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#40353 / #10

SoSe 2023 - WiSe 2023/24

English

BDASEM Big Data Analytics Seminar

3

Markl, Volker

benotet

Portfolioprüfung

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34351500 FG Datenbanksysteme und Informationsmanagement (DIMA)

No information

Kontakt


EN 7

Soto, Juan

sekr@dima.tu-berlin.de

Learning Outcomes

During the course of the seminar, the students will learn: (1) how to critically read and interpret scientific literature, (2) how to give a good scientific presentation that is technically precise, concentrated on the relevant topics, and also enjoyable, (3) how to summarize and write a critical, succinct technical review of a research paper. In addition, students will receive an introduction to contemporary research topics in data management, database systems, or technologies and systems for big data management and data science. Moreover, students will gain a great understanding of scientific publications, conferences, and the scientific review process.

Content

During the initial phase of this class, students will hear presentations on how to read and review scientific papers, and how to give a good presentation. Additionally, all participants will receive a raw submission version of a top-tier conference research paper. Task of the students is to critically read the paper and prepare a written review of the paper, following a typical conference reviewing template. Afterwards, students will receive the actual expert reviews of the paper, compare these to their findings, and prepare and deliver a presentation that discusses the improvements made between the original submission and the publication of the paper based on the reviewers' criticisms. Moreover, each week, students will be required to reflect on the main challenges addressed in each of their peers' presentations. (Details will be announced in the class.) Representative topics to be discussed, include data stream processing (e.g., scalable and parallel window joins, window aggregation, state management), data processing on modern hardware (e.g., GPU data processing, FPGA acceleration for data sketching), data processing for machine learning and data science (e.g., optimizing machine learning pipelines, dynamic parameter allocation in parameter servers), and query optimization and compilation (e.g., adaptive compilation).

Module Components

Pflichtteil:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
BDASEM - Big Data Analytics SeminarSEM0434 L 477WiSe/SoSeEnglish2

Workload and Credit Points

BDASEM - Big Data Analytics Seminar (SEM):

Workload descriptionMultiplierHoursTotal
Plenary Sessions15.02.0h30.0h
Preparation/Individual Work15.04.0h60.0h
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 students will receive an originally submitted conference paper. Then each student should identify and use secondary sources to further investigate the assigned topic. A few weeks afterwards students will prepare a written review of their paper adhering to the scientific reviewing standards. At the end of the semester, each student will offer a presentation, as well as a discussion of all of the other seminar papers. 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 third semester Master's students with a focus on database systems and information management. Desirable prerequisites include the following: (a) successfully completed the TU Berlin Database Technology (DBT) course or its equivalent, (b) at least one of the advanced information management courses, such as the Management of Data Streams or Data Management on Modern Hardware, (c) sound understanding of written and spoken English. Ideally, this seminar should be taken the semester before commencing your master's thesis.

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

English

Test elements

NamePointsCategorieDuration/Extent
(Deliverable Assessment) Discussion of Seminar Content30oral3-5 minutes discussion per paper
(Deliverable Assessment) Seminar Presentation40oral20 minutes
(Deliverable Assessment) Written Paper Review30written3-5 pages

Grading scale

Notenschlüssel »Notenschlüssel 2: Fak IV (2)«

Gesamtpunktzahl1.01.31.72.02.32.73.03.33.74.0
100.0pt95.0pt90.0pt85.0pt80.0pt75.0pt70.0pt65.0pt60.0pt55.0pt50.0pt

Test description (Module completion)

The exam will be done as a 'portfolio examination'. See the table above for details. The final grade according to § 68 (2) AllgStuPO will be calculated with the faculty grading Table 2. (Die Gesamtnote gemäß § 68 (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 8.

Registration Procedures

Students are required to register for the course in the official TUB examination system within six weeks after commencement of the first lecture or when the first graded assignment is due, whichever happens to be first.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  unavailable

 

Literature

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):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Computer Engineering (M. Sc.)14SoSe 2023WiSe 2023/24
Computer Science (Informatik) (M. Sc.)16SoSe 2023WiSe 2023/24
Elektrotechnik (M. Sc.)14SoSe 2023WiSe 2023/24
ICT Innovation (M. Sc.)12SoSe 2023WiSe 2023/24
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)14SoSe 2023WiSe 2023/24
Medieninformatik (M. Sc.)12SoSe 2023WiSe 2023/24
Wirtschaftsingenieurwesen (M. Sc.)12SoSe 2023WiSe 2023/24
This course targets Master's students interested in database systems and information management. This seminar is one of eligible courses for those following the Fak. IV Data Science and Engineering Track: https://www.tu.berlin/en/dima/analytics/data-science-and-engineering-track.

Miscellaneous

No information