Facultatea de Științe Economice și Gestiunea Afacerilor
Str. Teodor Mihali nr. 58-60
Cluj-Napoca, RO-400951
Tel.: 0264-41.86.52-5
Fax: 0264-41.25.70
econ@econ.ubbcluj.ro
www.econ.ubbcluj.ro
DETAILED SYLLABUS
Big Data and Web Computing
1. Information about the study program
1.1 University
1.2 Faculty
1.3 Department
1.4 Field of study
1.5 Program level (bachelor or master)
Babeş-Bolyai University
Faculty of Economics and Business Administration
Business Information Systems
Business Information Systems
Master
1.6 Study program / Qualification
Business Modeling and Distributed Computing
2. Information about the subject
2.1 Subject title
Big Data and Web Computing
2.2 Course activities professor
Assoc. prof. Ioan Petri
2.3 Seminar activities professor
Assoc. prof. Ioan Petri
2.4 Year of study
I
2.5 Semester
II
2.6 Type of assessment Colloquium 2.7 Subject regime mandatory
3. Total estimated time (teaching hours per semester)
3.1 Number of hours per week
4 out of which: 3.2 course
2
3.3 seminar/laboratory
3.4 Total number of hours in the
56 out of which: 3.5 course
28
3.6 seminar/laboratory
curriculum
Time distribution
Study based on textbook, course support, references and notes
Additional documentation in the library, through specialized databases and field activities
Preparing seminars/laboratories, essays, portfolios and reports
Tutoring
Assessment (examinations)
Others activities ...................................
3.7 Total hours for individual study
119
3.8 Total hours per semester
175
3.9 Number of credits
7
2
28
Hours
35
35
35
10
4
4. Preconditions (if necessary)
4.1 Curriculum
4.2 Skills
Distributed systems
Moderate programming skills (java or other object oriented language)
5. Conditions (if necessary)
5.1. For course
development
5.2. For seminar /
laboratory development
Projector
Access to a large scale computing infrastructure
1
NOTE: This document represents an informal translation performed by the faculty.
6. Acquired specific competences

Professional
competences
Transversal
competences
•
•
•
•
•
The course should allow the student to understand, use, and build practical big data
analytics an management systems; acknowledge the role of different operators used;
 The course is intended to provide a basic understanding of the issues and problems
involved in massive on-line repository systems, a knowledge of currently practical
techniques for satisfying the needs of such a system Implement heuristics adapted for
specific problems
 Indication of the current research approaches that are likely to provide a basis for
tomorrow's solutions
Analogies with existing technologies and their implications in the societal developments
Determine the impact of big data and web computing.
Identify the economic implications of big data
Associate the big data and web computing with social networks
Identify the major researching topics in the field of big data analysis
7. Subject objectives (arising from the acquired specific competences)
7.1 Subject’s general objective
7.2 Specific objectives
Building on the concept of distributed computing and understand the
repercussions of large scale computing infrastructures in terms of processing,
storage and analysis. Testing the various analytics algorithms and determine
the benefits such as recommendation, classifications, etc.
Adapting the knowledge to modern computing technologies and identifying
the economical options and business models of big data. Presenting a more
advanced perspective that sits beyond the technological implications of
modern technologies such as social influence, web collaboration and data
analytics.
8. Contents
8.1 Course
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
Introduction to Big Data and Web Computing
Technologies & Techniques for Big Data and Web Computing
Recommendation algorithms
Clustering algorithms
Classification algorithms
Graph computing: Graph Theory and Groups
Graph analytics in Social Networks
Social Computing and networks analytics
Mobile Data Collection, Analysis, and Interface
Web and Future Internet
Business Models for Big Data
Advanced Big Data Analytics
Teaching methods
Lectures/examples
Lectures/examples
Lectures/examples
Lectures/examples
Lectures/examples
Lectures/examples
Lectures/examples
Lectures/examples
Lectures/examples
Lectures/examples
Lectures/examples
Lectures/examples
Observations
1 lecture
1 lecture
1 lecture
1 lecture
1 lecture
2 lectures
1 lecture
2 lecture
1 lecture
1 lecture
1 lecture
1 lecture
2
NOTE: This document represents an informal translation performed by the faculty.
References:
1. Jimmy Lin and Chris Dyer, Data-Intensive Text Processing with MapReduce, Morgan & Claypool Publishers,
2010. http://lintool.github.com/MapReduceAlgorithms/ [Mandatory]
2. Graph Theory and Complex Networks” by Maarten Van Steen, 2010. [Mandatory]
3. Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Introduction to Data Mining, Addison-Wesley April 2005.
[Mandatory]
4. Anand Rajaraman and Jeff Ullman, Mining of Massive Datasets, Cambridge Press,
http://infolab.stanford.edu/~ullman/mmds/book.pdf [Mandatory]
5. Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, The Morgan Kaufmann Series in
Data Management Systems, Jim Gray, Series Editor Morgan Kaufmann Publishers, August 2000. 550 pages.
ISBN 1-55860-489-8. [Optional]
6. Social Network Analysis for Startups: Finding connections on the social web” by Maksim Tsvetovat and
Alexander Kouznetsov, O’Reilly Media, 2007. [Optional]
8.2 Seminar/laboratory
Teaching methods
Observations
Installation of the developing environments –Hadoop and
Examples/exercices
2 laboratories
PeerSim
2. Simulation of the P2P community
Examples/exercices
1 laboratory
3. Testing different network topologies and data analysis
Examples/exercices
2 laboratories
4. Algorithms implementations
Examples/exercices
3 laboratories
5. Applying graph analytics
Examples/exercices
2 laboratories
6. Hubs, Centrality, Connectivity
Examples/exercices
2 laboratories
7. Analysis APIs
Examples/exercices
2 laboratories
8. Costs management for big data
Examples/exercices
1 laboratory
References:
1. Tian Zhang, Raghu Ramakrishnan, Miron Livny, BIRCH: A New Data Clustering Algorithm
and Its Applications, Data Mining and Knowledge Discovery, Volume 1, Issue 2, 1997, 141182.
2. Indranil Palit and Chandan K. Reddy, "Scalable and Parallel Boosting with MapReduce", IEEE
Transactions on Knowledge and Data Engineering (TKDE), Vol.24, No.10, pp.1904-1916,
October 2012. Yang, X.S., Nature Inspired Meta-heuristic Algorithms, Luniver Press, 2010.
3. Trevor Hastie, Robert Tibshirani, Jerome. H. Friedman. The elements of statistical learning: data
mining, inference and prediction. Springer, 2009
4. T. L. Griffiths and M. Steyvers. Finding scientific topics. In Proceedings of the National
Academy of Sciences, 101, 5228-5235, 2004
1.
9. Corroboration / validation of the subject’s content in relation to the expectations coming from
representatives of the epistemic community, of the professional associations and of the representative
employers in the program’s field.
In many areas and domains, data are generated at a phenomenal speed that we have never experienced before.
Given the large amount of data, one fundamental scientific challenge is how to develop efficient and effective
computational tools to analyze the data, revealing insight and make predictions. Data analytics is the science of
achieving these goals.
10. Assessment (examination)
Type of activity
10.1 Assessment criteria
10.4 Course
Understand big data and distinguish the
various analysis algorithms
10.2 Assessment methods

Written exam
10.5
Setting up the testing environments for data Group project containing:
Seminar/laboratory collection and implementing the analytics
 Environment
algorithms
configuration
 System design and
network architecture
 Algorithms
implementations
10.3 Weight in
the final grade
0.4
0.6
3
NOTE: This document represents an informal translation performed by the faculty.
10.6 Minimum performance standard
• Students must demonstrate involvement and interest both in the lecturing activity and to laboratory exercises.
• A minimum of grade 5 on both assessment methods is required
• Students must comply with the project requirements
Date of filling
26 ianuarie 2015
Signature of the course professor
Assoc. prof. Ioan Petri
Date of approval by the department
28 ianuarie 2015
Signature of the seminar professor
Assoc. prof. Ioan Petri
Head of department’s signature
prof.univ.dr. Gheorghe Cosmin Silaghi
4
NOTE: This document represents an informal translation performed by the faculty.