva Vanteru
Tabu-Search-Based Classification Implementation and Performance Analysis
Nov 5 Farzana Jahan
Explore the Association of Phenotypic Traits with Seed Mineral Content using P-Tree
Nov 6 Harika Mallapathy
Automatic Alerter for Software Engineers
Nov 6 Annaji Ganti
Classification applied to Software Engineering Aspects
Nov 9 Sri Harsha Yamparala.Database or Data Mining in Software Engineering
Nov 12 Anupama Annapureddy
Research and Implementation of Federated Database Systems
Nov 12 Kareem Fazal
System Design Issues in Sensor Network
Nov 15 Mohamed Rahman
Wikipedia: Analyze the link structure (but not automation of its link structures)
Nov 16 Pavan K Bapanpally
Hierarchical clustering similar to BIRCH
Nov 16 Hari Mukka
Association Rule Mining Implementation and Performance Analysis
Nov 19 Srikanth Goud Aakula
Multilevel Association Rule Mining Implementation and Performance Analysis
Nov 10 Sharath Sambaraju
New Deadlock Managment Method for Widely Distributed DBMS
Term Paper topics chosen so far
Date
Name
Title
Nov 19 Samuel Kondamarri
Automatic Alerter in Software Engineers
Nov 20 Venkat NMK Raidu
New Distributed datamining algorithm and comparision of the same to the existing algorithms.
Nov 20 Syed Safi
Datamining for hospital/clinical based medical records methodologies
Nov 26 Ashok Vellaswamychelaiahrothimasw Performance Tuning - Automated Indexing for tables
Nov 27 Jianfei Wu
Segmentation of Fingerprint Image Based On Automatic-Parameter Normalization
Nov 28 Aaron Marback
Fingerprint (or more general biometric) analysis and processing using partitioned hashing
Nov 28 Jeremy Roseen
Data Visualization: improving query results through visual cues and user feedback
Nov 28 Mohamed Rahman
Sales Analysis: Analysing sales of Computers in big MNC companies like DELL,IBM,Microsoft..etc
Nov 28 Anita Sundaram
Annotation of multimedia video sequences using data mining tools
Nov 30 Chaitanya Dumpala
Markov Modelling based classification and performance Analysis as my final paper.
Dec 6 Alex Radermacher
Optimizing database design to improve performance on commonly performed tasks
Dec 6 Pradeep Amaran
Security Applications
Dec 8 Shivendushital Pandey
Data mining techniques in Wireless Sensor Networks.
What is GRADUATE SCHOOL?
GRADUATE SCHOOL, COLLEGE, TECHNICAL/PROF. SCHOOL RELATIONSHIP in a UNIVERSITY
Universities, by definition, integrate research, teaching and service.
The Graduate school at a University has the primary responsibility for research.
A College has the primary responsibility for teaching.
A Vocational, Technical and Professional School has primary responsibility for training in the use of specific existing tools
of a trade, area or profession.
This is a Graduate School course and will focus on research.
Even though 765 may be in your first graduate course, you have already been doing research for a long time, so it won't be
entirely new to you.
What is RESEARCH?
Research is just another word for active learning.
There is really very little difference between active learning and research,
sometimes with the slight difference that, early on, most concepts that you research have been pre-researched by
others, while, later on, most concepts that you research have not been pre-research by others.
In both cases, the student masters context, background and language of the area, and developes new or improved solutions to
questions and problems.
A good researcher takes the point of view:
There's almost always a better way to do anything.
A good researcher questions the prevailing methods and challenge the current practices in an attempt to find a better way. I
like to call it finding a new, killer idea and then taking the responsibility to prove that it is killer.