1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Dimensional Modeling I Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.

Slides:

Advertisements

Similar presentations

1 Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this proposal or quotation. An Introduction to Data.

Advertisements

Alternative Database topology: The star schema

Copyright © Starsoft Inc, Data Warehouse Architecture By Slavko Stemberger.

 Data Warehouse Architecture By: Harrison Reid. Outline  What is a Data Warehouse Architecture  Five Main Data Warehouse Architectures  Factors That.

Data Warehousing M R BRAHMAM.

Cognos 8 BI Transformer Fundamentals. Objectives  At the end of this module, you should be able to:  discuss the basics of OLAP analysis  discuss the.

Jennifer Widom On-Line Analytical Processing (OLAP) Introduction.

Introduction to data warehouses

Dimensional Modeling Business Intelligence Solutions.

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Introduction to Data Mining Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.

An Introduction to Dimensional Data Warehouse Design Presented by Joseph J. Sarna Jr. JJS Systems, LLC.

MIS 451 Building Business Intelligence Systems Logical Design (5) – Aggregate.

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Data Staging Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential Chair of.

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Association Rule Mining II Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.

Data Warehousing Design Transparencies

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Dimensional Modeling V Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) From Information Management to Knowledge Management Olivia R. Liu Sheng, Ph.D.

ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis)

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Physical Data Warehouse Design Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Dimensional Modeling II Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Introduction to Data Warehouse Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.

Data Warehousing - 3 ISYS 650. Snowflake Schema one or more dimension tables do not join directly to the fact table but must join through other dimension.

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) The Data Warehouse Lifecycle Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.

MIS 451 Building Business Intelligence Systems Logical Design (3) – Design Multiple-fact Dimensional Model.

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Clustering Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential Chair of.

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Dimensional Modeling VI Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.

Lecture-33 DWH Implementation: Goal Driven Approach (1)

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential Chair of Business Dimensional.

Data Warehousing DSCI 4103 Dr. Mennecke Introduction and Chapter 1.

Data Warehouse Toolkit Introduction. Data Warehouse Bill Inmon's paradigm: Data warehouse is one part of the overall business intelligence system. An.

Data warehousing theory and modelling techniques Building Dimensional Models.

MDC Open Information Model West Virginia University CS486 Presentation Feb 18, 2000 Lijian Liu (OIM:

DWH – Dimesional Modeling PDT Genči. 2 Outline Requirement gathering Fact and Dimension table Star schema Inside dimension table Inside fact table STAR.

1 Introduction to databases concepts CCIS – IS department Level 4.

Program Pelatihan Tenaga Infromasi dan Informatika Sistem Informasi Kesehatan Ari Cahyono.

Data Warehouse and Business Intelligence Dr. Minder Chen Fall 2009.

DIMENSIONAL MODELLING. Overview Clearly understand how the requirements definition determines data design Introduce dimensional modeling and contrast.

Data Warehouse. Design DataWarehouse Key Design Considerations it is important to consider the intended purpose of the data warehouse or business intelligence.

1 Data Warehouses BUAD/American University Data Warehouses.

BMI Consulting Business Intelligence Roadmap Business Analysis Requirements Subject Modeling.

The Data Warehouse “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of “all” an organisation’s data in support.

IS 325 Notes for Wednesday August 28, Data is the Core of the Enterprise.

Datawarehouse A sneak preview. 2 Data Warehouse Approach An old idea with a new interest: Cheap Computing Power Special Purpose Hardware New Data Structures.

6.1 © 2010 by Prentice Hall 6 Chapter Foundations of Business Intelligence: Databases and Information Management.

Data Staging Data Loading and Cleaning Marakas pg. 25 BCIS 4660 Spring 2012.

3 Defining the Business and Logical Models. Designing the Conceptual Model Phase I: Functional and Nonfunctional Requirements.

Creating the Dimensional Model

MIS 451 Building Business Intelligence Systems Logical Design (1)

Business Intelligence Transparencies 1. ©Pearson Education 2009 Objectives What business intelligence (BI) represents. The technologies associated with.

Pooja Sharma Shanti Ragathi Vaishnavi Kasala. BUSINESS BACKGROUND Lowe's started as a single hardware store in North Carolina in 1946 and since then has.

Managing Data for DSS II. Managing Data for DS Data Warehouse Common characteristics : –Database designed to meet analytical tasks comprising of data.

Business Intelligence Training Siemens Engineering Pakistan Zeeshan Shah December 07, 2009.

Copyright (c) 2014 Pearson Education, Inc. Introduction to DBMS.

MIS 451 Building Business Intelligence Systems Data Staging.

Last Updated : 26th may 2003 Center of Excellence Data Warehousing Introductionto Data Modeling.

I Copyright © 2006, Oracle. All rights reserved. Introduction.

1 Database Systems, 8 th Edition Star Schema Data modeling technique –Maps multidimensional decision support data into relational database Creates.

Building the Corporate Data Warehouse Pindaro Demertzoglou Data Resource Management.

Copyright © 2016 Pearson Education, Inc. Modern Database Management 12 th Edition Jeff Hoffer, Ramesh Venkataraman, Heikki Topi CHAPTER 9: DATA WAREHOUSING.

Data Warehouse/Data Mart It’s all about the data.

OLAP Theory-English version On-Line Analytical processing (Buisness Intelligence) Ing.Skorkovský,CSc Department of Corporate Economy Faculty of Economics.

© 2017 by McGraw-Hill Education. This proprietary material solely for authorized instructor use. Not authorized for sale or distribution in any manner.

Advanced Applied IT for Business 2

Building Data ware House

Lecture-34 DWH Implementation: Goal Driven Approach (2)

An Introduction to Data Warehousing

Data Warehouse Architecture

Data Warehouse Architecture

DWH – Dimesional Modeling

Data Warehousing Concepts

Presentation transcript:

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Dimensional Modeling I Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential Chair of Business

2 Technical Architecture Design Product Selection & Installation End-User Application Specification End-User Application Development The Business Dimensional Lifecycle Project Planning Business Requirement Definition Business Requirement Definition Deployment Maintenance and Growth Project Management Dimensional Modeling Physical Design Data Staging Design & Development

3 Outline Table structure, types, characteristics and terminology Design steps Dimensional models with varying types of fact and dimension tables

4 Introduction to Dimensional Modeling A logical data warehouse design technique Objectives of Dimensional Modeling: –Intuitive: easy to understand and query –High performance OLAP

5 Anatomy of Dimensional Models Facts or Fact Tables –Key – uniquely identifies a record –Attributes Dimensions or Dimension Tables –Keys –Attributes Connections –Between dimensions and facts –Cardinality: mostly one to many

6 Fact and Dimension Tables There are two types of tables in dimensional models: –Fact table: attributes in fact tables are measurements for analysis or main contents in reports. –Dimension table: attributes in dimension tables are constraints for the measurements or headers in reports. Dimensions Facts

7 Fact and Dimension Fact table Dimension tables

8 Facts and Dimensions How to identify facts and dimensions? –Top-down approach (Requirements Analysis): Report Sales in terms of – total amt, total qty or avg. price Report Sales by PRODUCT name and/or category name Report Sales by CUSTOMER name, city and/or or state Report using a combination of the measures and constraints –Bottom-up approach (Select from meta data of data sources) Characteristics of fact and dimension attributes

9 Facts and Dimensions CriteriaFact AttributesDimension Attributes PurposeMeasurements for reporting or analysis Constraints or qualifiers for the measurements Data typeAdditive or semi-additive quantitative data Textual, descriptive SizeLarger number of recordsSmaller number of records Reporting useMain report contentsRow or report headers ExamplesMeasurements for salesAbout time, people, departments, objects, geographic units