University of Malta
 

Study-Unit Description
UOM Main Page
 
 
 
Apply - Admissions 2016
Newspoint
Campus Map button
Facebook
Twitter


CODE ICS3216

 
TITLE Advanced Web Intelligence

 
LEVEL 03 - Years 2, 3, 4 in Modular Undergraduate Course

 
ECTS CREDITS 5

 
DEPARTMENT Artificial Intelligence

 
DESCRIPTION This study-unit will build over the theoretical and mathematical concepts dealt with in the 2nd year course on Web Intelligence (ICS2205), and also introduce elements of business aspects on how these concepts can be developed into commercial solutions.

Analyzing such a huge network such as the Web requires mining techniques that are capable of analyzing both the content, the structure and usage of such a huge graph.

The increase in popularity of social networks and online web communities and user-generated web content present interesting opportunities for the application of different analysis techniques. Such opportunities can also possess business potential.

Furthermore, initiatives such as the Semantic Web and linked data, are complimenting the Web with meta-content and ontologies that are intended to be exploited by intelligent systems to provide for more personalizedsolutions.

Study-unit Aims:

This study-unit aims to build over the theory and techniques introduced in the Web Intelligence course and to present a broad range of methods and techniques that deal with the evolving, structure, content, usage, semantics and social aspects of the Web.

This study-unit will also include a practical business aspect by teaching students on how certain problems solvable through web intelligence can be converted into business opportunities.

Learning Outcomes:

1. Knowledge & Understanding:

By the end of the study-unit the student will be able to:
- Describe the principals underpinning graph and link analysis in the context of the Web graph;
- Explain the techniques and fundamental principals central to social network analysis and user generated content;
- Explain the techniques and fundamental principals behind semantic web and linked data technologies;
- Identify problems and solutions related to web intelligence that make business sense.

2. Skills:

By the end of the study-unit the student will be able to:
- Evaluate and apply specific methods and techniques to analyze the Web's content,structure, usage, semantics and social aspects;
- Evaluate and apply specific analysis techniques within the development of intelligent web applications;
- Develop a “good” idea for a problem solution into a possible business venture.

Main Text/s and any supplementary readings:

Main Texts:

- Web Intelligence, Ning Zhong, Jiming Liu and Yiyu Yao (Eds), Springer Monograph, (2003). ISBN 978-3-540-44384-1.
- Foundations of Semantic Web Technologies, Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph, Chapman & Hall/CRC, 2009. ISBN: 9781420090505.
- Networks: An Introduction, Mark Newman, Oxford University Press, May 2010. ISBN-10: 0199206651.

Supplementary Readings:

- A Semantic Web Primer, Grigoris Antoniou, Frank van van Harmelen, MIT Press, 2nd Edition, March 2008. ISBN-10: 0262012421.
- Social Network Data Analytics (Springer) Ed. Charu Aggarwal, March 2011. ISBN -10: 1441984615.

 
RULES/CONDITIONS Before TAKING THIS STUDY-UNIT YOU MUST TAKE ICS2205

 
STUDY-UNIT TYPE Lecture, Independent Study & Tutorial

 
METHOD OF ASSESSMENT
Assessment Component/s Resit Availability Weighting
Project Yes 100%

 
LECTURER/S

 
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the study-unit description above applies to the academic year 2017/8, if study-unit is available during this academic year, and may be subject to change in subsequent years.
Calendar
Notices
Study-unit Registration Forms 2017/8

Register

For Undergraduate (Day) and Postgraduate students.

 

Academic Advisors 2017/8

AA1

Academic Advisors for ICT 1st year students (Intake 2017/8), NOW available

Faculty of ICT Timetables

Timetables

ICT Timetables are available from Here.

Health and Safety Regulations for Labs Form

The Faculty of ICT Health and Safety Regulations for Laboratories form can be found here

 HealthAndSafety

 
 

Log In back to UoM Homepage