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BDASEM - Big Data Analytics Seminar

3 LP

English

#40353 / #4

Seit SS 2017

Fakultät IV

EN 7

Institut für Softwaretechnik und Theoretische Informatik

34351500 FG Datenbanksysteme und Informationsmanagement

Markl, Volker

Rabl, Tilmann

sekr@dima.tu-berlin.de

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

Content

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

Pflichtteil:

All Courses are mandatory.

Course Name Type Number Cycle Language SWS
BDASEM - Big Data Analytics Seminar SEM 0434 L 477 WS No information 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

Grading:

graded

Type of exam:

Portfolio examination

Language:

English

Typ of portfolio examination

100 points in total

Test elements

Name Points Categorie Duration/Extent
(Deliverable assessment) presentation 50 oral 60 min.
(Deliverable assessment) written seminar report 50 written 20-25 pages

Grading scale

1.01.31.72.02.32.73.03.33.74.0
95.090.085.080.075.070.065.060.055.050.0

Test description (Module completion)

The exam will be done as a 'portfolio examination', including two deliverable assessments, totaling for 100 portfolio points: - seminar presentation (50 portfolio points) - written seminar report (50 portfolio points) 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

This module can be completed in one semester.

Maximum Number of Participants

The maximum capacity of students is 16.

Registration Procedures

Students are required to register via the DIMA course registration tool before the start of the first lecture (http://www.dima.tu-berlin.de/). Within the first six weeks after commencement of the lecture, students will have to register for the course at QISPOS (university examination protocol tool) and ISIS (course organization tool) in addition to the registration at the DIMA course registration tool.

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 modulelists:

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

Miscellaneous

No information