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Veranstaltungsbeschreibung

335137a Vorlesung Business Intelligence

Zuletzt geändert:25.02.2020 / von Carlsburg
EDV-Nr:335137a
Studiengänge:
Dozent: Prof. Dr. Peter Lehmann
Sprache: Deutsch
Art: -
Umfang: 2 SWS
ECTS-Punkte: 2
Workload:

  • Lecture: 15 weeks, 1,5 hours = 22 hours
  • Preparation: 15 weeks, 3 hours = 45 hours

Inhaltliche Verbindung zu anderen Lehrveranstaltungen im Modul: keine
Prüfungsform:
Beschreibung:

Participants will learn the key aspects and principles of business intelligence, data warehousing and data mining know. Specifically, the following points are covered:

Einführung in Business Intelligence
  • Betriebliche Informationssysteme
  • Das Problem der Vertikalen Integration
  • Das Data-Warehouse-Konzept
  • Data-Warehouse-Referenzarchitektur
  • Strukturen eines Data Marts
  • Online-Analytical-Processing
Multidimensionale Modellierung (I)
  • Dimensional Fact Modelling
  • Star Schema
  • Der ETL-Prozess
  • Basic Data Flows
  • Lookup-Transformations
  • Calculation-Transformations
  • Mapping Source - Destination
Multidimensionale Modellierung (II)
  • Galaxy Schema
  • Slowly Changing Dimensions
  • Modellierung von KPIs
  • Modellierung von Hierarchien
  • Modellierung von berechneten Kennzahlen
Data Mining and Supervised Learning
English Title: Analytical Information Systems
English Abstract:

The lecuture will introduce the concept of Business Intelligence to the audience. For informed decision-making in companies an integrated view of all decision-relevant information is essential. Business Intelligence (BI) refers to strategies, methods and technologies to gain from heterogeneous distributed data and mission-critical knowledge about the status, potentials and prospects of a company. The associated functions are performed by different tools, seamless interaction is the key feature of a BI landscape. Data warehouse systems and their processes are central concepts for the integration of data and ensuring high data quality. Building on offer BI systems such as Management dashboards or OLAP-based BI services capabilities to analyze operational metrics and processes. Data mining methods also provide insights into previously unknown relationships in business data.

Literatur:

    Gluchowski, P.; Gabriel, R.; Dittmar, C. (2008): Management Support Systeme und Business Intelligence. Computergestützte Informationssysteme für Fach- und Führungskräfte, Springer, 2008
    Kimball, R.; Ross, M. (2013): The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, Wiley, 2013.
    Inmon, W.H.; Linstedt, D. (2014): Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault, Morgan Kaufmann; 1 edition, 2014.



Weitere Literatur finden Sie in der HdM-Bibliothek.
Internet:

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