Business intelligence and big data in e-business

Theoretical classes

Business processes in e-business, the role of business intelligence and big data analytics.
Characteristics of business intelligence in B2B and B2C e-business. Business intelligence system
architecture. Cloud infrastructure for business intelligence. Data warehouses in electronic
business. Nonrelational databases. Data modeling for nonrelational databases. ETL processes.
Big data infrastructure and services in e-business. Reporting systems and key performance
indicators. Methods and algorithms for knowledge discovery in data. Interactive reporting and
real-time analytics. Data visualization and knowledge graphs. An overview of open source and
commercial solutions for business intelligence and big data analytics. Application of business
intelligence and big data analytics concepts to solve problems in e-commerce, e-marketing, e-
government, e-education, e-health, mobile business, smart grid management, supply chain
management and other areas of e-business. Business intelligence and big data analytics in
systems based on the Internet of intelligent devices and virtual reality. Integration of big data
analytics and artificial intelligence. Machine learning and big data analytics. Integration of
business intelligence and big data analytics into complex e-business systems.

Practical teaching

Solving typical problems of data manipulation in e-business: parallel sorting, search, analysis of
links on the Internet, analysis of log files, personalized Internet advertising, analysis of emails.
Analysis of unstructured data in e-business applications: pattern detection, online market
segmentation, analysis of consumer behavior, implementation of real-time referral systems.
Data visualization. Apache Hadoop ecosystem for big data analytics. Importing data into
Hadoop. Ad hoc queries using the Hive tool. Execute queries using HiveQL. Real-time queries
using the Impala tool. Advanced data analysis using the Mahout tool. Python ecosystem for big
data analytics: NumPy, SciPy, Pandas, Scikits, Matplotlib. Apache Spark and real-time analytics.

Development of machine learning-based services, TensorFlow. Design of e-business services
based on big data analytics and their integration into the e-business ecosystem.

The aim of the course is to acquaint students with modern concepts, methods and techniques
of business intelligence and big data analytics and training for the application of acquired
knowledge in e-business systems.

Students are trained to design, implement and use business intelligence systems and big data
analytics in various e-business contexts.

1. B. Radenković, M. Despotović-Zrakić, Z. Bogdanović, D. Barać, A. Labus, Electronic business,
ISBN 978-86-7680-304-0; Faculty of Organizational Sciences, Belgrade, 2015
2. Lukić, J., Radenković, M., Despotović-Zrakić, M., Labus, A., & Bogdanović, Z. (2017). Supply
chain intelligence for electricity markets: A smart grid perspective. Information Systems
Frontiers, 19 (1), pp. 91-107, ISSN: 1387-3326.
3. Milovanović, S., Bogdanović, Z., Labus, A., Barać, D., & Despotović-Zrakić, M. (2019). An
approach to identify user preferences based on social network analysis. Future Generation
Computer Systems, 93, 121-129, ISSN 0167-739X,
https://doi.org/10.1016/j.future.2018.10.028.
4. J. Šuh, V. Vujin, D. Barać, Z. Bogdanović, B. Radenković, Designing Cloud Infrastructure for Big
Data in E-Government, Journal for Universal Excellence, vol. 4, no. 1, pp. A26-A38, Faculty of
Organizational Studies in Novo Mesto, Slovenia, 2015.
5. N.Stefanovic, B.Radenkovic, D.Stefanovic, Designing OLAP Multidimensional Systems For
Supply Chain Management, International Journal of Pure and Applied Mathematics, IJPAM, ISSN
1311-8080, 2007.
6. Kleppmann, M. (2017). Designing Data-Intensive Applications: The Big Ideas Behind Reliable,
Scalable, and Maintainable Systems, O'Reilly Media; 1 edition, ISBN-10: 1449373321.
7. Malaska, T. & Seidman, J. (2018). Foundations for Architecting Data Solutions: Managing
Successful Data Projects, O'Reilly Media; 1 edition, ISBN-10: 1492038741.

8.Bengfort, B. & Kim, J. (2016). Data Analytics with Hadoop: An Introduction for Data Scientists,
O'Reilly Media; 1 edition ASIN: B01GGQKXO4.
9. Materials in e-form, from the e-learning portal moodle.elab.fon.bg.ac.rs