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#41095 / #1

Seit SoSe 2023


Seminar Large-scale Data Engineering


Böhm, Matthias




Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34352900 FG Big Data Engineering

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TEL 8-1

Damme, Patrick


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Learning Outcomes

In this seminar module, students will learn how to critically read scientific publications, search for scientific literature on a given topic, write a high-quality scientific paper, and give high-quality presentations on papers. All of these aspects are covered with a special focus on the areas data engineering, data management, and machine learning systems.


This module is comprised of a seminar in the large context of big data engineering, i.e., topics related to scalable data and ML systems. In detail, the module is structured as follows: * 3 Lectures on scientific methods (structure of scientific papers, scientific reading and writing, experiments and reproducibility) * Reading selected papers and writing a 6-page summary paper (in LaTeX with provided template) * 20min oral presentation of summarized topic

Module Components


All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Large-scale Data EngineeringSeminarWiSe/SoSeEnglish2

Workload and Credit Points

Large-scale Data Engineering (Seminar):

Workload descriptionMultiplierHoursTotal
Attendance Lectures3.02.0h6.0h
Paper reading and writing1.065.0h65.0h
Talk Preparation and Presentation1.015.0h15.0h
86.0h(~3 LP)
The Workload of the module sums up to 86.0 Hours. Therefore the module contains 3 Credits.

Description of Teaching and Learning Methods

Guided and self-organized reading of scientific papers, literature search, and writing of a summary paper. In the beginning of the semester, students will hear presentations on reading scientific papers, finding related work, writing high-quality scientific papers, and giving a high-quality scientific presentation. Each student will be assigned an initial paper to read and understand. After that, students search for related work and write a short summary of the assigned paper, including some remarks on related work. In the end of the semester, each student gives a slide presentation in front of the group, followed by a discussion of the topic.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Completed basic courses on applied machine learning and data management

Mandatory requirements for the module test application:

This module has no requirements.

Module completion



Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt



Test elements

(Deliverable assessment) Paper65written6 pages
(Deliverable assessment) Presentation35oral20 min

Grading scale

Notenschlüssel »Notenschlüssel 2: Fak IV (2)«


Test description (Module completion)

All parts of the portfolio exam are graded individually for every student.

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Winter- und Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 20.

Registration Procedures

Registration via email to Patrick Damme (patrick.damme@tu-berlin.de)

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable


Electronical lecture notes

Availability:  unavailable



Recommended literature
Seminar-specific literature will be discussed during the first lecture.

Assigned Degree Programs

This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Informatik (B. Sc.)13SoSe 2023SoSe 2024
Medientechnik (B. Sc.)13SoSe 2023SoSe 2024
Technische Informatik (B. Sc.)13SoSe 2023SoSe 2024
Wirtschaftsinformatik (B. Sc.)26SoSe 2023SoSe 2024


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