Big Data

Module Code
EB2505
Module Coordinator
  • Harald Ritz
Teacher
  • Harald Ritz
Short Description

Especially in the area of social media, a large variety of large and heterogeneous data sets are generated, which in part provide real-time results for decision-making. On the way to a data-driven enterprise, advanced data retention methods, scalable computing, and advanced analytics skills are needed to meet these big data challenges. This module deals with the relevant technologies and analytical methods.

Learning Objectives

Students understand the basic principles and benefits of big-data technologies and analysis methods and can classify them into a company processes. They know the basic challenges and can design and implement data models to build a big data warehouse. The students will be able to realistically evaluate the potential applications of big data technologies for various operational application scenarios in the field of social media, to create an introductory concept and to evaluate them with their own experiments.

Contents
  • Big Data: Basics & terms
  • Big Data use cases
  • Integrated application landscape with big data solutions
  • Big Data technologies:
  • Storage solutions: Apache Hadoop ecosystem
  • Distributed non-relational database systems
  • Column-oriented databases
  • Document-oriented databases
  • key-value databases
  • Graph Databases
  • In-memory databases
  • Database & programming languages (including Mape Reduce programming model)
  • Data Integration Tools
  • Display tools for visualization
  • Big Data analysis methods:
  • Data mining methods
  • Advanced Analytics (Predictive and Prescriptive Analytics)
  • Text and Web Mining (including sentiment analysis)
  • Big Data Strategy Development
  • feasibility study, introduction of procedures, efficiency considerations, maturity models
  • Big-Data project case studies with various software tools
Duration in Semester
1
Instruction Language
German
Total Effort
6.0 CrP; an estimated 180 hours, of which approximately 60 are spent in class.
Weekly School Hours
4
Method of Instruction

Seminar 2 SWS, practical course 2 SWS

Requirements for the awarding of Credit Points

Prüfungsvorleistung: Regelmäßige Teilnahme

Prüfungsleistung: Klausur, Projekt oder eine Kombination von beiden. Es wird eine Gesamtnote vergeben. (Art des Leistungsnachweises wird den Studierenden rechtzeitig und in geeigneter Weise bekannt gegeben.)

Evaluation Standard

according to examination regulations (§ 9)

Availability
Yearly
References
  • Edlich, St. u.a.: NoSQL-Einstieg in die Welt der nichtrelationalen Web 2.0 Datenbanken, Hanser, 2. Aufl., München 2011
  • Redmond, E.; Wilson, J.R.: Sieben Wochen, sieben Datenbanken : moderne Datenbanken und die NoSQL-Bewegung, The Pragmatic Programmers, Dallas 2012
  • Hurwitz, J. u.a.: "Big Data for dummies", Wiley, Hoboken (NJ), 2013
  • DeRoos, D. u.a.: "Hadoop for dummies", Wiley, Hoboken (NJ), 2014
  • Bari, A.; Chaouchi, M.; Jung, T.: "Predictive Analytics for dummies", Wiley, Hoboken (NJ), 2014
  • Cleve, Jürgen; Lämmel, Uwe: Data Mining, de Gruyter Oldenbourg, München 2014
  • Kohlhammer, Jörn; Proff, Dirk U.; Wiener, Andreas: Visual Business Analytics,dpunkt.verlag, Heidelberg 2013
  • BI-Spektrum: Fachzeitschrift für Business Intelligence und Data Warehousing / eine Publikation des TDWI Germany e.V. - Präsenzexemplar im Lesesaal der THMBibliothek
  • Dorschel, Joachim (Hrsg.): "Praxishandbuch Big Data - Wirtschaft-Recht-Technik", Springer Gabler, Wiesbaden, 2015

Eine große Auswahl von weiteren Fachbüchern zu Big Data, Hadoop / MapReduce, NoSQL (HBase, MongoDB, CouchDB, Cassandra u.a.), Daten-Visualisierung, Data Mining, Text Mining, Predictive Analytics und Business Intelligence sowie Data Warehousing werden projektspezifisch empfohlen und sind in der THM-Bibliothek verfügbar.