2023-2024 / INFO9003-1

Advanced Topics in Digital Business


30h Th

Number of credits

 Master in business engineering (120 ECTS)5 crédits 
 Extra courses intended for exchange students (Erasmus, ...)5 crédits 


Ashwin Ittoo

Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester


Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents


The aim of this course is to introduce advanced, novel technologies and the state of the art in related research to masters students. These technologies and research are poised to play an increasingly important role in the digital age.

The main themes will revolve around:

  • Blockchains & Smartcontracts
  • Distributed systems & NoSQL
  • Big data computing
  • Ethics, Regulations and Legal Aspects 
The course will comprise a practical component to enable the participants develop, implement and experiment with these advanced technologies and algorithms from research.

The topics to be covered are listed below.

Necessary support and guidance (incl. consultation will be provided).



Distributed Systems

  • Overview of distributed systems (master-slave architectures)
  • Distributed databases vs. Relational databases

Distributed Computing for Big Data

  • MapReduce
  • Introduction to Apache Spark
  • Practical with Apache Spark &Python


  • Motivation for NoSQL
  • Comparison with SQL type databases
  • Replication & sharing strategies
  • Introduction to NoSQL databases (column-family, XML-based, ...-
  • MongoDB (including practical)

Blockchains & SmartContracts 

  • Revision of blockchains & smartcontracts(based INFO0934)
  • Smartcontract technologies, including Ropsten, MetaMask
  • Smartcontract programming in Solidity and Javascript
  • Developing a smartcontract application in Ethereum
  • Deploying smartcontracts on Ethereum and issuing transactions based on gas and ether

Regulations, Ethics and Legal Aspects 

  • Bias in AI (machine learning)
  • AI and anti-trust (competition law), algorithmic collusion
  • Discussion of legal, judicial cases 


Practical1: Web crawler with NoSQL


  • Harvest a collection of text articles using a web-crawler
  • Interface the crawler with MongoDB, dump the text in that database
  • Query the database for various descriptive statistics and meta-data on the texts

Practical2: Distributed Computing


  • For a given task, determine whether it lends itself to distributed computing, e.g. MapReduce
  • Implement the task using MapReduce, Spark
  • Estimate any gains in performance

Practical3: SmartContracts (if time permits)


  • Implement an Ethereum smart contract for a business transaction
  • Deploy and run the smart contract on a virtual blockchain


Practical sessions will be organised.

Course participants will present their practical projects.

Learning outcomes of the learning unit

  • Understand the underlying principles and algebraic formulations of advanced methods, algorithms and technologies in digital business.
  • Ability to apply these models in various managerial contexts.
  • Synthesize various principles and algorithms introduced in the course and to develop a full-fledge applications/solutions (as part of the course project)
  • Implement at least one of  the technology (smart contract project)
  • Formulate a strategy based on the  skills to optimize the value of an organization
  • Ability to perform research on and understand advanced topics in the field and to be informed on recent developments to adapt easily to changing requirements 
  • Communicate appropriately about text analytics projects/applications to various stakeholders

Prerequisite knowledge and skills

  • Reasonable skills (masters level) in mathematics & programming.

Planned learning activities and teaching methods

The course carries 5 credits and therefore requires 150 hours of work (1 credit = 30 hours).
Theory lectures = 18-22 hours 

  • Self-study for exam = approx. 70 hours
  • Practical lectures = 9-12 hours
  • Working on practical exercises and projects = approx. 80 hours
  • Total = 150 hours (5 credits)

Mode of delivery (face to face, distance learning, hybrid learning)

  • Lectures  (face-to-face)
  • Practical  (during lectures and as homework)

Recommended or required readings

  • Scientific articles, to be communicated during the lectures.These will be provided on Lol@, the online learning platform.

Exam(s) in session

Any session

- In-person

oral exam

Written work / report

Continuous assessment

Additional information:

Continuous Evaluation with final exam: 

  • Web crawling & NoSQL project (25%)
  • Spark/Hadoop project (35%)
  • Final exam (40%)
Note that the weights given are tentative and may change during the course of the lectures.

Work placement(s)

Organisational remarks and main changes to the course

Materials will be made available on Lol@, the online learning platform.


Ashwin Ittoo, ashwin.ittoo@uliege.be

Association of one or more MOOCs