IS 633: Database Management Systems (3 credits)
The course covers most of the major advancements in database technology that have taken place recently. It does not assume any prior background in the field of databases, and hence starts with basic introductory concepts along with more advanced topics.
The course will cover both conceptual and hands-on material in the area of database management, thus enabling student to have the maximum amount of comprehension and retention of the material covered in the course.
Pre-requisite: IS 607.
- What is a DBMS and why should it be used
· Compare file systems with DBMS
· Describe the levels of abstractions and data independence
· Describe transaction management
- Given a set of user requirements design and implement a prototype relational database.
· Construct a high-level conceptual model, given an organization’s data requirements.
· Construct a normalized relational model from a conceptual model.
· Implement a relational database by creating table definitions, constraints, loading data using Oracle, version 8, database management system.
- Manipulate data correctly using SQL.
· Write SPJ queries, sub-queries, use aggregate functions, group data using SQL
- Design and implement a prototype web-based front-end with an ODBC compliant database as the back-end.
- Describe Internet databases and how they work
· Describe the architecture of Application servers and Server-side Java
· Describe how XML works
· Describe the implementation of semi-structured data
- Design a data warehouse (cube, bitmap/join indexes, summary tables) given user requirements.
· Create a multi-dimensional data model given data requirements for an example scenario.
· Distinguish between ROLAP and MOLAP.
· Describe the architecture of a data warehouse.
· Describe and perform the following operations for a given dataset: roll-up, drill-down, pivot, slice, and dice.
- Design and apply data mining tools from user requirements.
· For a give dataset, minconf, and minsup, develop association rules.
· Explain sequential patterns, time series patterns, classification rules, segmentation, and clustering of data.