| CODE | ARI3216 | ||||||
| TITLE | Web Data Mining | ||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||
| MQF LEVEL | 6 | ||||||
| ECTS CREDITS | 5 | ||||||
| DEPARTMENT | Artificial Intelligence | ||||||
| DESCRIPTION | This study-unit will build on the theoretical concepts dealt with in the 2nd year study-unit on Web Intelligence, and also introduces elements of business aspects on how these concepts can be developed into commercial solutions. In the first part of the study-unit the students will be exposed to techniques through which the content and structure of such a huge network such as the Web can be mined and analyzed. Topics that will be covered include: - Web Crawling; - Structured Data Extraction; - Personalised Recommendation; - Big Data on the Web; - Knowledge Graphs. In the second part of the study-unit the students will be exposed to design thinking methods intended to inspire students to ideate bottom up solutions that address a particular Web related problem and in so doing pass on value to potential customers. Study-Unit Aims: This study-unit aims to build over the theory and techniques introduced in the Web Intelligence study unit and to present a broad range of methods and techniques that deal with the evolving, structure, content and semantics of the Web. This study-unit will also include a practical business aspect to expose students to problems solvable through web intelligence that 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 the Web graph; - Explain the techniques and fundamental principals central to big data technologies 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 and semantics; - Evaluate and apply specific big data techniques for 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 Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications). Bing Liu. 2006. Springer-Verlag, ISBN:3540378812. - Data-Intensive Text Processing with MapReduce, Jimmy Lin and Chris Dyer (Morgan and Claypool 2010). - 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. |
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| ADDITIONAL NOTES | Pre-requisite Study-unit: ICS2205 | ||||||
| STUDY-UNIT TYPE | Lecture, Independent Study & Tutorial | ||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Charlie Abela (Co-ord.) Joel Azzopardi Alexander Borg Johan Zammit |
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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 description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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