1. Syllabus CS765: Intro to Database Systems 3208 F07 william.perrizo@ndsu.edu course web site: http://www.cs.ndsu.nodak.edu/~perrizo/classes/#1course 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 william.perrizo@ndsu.edu 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. http://www.cs.ndsu.nodak.edu/~perrizo/classes/#1

2. 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.Possible Topics 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 william.perrizo@ndsu.edu) Papers will be judged on contribution, level of current research interest, depth, correctness, clarity, and insight.

3. COURSE Assignments: Course website: http://www.cs.ndsu.nodak.edu/~perrizo/classes/#1http://www.cs.ndsu.nodak.edu/~perrizo/classes/#1 Assignment 0 is due December 11 5PM (Text exercises): (30 points) Assignment 1 is due September 13 5PM (Age of infinite storage) (10 points) Assignment 2 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 (150 points) Grades will be based on a grade curve of your total points out of 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.

4. 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!

5. Term Paper topics chosen so far (continued on next slide) DateNameTitle 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 LimkeUser Interface Optimization using Data Mining Techniques Oct 16 Sunil MaddiA new method of K-Medoids Clustering and comparison to known methods. Oct 19 Rajani GarimediA Websites interactions analyzer and a study of strength of reference between websites Oct 23 Huma RizviAggregation 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 VanteruTabu-Search-Based Classification Implementation and Performance Analysis Nov 5 Farzana JahanExplore the Association of Phenotypic Traits with Seed Mineral Content using P-Tree Nov 6 Harika MallapathyAutomatic Alerter for Software Engineers Nov 6 Annaji GantiClassification applied to Software Engineering Aspects Nov 9 Sri Harsha Yamparala.Database or Data Mining in Software Engineering Nov 12 Anupama AnnapureddyResearch and Implementation of Federated Database Systems Nov 12 Kareem FazalSystem 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 SambarajuNew Deadlock Managment Method for Widely Distributed DBMS

6. Term Paper topics chosen so far DateNameTitle Nov 19 Samuel KondamarriAutomatic 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 DumpalaMarkov 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.

7. 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.