CourseSpecifications
Valid as from the academic year 2024-2025

Big Data Algorithms (E018250)

Course size
Credits 3.0(nominal values; actual values may depend on programme)
Study time 90 h

Course offerings in academic year 2024-2025
A (semester 2)
EnglishGent

Lecturers in academic year 2024-2025
De Witte, Dieter
De Bie, Tijl
Lijffijt, Jefrey

TW06
TW06
TW06

Offered in the following programmes in 2024-2025
Bridging Programme Master of Science in Bioinformatics(main subject Engineering)
Master of Science in Bioinformatics(main subject Engineering)
Master of Science in Industrial Engineering and Operations Research(main subject
Manufacturing and Supply Chain Engineering)
Master of Science in Industrial Engineering and Operations Research(main subject
Transport and Mobility Engineering)
Master of Science in Computer Science Engineering
Master of Science in Industrial Engineering and Operations Researchlecturer-in-charge
co-lecturer
co-lecturer
crdts
33
3offering
AAA

3A

33

A
A

Teaching languages
EnglishKeywords
Hashing, Sketching, Algorithms for large-scale ML and Data Mining, Big Data frameworks,
Graph embedding, Graph, analytics, Pattern mining
Position of the course
Advanced, scalable algorithms are indispensable in the toolkit of modern data engineers.
Beyond the typical deep learning tools, these algorithms often play a significant role in their
daily practice where data can have 'challenging dimensions'. This 'Big Data' can be found inboth centralized data warehouses as well as decentralized systems or even data streams.
We also cover data quality and data privacy, as they are some of the biggest hurdles in daily
handling of (sensitive) datasets.
Finally, we explore how insights can be extracted from (knowledge) graphs using graphembeddings.
A number of hands-on assignments are provided, allowing students to explore theoreticaltopics.
.
Contents• Processing data streams: sketching algorithms, probabilistic counting, and online• algorithms• Scalable data mining: algorithms for clustering, dimensionality reduction, and machine• learning fit for a distributed context. (for example: with MapReduce)• Decentralized Data mining: federated querying and federated learning in decentralized data
• sources such as data vaults (SOLID) and in data spaces• Data Quality: generic techniques to improve data quality, hashing techniques for detecting• (near-)duplicates.
• Data Privacy and Security: algorithms for anonymization and pseudonymization, the• European AI Act and GDPR, risks of de-anonymization. Privacy-preserving machine learning(Approved)1• (differential privacy, homomorphic encryption, etc).
• Mining of (knowledge) graphs: algorithms for detecting structures in (social) networks• (triangles, communities, centrality, k-cores, etc.), embeddings.
Initial competences• basic programming skills• • Experience with Python (passed the course Informatics E015041 or an equivalent course)• • Experience with Object Oriented Programming (passed the course Computer• • Programming E017210 or an equivalent course)• • Experience with data structures and algorithms• elementary understanding about basic data formats (CSV, TSV, etc.)• linear algebra• introductory course on statistics• ML and AI basics
Final competences
1 Handle datasets with multiple challenging dimensions (size, format, quality, …)
2 Setting up a (cloud) environment for scalable data processing
3 Applying machine learning / data mining algorithms to Big Data
4 Applying sketching techniques to solve challenging Big Data problems
5 Have an in-depth understanding on how to transform graphs to be used in machine learning
1 setups
6 Being able to conduct an analysis on a large relational graph
7 Detect near-duplicates in large datasets using hashing techniques
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Lecture, Practical
Study material
Type: Handbook
Name: Mining of Massive Datasets (3rd edition)
Indicative price: Free or paid by faculty
Optional: noLanguage : EnglishAuthor : Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman
ISBN : 978-1-10847-634-8
Number of Pages : 315
Online Available : Yes
Available in the Library : No
Available through Student Association : No
Usability and Lifetime within the Course Unit : regularly
Usability and Lifetime within the Study Programme : one-time
Usability and Lifetime after the Study Programme : notReferences
Course content-related study coaching
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Oral assessment
Examination methods in case of periodic assessment during the second examination period
Oral assessment
Examination methods in case of permanent assessment
Skills test, Presentation
Possibilities of retake in case of permanent assessment(Approved)2examination during the second examination period is not possible
Extra information on the examination methods• Periodical evaluation• • Oral exam with limited preparation time
Calculation of the examination mark
50% lab reports, 50% oral exam

(Approved)3