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335107b Analytische Informationssysteme Übungen

Zuletzt geändert:25.09.2017 / Huber
Studiengänge: Wirtschaftsinformatik und digitale Medien (Bachelor, 7 Semester) , Prüfungsleistung im Modul Analytische Informationssysteme (ab SS 16) in Semester 4
Häufigkeit: immer
Dozent: Prof. Dr. Peter LehmannDetails zum Dozenten
Sprache: Englisch
Art: V
Umfang: 2 SWS
ECTS-Punkte: 3
  • Lecture: 15 weeks, 1,5 hours = 22 hours
  • Preparation: 15 weeks, 3 hours = 45 hours
Beschreibung: OLAP

Online Analytical Processing, or OLAP, is an approach for business users to answering multi-dimensional analytical queries. OLAP is part of the broader category of Business Intelligence. Typical applications of OLAP include business reporting, for example for sales, marketing, management reporting, budgeting and forecasting, financial reporting and similar areas. OLAP tools enable business users to analyze multidimensional data interactively from multiple perspectives. OLAP consists of three basic analytical operations: consolidation (roll-up), drill-down, and slicing and dicing. 1.Consolidation involves the aggregation of data that can be accumulated and computed in one or more dimensions. 2.Drill-down is a technique that allows users to navigate through the details. 3.Slicing and dicing is a feature whereby users can take out (slicing) a specific set of data of the OLAP cube and view (dicing) the slices from different viewpoints.

Multi-dimensional Modelling

Data Warehouses are databases used by decision makers to analyze the status and the development of an organization. Data Warehouses are based on large amounts of data integrated from heterogeneous sources into multi-dimensional, and they are optimized for accessing data in a way that comes natural to human analysts (e.g., OLAP applications). Data in a Data Warehouse are organized according to the multi-dimensional model, that hinges on the concepts of fact (a focus of interest for the decision-making process, such as sales and orders) and dimension (a coordinate for analyzing a fact, such as time, customer, and product). Each fact is quantified through a set of numerical measures, such as the quantity of product sold, the price of products, etc. DW design and development require ad hoc methodologies and an appropriate life-cycle.

Data Mining

Microsoft SQL Server is a complete set of enterprise-ready technologies and tools that help people derive the most value from information. We will demonstrate important data mining issues with easy to understand yet powerful business scenarios.
English Title: Hand's On's to Analytical Information Systems
English Abstract: In the Exercises, the students will lean: •How to create a Data Cube •How to create an OLAP Query •How to create an ETl Process •How to do Data Analytics •How to do Data Mining
Internet: www.bi-academy.eu
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