Courses to be offered

Distributed Databases

Pre-requisite:

  • Computer Networking, Basic DBMS

Post Condition:

  • Understanding of concepts and issues in distributed database systems
  • Ability to design and develop distributed database systems and applications
  • Ability to provide reasonable solutions to problems in distributed database management and in-depth understanding of the trade-offs associated with different solutions

Course Description:

This course covers important concepts of distributed database systems. In particular, we focus on architectures, algorithms and techniques in the design of high-performance distributed database systems. The course also includes an overview of some emerging distributed database technologies such as mobile databases and peer-to-peer databases, thereby providing a foundation for students interested in specializing in these emerging areas.

Course Contents:

  • Distributed database design and architecture
  • Distributed indexing of data
  • Query processing and optimization in distributed databases
  • Concurrency control and reliability
  • Data replication and load-balancing in LAN and WAN environments
  • Federated and heterogeneous data management
  • Data Security issues in Distributed databases
  • Overview of emerging data management technologies such as peer-to-peer and mobile databases.

Data Mining

Pre-requisite:

  • DBMS course, algorithms course
  • Familiarity with C/C++/JAVA as well as SQL
  • Prior course-work in statistics (this will be judged on a case-by-case basis by the instructor)

Post Condition:

  • Knowledge of data mining process, techniques, algorithms and applications
  • Ability to apply data mining tools and techniques for solving real-world problems in interdisciplinary domains
  • An overview of business intelligence

Course Description:

The course covers some of the widely used data mining techniques, algorithms and applications. The course starts by refreshing basic statistics and database fundamentals pertaining to data mining. Each phase in the data mining process such as data exploration, data preparation (data cleaning, transformation and standardization), model building, evaluation and deployment is covered. Algorithms and applications of common data mining tasks like association rule mining, classification and data clustering are covered. Finally, the course provides an overview of business intelligence, the aim being to provide a foundation for students interested in specializing in business intelligence.

Advanced topics in data management (designed for post-graduate students)

Pre-requisite:

  • Bachelors degree in Computer Science or equivalent

Post Condition:

  • an in-depth understanding of some of the advanced topics in data management
  • the ability to identify various solutions to existing as well as emerging data management problems and also the trade-offs pertaining to these solutions
  • the ability to solve real-world data management problems in an efficient manner
  • the ability to read research papers on data management (and possibly other research domains) critically and also survey a research area in an efficient manner
  • the ability to give state-of-the-art presentations/seminars on research topics

Course Description:

The aim of this course is to equip the students with comprehensive knowledge about advanced topics in data management. In the first part of this course, we will focus on database indexing, starting with the indexing of one-dimensional data, and then progressing to the indexing of spatial and multi-dimensional data. Then we will consider complex database queries such as nearest-neighbour (NN) queries, multi-way spatial joins and skyline queries. Next, we will examine distributed data management both for LAN environments (clusters) as well as for emerging WAN environments such as P2P and GRID.

In the second part of this course, we will see how to identify trends in the data using data mining and data clustering techniques. Next, we will consider data management in mobile environments. Finally, we will focus on understanding real-world data management issues in different domains such as P2P, mobile and the Internet. During the last lecture, some future research directions in data management will be provided.

Course Contents:

  • Indexing of complex data (including spatial and multi-dimensional)
  • Complex database queries: NN, Multi-way spatial joins and skyline queries
  • Distributed data management for LANs
  • Distributed data management for WANs (P2P and GRID)
  • Data mining and data clustering
  • Data management in emerging domains such as P2P and mobile environments
  • Real-world data management issues in different relevant and emerging domains

Information Retrieval

Pre-requisite:

  • Advanced Programming, Data Structures, Algorithms, Database Management Systems (DBMS)

Post Condition:

  • Knowledge of Information Retrieval  concepts, algorithms and applications
  • Knowledge of Search Engine architecture and underlying components
  • Ability to apply tools and techniques from Information Retrieval such as data crawling, indexing and ranking for solving real-world problems in interdisciplinary domains
  • An overview of text & document processing tools & techniques

Course Description:

This course will teach basic concepts, tools & techniques in the field of Information Retrieval (IR) & Search. It will cover theoretical foundations, implementation aspects, issues and state-of-the-art in the area of information retrieval, representation, organization, indexing and categorization. The course will cover topics such as retrieval models, inverted index construction, performance evaluation in information retrieval, search engine architecture, crawling, indexing, ranking, text categorization & clustering In the end, trends and research issues will be discussed.