Prof. Bayer, DWH, CH. 4.5, SS 20021 Chapt.4.5 Modeling of Features of Dimensions Within a dimension hierarchy, elements at the same level may have different.

Slides:

Advertisements

Similar presentations

Vorlesung Datawarehousing Table of Contents Prof. Rudolf Bayer, Ph.D. Institut für Informatik, TUM SS 2002.

Advertisements

Alternative Database topology: The star schema

Spatial Information Systems (SIS) COMP Spatial queries and operations.

Dimensional Modeling – Part 2

Multidimensional Modeling MIS 497. What is multidimensional model? Logical view of the enterprise Logical view of the enterprise Shows main entities of.

Hachim Haddouti, adv. DBMS & DW CSC5301, Ch6 Chapter 6: The Big Dimensions Adv. DBMS & DW Hachim Haddouti.

Dr. M. Sulaiman Khan Dept. of Computer Science University of Liverpool 2010 COMP207: Data Mining Data Warehousing COMP207: Data Mining.

Typical phases of a project: 1.Schematic Design 2.Design Development 3.Construction Documents 4.Construction Administration.

Advanced Querying OLAP Part 2. Context OLAP systems for supporting decision making. Components: –Dimensions with hierarchies, –Measures, –Aggregation.

Chap8: Trends in DBMS 8.1 Database support for Field Entities 8.2 Content-based retrieval 8.3 Introduction to spatial data warehouses 8.4 Summary.

1 Lecture 10: More OLAP - Dimensional modeling

Microsoft SQL Server 2012 Analysis Services (SSAS) Reporting Services (SSRS)

Ch3 Data Warehouse part2 Dr. Bernard Chen Ph.D. University of Central Arkansas Fall 2009.

1 Data Warehousing and OLAP. 2 Data Warehousing & OLAP Defined in many different ways, but not rigorously.  A decision support database that is maintained.

Business Intelligence Instructor: Bajuna Salehe Web:

1 Basic concepts of On-Line Analytical processing DT211 /4.

Dr. Bernard Chen Ph.D. University of Central Arkansas

8/20/ Data Warehousing and OLAP. 2 Data Warehousing & OLAP Defined in many different ways, but not rigorously. Defined in many different ways, but.

Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals Presenter : Parminder Jeet Kaur Discussion Lead : Kailang.

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

Data Warehousing.

Ahsan Abdullah 1 Data Warehousing Lecture-12 Relational OLAP (ROLAP) Virtual University of Pakistan Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics.

IST722 Data Warehousing Business Intelligence Development with SQL Server Analysis Services and Excel 2013 Michael A. Fudge, Jr.

Querying Tree-Structured Data Using Dimension Graphs Dimitri Theodoratos (New Jersey Institute of Technology, USA) Theodore Dalamagas (National Techn.

Multi-Dimensional Databases & Online Analytical Processing This presentation uses some materials from: “ An Introduction to Multidimensional Database Technology,

1 Cube Computation and Indexes for Data Warehouses CPS Notes 7.

1 Data Warehousing Lecture-13 Dimensional Modeling (DM) Virtual University of Pakistan Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics Research.

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

Prof. Bayer, DWH, Ch.4, SS Chapter 4: Dimensions, Hierarchies, Operations, Modeling.

Using SQL to Query Oracle OLAP Cubes Bud Endress Director of Product Management, OLAP.

October 28, Data Warehouse Architecture Data Sources Operational DBs other sources Analysis Query Reports Data mining Front-End Tools OLAP Engine.

Decision Support and Date Warehouse Jingyi Lu. Outline Decision Support System OLAP vs. OLTP What is Date Warehouse? Dimensional Modeling Extract, Transform,

Fall 2013, Databases, Exam 2 Questions for the second exam. Your answers are due by Dec. 18 at 4PM. (This is the final exam slot.) And please type your.

Prof. R. BayerDWH, Ch. 3-1, SS Ch.3 The Multidimensional Data Model Ch. 3.1 Introduction to MDD Model Requirements: must support typical analyses,

Ch3 Data Warehouse Dr. Bernard Chen Ph.D. University of Central Arkansas Fall 2009.

Exercises for the Course Datawarehousing, SS 2002, Prof. R. Bayer, TUM Exercise Sheet 2 Exercise 4, Ch.2-5: Schema optimization Consider the data warehouse.

Prof. Bayer, DWH, Ch.5, SS Chapter 5. Indexing for DWH D1Facts D2.

© 1999 FORWISS General Research Report Implementation and Optimization Issues of the ROLAP Algebra F. Ramsak, M.S. (UIUC) Dr. V. Markl Prof. R. Bayer,

Data Warehousing Multidimensional Analysis

Data Mining Data Warehouses.

Data Warehousing (Kimball, Ch.5-12) Dr. Vairam Arunachalam School of Accountancy, MU.

DAY 21: MICROSOFT ACCESS – CHAPTER 5 MICROSOFT ACCESS – CHAPTER 6 MICROSOFT ACCESS – CHAPTER 7 Aliya Farheen October 29,2015.

OLAP On Line Analytic Processing. OLTP On Line Transaction Processing –support for ‘real-time’ processing of orders, bookings, sales –typically access.

Chapter 3.2 Basic Concepts of the MDD-Model

1 Exercise Sheet 3 Exercise 7.: ROLAP Algebra Assume that a fact table SalesCube has 3 hierarchies with attributes  ear , Month M, Productgroup P and.

Indexing OLAP Data Sunita Sarawagi Monowar Hossain York University.

1.2 Data Classification Qualitative Data consist of attributes, labels, or non-numerical entries. – Examples are bigger, color, names, etc. Quantitative.

Prof. Bayer, DWH, Ch.7, SS20021 Chapt. 7 Multidimensional Hierarchical Clustering Fig. 3.1 Hierarchies in the `Juice and More´ schema Year (3) Month (12)

Data warehousing for Profit Analysis By A Sai Krishna Geethika Lokanadham Mithun Rajanna KV Kumar.

The Need for Data Analysis 2 Managers track daily transactions to evaluate how the business is performing Strategies should be developed to meet organizational.

COMP 430 Intro. to Database Systems Denormalization & Dimensional Modeling.

Introduction to OLAP and Data Warehouse Assoc. Professor Bela Stantic September 2014 Database Systems.

DWH, Prof. Bayer, SS Caller Prefixsmallint100 Numberinteger10 7 Namestring Adress...string... Callee Prefixsmallint100 Numberinteger10 7 Namestring.

Learn about relations and their basic properties

Object-Oriented Programming Basics

Data warehouse and OLAP

3. Data storage and data structures in Warehouses

Data warehouse Design Using Oracle

Chapter 4: Dimensions, Hierarchies, Operations, Modeling

On Relationships Offering New Drill-across Possibilities

CSCI N207 Data Analysis Using Spreadsheet

Dr. Bernard Chen Ph.D. University of Central Arkansas Fall 2009

Jeff Rix Navya Karuturi Vineela Tatineni.

Dimensional Model January 16, 2003

Fundamentals of Data Cube & OLAP Operations

DWH – Dimesional Modeling

Slides based on those originally by : Parminder Jeet Kaur

Chapt. 7 Multidimensional Hierarchical Clustering

Ch. 16: Sweep-Zones Basic Question: Is it possible to compute nearest neighbors in expected time O(n*log(n)) ??? Basic Idea: Generalize sweep-lines to.

Chapter 11: Class Diagram

Presentation transcript:

Prof. Bayer, DWH, CH. 4.5, SS Chapt.4.5 Modeling of Features of Dimensions Within a dimension hierarchy, elements at the same level may have different attributes (properties). Consider the Product Groups (PG) Car: (cc, PS, cyl, fuel) Camera: (reflex, digital, focal length) TV: (screensize, audio, color, flat) etc. Such specific properties are called Features in DWH If all elements have the same attributes, modelling is trivial, within a common relation.

Prof. Bayer, DWH, CH. 4.5, SS Example of heterogeneous Hierarchy with Features Dimension Products BrandProd-GroupModel BMW Cars (cc, PS, cyl, fuel) Motorcycles Sony Cameras (reflex, digital, focal length) TVs (screensize, audio, color, flat) ComputersVaio Daimler-Chr Cars Buses Trucks

Prof. Bayer, DWH, CH. 4.5, SS Partial Schema for Features shows possesses P#,FN,F-Value P# Products belongs PG# Prod-Group Feature FN, Unit n n 1 n m m

Prof. Bayer, DWH, CH. 4.5, SS Auxiliary Query for Query Formulation to see the features of the product-group ‚Car‘: select FN, Unit from Prod-Group G, shows s, Feature F where G.name = ‚Car‘ and G.PG# = s.PG# and s.FN = F.FN i.e. semantic help for follow-up query

Prof. Bayer, DWH, CH. 4.5, SS „P# of engines with 2800 cc“ select P# from Posesses pos where pos.FN = ‚cc‘ and pos.F-Value = ‚2800‘

Prof. Bayer, DWH, CH. 4.5, SS Problem: F-values have specific Units and therefore types, e.g. cc, PS, audio, screensize, … How to code, represent F-Value? Ex: Cities belong to countries, which show features like: currency political system VAT-Rate Steuer-Hebesatz Exercise: construct detailed schema for this

Prof. Bayer, DWH, CH. 4.5, SS Month M# belongs_TD# Time shows_T Feature-T FN, unit Posesses_T D#, FN, F-Value P# Products Facts Posesses_P P#, FN, F-Value

Prof. Bayer, DWH, CH. 4.5, SS P# Products Facts Posesses_P P#, FN, F-Value Region belongs_P Feature-P FN, Unit Prod-Group PG# shows_P n n m k... m m

Prof. Bayer, DWH, CH. 4.5, SS Typical features for months: avg temperature avg sunshine avg beer consumption on holidays Production planing for beer and icecream

Prof. Bayer, DWH, CH. 4.5, SS Typical Query  Sales where screensize = 17  Brand = Sony  ( Year = 1999  Month = Dec  Year = 2000  Month = Jan) screensize  restriction on Product-Hierarchy Brand  restriction on Manufacture-Hierarchy or additional restriction on Product-Hierarchy Combine all restrictions to get one or a few Query-Boxes on Base-Cube. Retrieve Query-Boxes only from Base-Cube, perform 1. Post-filtering 2. aggregation Problem: Restrictions, computations based on F-Values? Be careful with type Incompatibilities!