COT 6930 - Web 2.0 Architectures and Algorithms - Spring 2015
Online and Live Course Sections
Course Syllabus: Web 2.0 architectures, models and patterns; and algorithms for searching, ranking, recommending,
social networking, clustering, and classification.
Text books: 1. Mining the Social Web, Matthew A Russel, O'Reilly, 2nd edition, 2013, 978-1-449-36761-9; 2.
Governor, J., Hinchcliffe, D., and Nickull, D., Web 2.0 Architectures, O’Reilly, 2009
Pre-requisite: Graduate student status in computer science, computer engineering, or ITOM; Consent of Instructor
for graduate majors in other fields; Programming background in Python. Python is easy to learn. A good book on
Python: McKinney, W., Python for Data Analytics, O’Reilly, 2013
Instructor: Ravi Shankar, Professor CEECS; (561) 297-3470, shankar@fau.edu; Office Hours: MWF 3 to 5 PM
Course Time and Place: MWF 2 to 2.50 PM, FL 401, Online and Live sections
Course Description: Web 2.0, or Social Web, is architecture of user participation, and goes beyond the page
metaphor of Web 1.0 to deliver rich user experiences (Tim O’Reilly, 2005). The course will discuss the architectural
models and patterns, and algorithms that make today’s web experience so unique from this social networking
perspective. The course is not about web-enabling technologies such as JSON, XUL and RESTful. The algorithms
discussed are optimized for social networking issues such as information retrieval across various formats, indexing,
link analysis, ranking, user click analysis, natural language processing, recommending, discovering user groups,
link-based clustering, and rule-based data classification. Practical examples of social web mining at Twitter,
Facebook, LinkedIn, Google+, and Github will be covered. Use of semantic web to enhance the experience will be
explored.
Grading:
 Five Assignments (in Python) with data at key social web sites: 40% - during the semester
 Mid-term and Final Exam 30% - during the 7th and final week
 Research Paper: 20% - during the second half of the semester; due at the end of the semester
 Web 2.0 community service (review advances in architecture, standard, algorithm, , etc.) 10% - anytime
Topics to cover: Number of 50 minute lectures in parentheses.
1. Web 2.0 Architecture - Intro, new client-server model, reference architecture, and patterns (4)
2. Web profile - Data and Meta data; Distributed, Large, Sparse, and Dynamic databases; and Social Linkages.
Embedding intelligent Web 2.0 behavior (4)
3. Trending with Frequency Analysis: An overview example with Twitter (4)
4. Social Graphs, Meta Tags, and Click Analysis: Example of Facebook (4)
5. Clustering Algorithms - Identification of user groups with traditional, clustering, link-based, and K-means
algorithms. Example of LinkedIn(4)
6. Searching Algorithms: Information retrieval, indexing and ranking; Precision and recall. Example of
Google+ (4)
7. Natural Language Processing - Web crawling with link analysis and Discovering Semantics - Examples on
WWW (4)
8. Algorithms for suggestions and recommendations: collaborative filtering and content-based recommendation.
Example of Github (4)
9. Time Series Trends and Analysis - Example of mailbox mining (4)
10. Classification Algorithms - structural, statistical and rule-based classifiers; life cycle of a classifier (4)
11. Research Issues – Performance and computing architectures, Semantic Web, Socio-Technical Systems,
Natural Language Processing, Visualization, and Domain specific constraints (4)
References:
 Marmanis, H., and Babenko, D., Algorithms of the Intelligent Web, Manning, 2009;
 O’Reilly, T., What is Web 2.0 - Design Patterns and Business Models for the Next Generation of Software,
http://oreilly.com/web2/archive/what-is-web-20.html, 2006
 McKinney, W., Python for Data Analytics, O’Reilly, 2013
 Contemporary topics in Web 2.0, senior student presentations, http://csi.fau.edu/courses/senior-seminar/,
2013
 McCandless, M., Hatcher, E., and Gospodnetic, O., Lucene in Action, Manning, 2nd edition, 2010
 Visualization and Analytics, special issues, IEEE Computer Magazine, May and July 2013
 Socio-Technical Systems Engineering Handbook, http://archive.cs.st-andrews.ac.uk/STSEHandbook/index.html , accessed in 2013.
 Medical domain infrastructure: http://www.nlm.nih.gov/research/umls/