Moses - IMPRO3 - Big Data Analytics Project (BDAPRO)

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum WS 2015/16 - WS 2015/16. Die Version für das aktuelle Semester (SoSe 2024) finden Sie hier: Modulbeschreibung des SoSe 2024

Module / Version:
#40494 / #3

Validity:
WS 2015/16 - WS 2015/16

Default display language:
English

Module title:
IMPRO3 - Big Data Analytics Project (BDAPRO)

Credits:
9

Responsible person:
Markl, Volker

Grading:
benotet

Type of exam:
Portfolioprüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Softwaretechnik und Theoretische Informatik

Area of expertise:
34351500 FG Datenbanksysteme und Informationsmanagement (DIMA)

Prüfungsausschuss:
No information

Kontakt

Office:
EN 7

Contact person:
Soto, Juan

E-mail address:
sekr@dima.tu-berlin.de

Website:

https://www.tu.berlin/dima

Learning Outcomes

In this course you will learn to systematically analyze a current issue in the information management area and to develop and implement a problem-oriented solution as part of a team. You will learn to cooperate as team member and to contribute to project organization, quality assurance and documentation. The quality of your solution has to be proven through analysis, systematic experiments and test cases. Examples of IMPRO projects carried out in recent semesters are a tool used to analyse Web 2.0 Forum data, an online multiplayer game for mobile phones, implementation and analysis of new join methods for a cloud computing platform or the development of data mining operations on the massively parallel system Hadoop as part of the Apache open source project Mahout. 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. Technical skills 30% Methodological skills 30% System skills 10% Social skills 30%

Content

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 applying data mining algorithms to large datasets. For that, students will learn to use so called Parallel Processing Platforms, 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 report, a project poster as well as a final presentation which includes a demonstration of the prototype.

Module Components

Pflichtteil:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
IMPRO3: Big Data Analytics Project	PJ	0434 L 484	WiSe/SoSe	No information	6

Workload and Credit Points

IMPRO3: Big Data Analytics Project (PJ):

Workload description	Multiplier	Hours	Total
Documentation, Presentations	1.0	40.0h	40.0h
Implementation, Tests, Experiments	1.0	130.0h	130.0h
Participation in Meetings	20.0	3.0h	60.0h
Preparation Phase and Design	1.0	40.0h	40.0h
			270.0h(~9 LP)

The Workload of the module sums up to 270.0 Hours. Therefore the module contains 9 Credits.

Description of Teaching and Learning Methods

Guided and self-organized project work.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Knowledge from the complete Bachelor program (Informatik or Technische Informatik) is required, as well as experiences in programming, software development, linear algebra and statistics. Depending on the topic, additional prerequisites may be required, e.g. „IDB – Implementation of Database Systems"

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

No information

Language

English

Test elements

Name	Points/Weight	Categorie	Duration/Extent
Documentation	10	No information	No information
Final Presentation	10	No information	No information
Final Report	10	No information	No information
Intermediate Presentation	10	No information	No information
Project Poster	10	No information	No information
Prototype with test cases	50	No information	No information

Grading scale

No information

Test description (Module completion)

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. Intermediate presentation (10p.) 2. Prototype with test cases (50p.) 3. Documentation (10p.) 4. Final Report (10p.) 5. Project Poster (10p.) 6. Final presentation (10p.) 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

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 literature
[1] H. Garcia-Molina, J. Ullman, J. Widom: Database Systems – The Complete Book, Pearson 2009
[2] A. Rajaraman, J. Ullman: Mining of Massive Datasets, Cambridge 2010
More, project specific, literature will be announced in the first lecture.

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

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

	Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
This module is not used in any degree program.

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 it is a compulsory class for the ERASMUS MUNDUS IT4BI master programme.

IMPRO3 - Big Data Analytics Project (BDAPRO)