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BDASEM - Big Data Analytics Seminar



#40353 / #7

SoSe 2021 - SoSe 2021

Fakultät IV

EN 7

Institut für Softwaretechnik und Theoretische Informatik

34351500 FG Datenbanksysteme und Informationsmanagement

Markl, Volker

Soto, Juan

POS-Nummer PORD-Nummer Modultitel
60806 29164 BDASEM - Big Data Analytics Seminar

Learning Outcomes

Participants of this seminar will acquire knowledge about recent research results and trends in the analysis of web-scale data. Through the work in this seminar, students will learn the comprehensive preparation and presentation of a research topic in this field. In order to achieve this, students will get to read and categorise a scientific paper, conduct background literature research and present as well as discuss their findings. After the course, students will be able to critically read and evaluate scientific publications, and to conduct background research. They will be capable of preparing for and giving oral presentations on research topics for an expert audience, of analyzing the state of the art of a research topic, and of summarizing it in a scientific paper. They should also understand techniques used in the scientific community like peer reviews, conference presentations, and defenses of the findings after their presentation, as well as they should understand methods for large-scale data analytics. The course is principally designed to impart: Technical skills: 50x Methodological skills: 20x System skills: 10x Social skills: 20x


Both the sciences and industry are currently undergoing a profound transformation: largescale, diverse data sets - derived from sensors, the web, or via crowd sourcing - present a huge opportunity for data-driven decision making. This data poses new challenges in a variety of dimensions: in its unprecedented volume, in the speed at which it is generated (its velocity) and in the variety of data sources that need to be integrated. A whole new breed of systems and paradigms is currently developed to be able to cope with that these challenges. The field of Big Data Analytics deals with the technological means of gaining insights from huge amounts of data. In this seminar, students will review the current state of the art in this field. At the beginning of the semester students will receive a set of primary literature, which consists of basic reading material for every participant. Students will review this material under the guidance of a mentor. At the same time, students will learn about presentation techniques, techniques for critical reading of scientific papers, as well as scientific writing. Students will use secondary literature to research the assigned topic assigned, including research journals and articles published at information management conferences such as WWW, VLDB, or SIGMOD.

Module Components


All Courses are mandatory.

Course Name Type Number Cycle Language SWS VZ
BDASEM - Big Data Analytics Seminar SEM 0434 L 477 WS/SS English 2

Workload and Credit Points

BDASEM - Big Data Analytics Seminar (SEM):

Workload description Multiplier Hours Total
Plenary sessions 15.0 2.0h 30.0h
Preparation/Individual work 15.0 4.0h 60.0h
90.0h(~3 LP)
The Workload of the module sums up to 90.0 Hours. Therefore the module contains 3 Credits.

Description of Teaching and Learning Methods

At the beginning of the semester you will receive a set of primary literature. Then you should find and use secondary sources to investigate the topic assigned to you in the seminar, which should go beyond the supplied primary literature. A few weeks after receiving the primary literature you will have to give a short presentation of approx. 10 minutes. The presented topic will be discussed by the other seminar participants in order to give you constructive feedback about the content of your talk. In the further course of the semester you will have to give a long final presentation of approx. 30 minutes. In addition to the talk you will also have to hand in a 10-15 page technical report describing and evaluating your topic. Details may vary and will be announced in the respective semester.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

This course is aimed at master students with a focus on database systems and information management and should be chosen after the 2nd master semester. To be able to participate you should have successfully completed 'Database Technology DBT' and one of the 'Advanced Information Management AIM' (1,2,3) courses. In the ideal case this seminar should be taken by students who are directly in front of their master thesis, or have already completed it and are interested in a doctorate in information management. It is vital that you have a sound understanding of written and spoken English.

Mandatory requirements for the module test application:

No information

Module completion



Type of exam

Portfolio examination

Type of portfolio examination

100 points in total



Test elements

Name Points Categorie Duration/Extent
(Deliverable assessment) Long written seminar report 40 written 10-15 pages
(Deliverable assessment) Presentation 1 20 oral 20 min
(Deliverable assessment) Presentation 2 30 oral 30 min.
(Deliverable assessment) Short written seminar report/s 10 written 4-6 pages in total

Grading scale

Test description (Module completion)

The exam will be done as a 'portfolio examination', including two deliverable assessments, totaling for 100 portfolio points: - 2 seminar presentations (50 portfolio points) - 2 written seminar reports (50 portfolio points) Cf. the details in the resp. fields, and follow the instructions in the beginning of the class. The final grade according to § 47 (2) AllgStuPO will be calculated with the faculty grading table 2. (Die Gesamtnote gemäß § 47 (2) AllgStuPO wird nach dem Notenschlüssel 2 der Fakultät IV ermittelt.)

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 16.

Registration Procedures

For registration to this module, please apply to DIMA following the guidelines in the DIMA web site:

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

Electronical lecture notes

Availability:  unavailable


Recommended literature
Will be released at the beginning of the seminar.

Assigned Degree Programs

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

This moduleversion is used in the following modulelists:

This module is mandatory for the Erasmus Mundus IT4BI master program, and it is mandatory elective for the EIT-Digital Data Science Master.


No information