1. Modulverzeichnis (MTS)
  2. Module
  3. Modulbeschreibung anzeigen

BDSPRO - Big Data Systems Project

Zur Modulseite PDF generieren

#40494 / #7

SoSe 2022 - WiSe 2022/23

English

BDSPRO - Big Data Systems Project

9

Markl, Volker

Benotet

Portfolioprüfung

English

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34351500 FG Datenbanksysteme und Informationsmanagement (DIMA)

Keine Angabe

Kontakt


EN 7

Soto, Juan

sekr@dima.tu-berlin.de

https://www.tu.berlin/dima

Lernergebnisse

In this course students will learn how to systematically analyze a current issue in the information management area as well as develop and implement a problem-oriented solution as part of a team. Students will learn how to cooperate as team member, to contribute to project organization, quality assurance and documentation, and to evaluate the quality of your solution through analysis, systematic experiments and test cases. After the course, students will be able to understand methods for large scale data analytics and to solve large scale data analytics problems. They will be capable of designing and implementing large-scale data analytics solutions in a collaborative team

Lehrinhalte

Both the sciences and industry are currently undergoing a profound transformation: large-scale, 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. Students will conduct projects that deal with topics related to various aspects of data management, such as Novel Database Architectures, Distributed Database Systems, Database Engines and others. This scope of the project will be adjusted to the final group size to reflect the overall workload of the course (i.e., 270h of work per student) For that, students will learn to use so called Parallel Processing Platforms (e.g. Flink, Spark, Hadoop, HBase), systems that execute parallel computations with terabytes of data on clusters of up to several thousand machines. At the start of the project, a student will receive a topic as well as some information material. The team, with the assistance of the lecturer, will decide on a project environment with the suitable tools for team work, project communication, development and testing. Next, the problem will have to be analyzed, modelled and decomposed into individual components, from which tasks are derived that are subsequently assigned to smaller teams or individuals. At weekly project meetings, the project team presents progress and milestones that have been reached. In consultation with the lecturer, it is decided which further steps to take. The project is concluded with a final presentation which includes a demonstration of the prototype.

Modulbestandteile

Compulsory area

Die folgenden Veranstaltungen sind für das Modul obligatorisch:

LehrveranstaltungenArtNummerTurnusSpracheSWS ISIS VVZ
BDAPRO - Big Data Analytics ProjectPJ0434 L 484WiSe/SoSeKeine Angabe6
ISIS-Kurssuche nach Veranstaltungen des Moduls

Arbeitsaufwand und Leistungspunkte

BDAPRO - Big Data Analytics Project (PJ):

AufwandbeschreibungMultiplikatorStundenGesamt
Documentation, Presentation1.040.0h40.0h
Participation in Meetings20.03.0h60.0h
Implementation, Tests, Experiments1.0130.0h130.0h
Preparation Phase and Design1.040.0h40.0h
270.0h(~9 LP)
Der Aufwand des Moduls summiert sich zu 270.0 Stunden. Damit umfasst das Modul 9 Leistungspunkte.

Beschreibung der Lehr- und Lernformen

Guided and self-organized project work.

Voraussetzungen für die Teilnahme / Prüfung

Wünschenswerte Voraussetzungen für die Teilnahme an den Lehrveranstaltungen:

Knowledge from the complete Bachelor program (Informatik or Technische Informatik) is required, as well as linear algebra and statistics. Depending on the topic, additional prerequisites may be required, e.g. „DBT - Database Technology" Solid programming skills in at least one of the following programming languages: Java, C++, Scala, Python. Basic knowledge in functional programming. Basic knowledge in distributed source control management systems (Git, Mercurial) and software processes like Scrum. Student must have completed DBT prior to enrolling in BDSPRO. In addition, students must have already completed SDS prior to enrolling in BDSPRO or be concurrently enrolled in SDS and BDSPRO in the same semester. • General Computer Science knowledge: algorithms, data structures, systems architecture, Git • Solid programming skills in at least one of the languages: Java, C/C++, Scala (depending on the project) • Depending on the topic, additional prerequisites may be required, such as, knowledge of database systems, or previous attendance in DIMA courses (e.g., DBT, ISDA)

Verpflichtende Voraussetzungen für die Modulprüfungsanmeldung:

Dieses Modul hat keine Prüfungsvoraussetzungen.

Abschluss des Moduls

Benotung

Benotet

Prüfungsform

Portfolio examination

Art der Portfolioprüfung

100 Punkte insgesamt

Sprache(n)

English

Prüfungselemente

NamePunkteKategorieDauer/Umfang
(Deliverable assessment) Experiments analysis20praktischabout 30h
(Deliverable assessment) Final presentation20mündlichabout 20 minutes
(Deliverable assessment) Intermediate presentation10mündlichabout 10-15 minutes
(Learning process review) Experiment design and execution20praktischabout 30h
(Learning process review) Prototype with test cases and documentation30praktischabout 60h

Notenschlüssel

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

Prüfungsbeschreibung (Abschluss des Moduls)

The overall grade for the module consists of the results of the course work ('portfolio exam'). The following are included in the final grade: 1. Prototype with test cases and documentation (30p.) 2. Experiment design and execution (20p.) 3. Intermediate presentation (10p.) 4. Experiments analysis (20p.) 5. Final presentation (20p.) In the final grade, students are graded individually, i.e., final grades between students in a group can vary depending on the amount of work carried out by each person. 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.)

Dauer des Moduls

Für Belegung und Abschluss des Moduls ist folgende Semesteranzahl veranschlagt:
1 Semester.

Dieses Modul kann in folgenden Semestern begonnen werden:
Winter- und Sommersemester.

Maximale teilnehmende Personen

Die maximale Teilnehmerzahl beträgt 18.

Anmeldeformalitäten

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.

Literaturhinweise, Skripte

Skript in Papierform

Verfügbarkeit:  nicht verfügbar

 

Skript in elektronischer Form

Verfügbarkeit:  nicht verfügbar

 

Literatur

Empfohlene Literatur
Project specific literature will be announced in the first lecture.

Zugeordnete Studiengänge


Diese Modulversion wird in folgenden Studiengängen verwendet:

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Dieses Modul findet in keinem Studiengang Verwendung.
This course addresses master students with a focus on database systems and information management after the first (master) term in “Informatik - System Engineering”, “Technische Informatik -- Informationssysteme”, “Wirtschaftsingenieurwesen -- IuK”. (If capacity is available, it will be open also for other faculties). Moreover, BDSPRO is a compulsory elective for the ICT Innovation (i.e., EIT Digital Data Science Master’s)

Sonstiges

Keine Angabe