Introduction: Modeling:

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Introduction: Modeling:"

Transcription

1 Introduction: In this lecture, we discuss the principles of dimensional modeling, in what way dimensional modeling is different from traditional entity relationship modeling, various types of schema models, concept of data mart and finally we conclude this lecture by highlighting various design steps associated with dimensional modeling. Modeling: Analytical requirements and subject orientation are the key differences between data warehouses and operational structures. To build Data Warehouse, we must map data to subject oriented information by identifying business subjects, relationship between subjects and different attributes that are needed to represent each subject. Of course modeling is an iterative process and tools exists today to assist the modeling. Relative Data sizes in a Data Warehouse: The slide shows how size of data differ in Data Warehouses. For instance, if we consider several years of data, such data we can classify as older detail data, current detail data, lightly summarized data and highly summarized data and so on. For instance in the sales data, we have sales detail data during and are scatted at various sources. This we consider as old detail data. Sales detail current data during This is called current data. Region wise weekly sales data for 4 years from Weekly sales by product and sub product for 4 years from Monthly sales data by product for Typical Data Warehouse: Slide shows typical data warehouse system. Data Warehouse is developed by using different databases. In this diagram there are three different levels. Top level is known as usage level of the data Warehouse. We can use the data in DW to generate MIS reports, adhoc reports. In fact you can as well do deeper analysis using data mining using the data warehouse data. Bottom layer consists of difference data sources, which we can as well denote as databases. Data in DW is either derived or selected from these data sources. Data sources are heterogeneous in nature. Middle level is called Data Warehouse. The important issue is how do we organize the data in Data Warehouse. In other words, how do we model the data in Data Warehouse is the topic we concentrate in this lecture.? Quick recap of Data Warehousing: As per the architecture of the Data Warehouse, we have different heterogeneous sources. These sources are used to select the data into the staging area, where we clean transform, validate and summarize or aggregate the data and finally populated the data into data warehouse as per the needs of business users. Data Warehouse is a SUBJECT ORIENTED, INTEGRATED, TIME-VARIANT, NON-VOLATILE collection of data enabling Management Decision Making. Actually the dimensional modeling starts after collecting the requirements of data design for data warehouse. There are several phases prior to dimensional modeling. Dimensional Modeling is an effective, efficient and successful technique to design Enterprise Data Warehouse and Distributed Data Mart, database schemas for maximizing the query performance. So the main modeling technique used while building the Data Warehouse is known as Dimensional modeling.

2 From Requirements to Design: There are several phases: Requirements gathering phase, requirements definition document, Information package, data design and finally Dimensional model. From the requirements gathering phase, the data is documented in detail in Requirements definition document. A set information package diagrams is one of the most essential document used for dimensional modeling. These basically used to show metrics, business dimensions, and hierarchies within individual business dimensions. The information package diagrams form the basis for the logical data design for Data Warehouse. The data design process results in a dimensional model. ER Model Vs Dimensional Model: Modeling Logical Model: For any kind of modeling, first of all we have to understand how to design a logical model. So logical model is representation of business problem without regard to imp tech and organ structure. From the database point, we generally use Entity relationship model to build a logical structure for a given application. The feature of logical model are complete, correct and consistent representation of business requirement, removal of redundancy, does not presuppose data granularity, not an implementation of the product and is independent of DBMS. The slide shows an example of ER model and left slide of the shows an ER diagram and RHS is a dimensional model. In ER diagram, we show entities and relationship among entities, cardinalities and keys. While modeling ER structure, we always consider efficiency and execution of the application into account. But if we observe Dimensional modeling, diagram shown on the RHS of the slide, we call this as Star Schema. Here dimensions are linked to central fact table to record the measures, which we call them as facts. Whenever we convert the logical model into physical model, we translate the specification what is logical represented into physical organization of is it be implemented. The main features considered while translating are optimized, efficient, buildable, robust and flexible are the key features are used while translating logical into physical structure. Dimensional Modeling: The process of building dimensional model is termed as Dimensional modeling. Dimensional modeling is a form of analytical design in which data is pre-classified as a fact or dimension. Such representation improves the performance by matching the data structure to the queries. The purpose of modeling this is to improve the performance by matching data structure to queries. Logical Data Modeling is the first step towards building the warehouse. Dimensional modeling is logical modeling design technique often used for data warehouses. It is a logical design technique to structure the business dimensions and metrics that are analyzed along these dimensions. Data is organized around business processes rather than business entities. In databases, we design databases by considering business entities into account. Data Modeling Approach: For any design process, requirements specification is important. What are the expectations from DW? How much ease user is interested to perform analysis? Based on the requirements how best we can model the data in DW is driven by data modeling in such a way that query performance is optimized. In view of above, dimensional modeling is building block in designing of DW. That is to say, it is an approach of choice for most of data Warehouse teams when designing a structure in such a

3 way that its is easy for end user to access the desired data for analysis. Entity Relationship Diagram: The slide shows an ER diagram. OLTP system design generally use ER Diagrams for modeling database. ERD consists basic entities, relationships, keys and cardinalities. Each entity is associated with key and several attributes associated with each entity. Different entities are connected thru relationship. Another aspect is cardinality? How each entity is associated with another entity. Metadata is also used while designing OLTP systems. We now discuss in what way ER Modeling is different from Dimensional modeling: In ER modeling there is no explicit allowance for time. This is One of the major influencing requirement for DW. The entity relationship model is commonly used in the design of relational databases where a database schema consists of a set of entities and relationships between the entities. Such a data model is appropriate for OLTP system. However, a data warehouse however requires a concise, subject oriented schema that facilitates online data analysis. Design of models for data warehouses are different from ERModels. In DW Most popular models are star schema, a snowflake schema or fact constellation schema. Main focus in dimensional modeling is business process and intended for read only access of data and data is de normalized and are time oriented. The salient differences between ER model and Dimensional model are given below in the form of table: ER Model 1. Optimized for insert, update and delete. 2. There would be more tables, less indexes, more joins. Dimensional Model 1. Optimized for querying. 2. There would be less tables, more indexes and less joins. 3. It s a conceptual modeling. 3. It is a logical model 4. It is very complex for comprehension 4. It is very easy to understand 5. Not easy to incorporate changes in the design. 6. Normalized. Tables are broken into many small tables. 7. Used for OLTP. 7. Used for OLAP. 8. There are no prescribed methods to handle changes in the entities 5. Flexible to accommodate changes in the design 6. De-Normalized. Many small tables are combined together. 8. There are prescribed methods to handle changes in dimensions We now look in detail about the dimensional modeling principles. Dimension modeling is an approach to identify and design appropriate data structures based on the user and business needs. We must first know what transaction, balance or event to model. Dimension model consists of two elements dimensions and facts or measures. What is a dimension? Dimension: A finite domain attribute defining minimum fact granularity. Attribute type generally alpha numeric in nature. Dimension tables determine the contextual background for the facts. The term DIMENSION represents a single category or perspective by which

4 associated FACTS are interpreted and understood. Dimension Table: A dimension table is a de-normalized relational table that includes a surrogate key and a set of attributes belonging to a hierarchy. E.g. "Store" is a perspective by which sales are understood. It is the answer to the question "Where did the sales occur?" A DIMENSION TABLE is a table which holds a list of attributes or qualities of the dimension most often used in queries and reports. Fact table is used to store business information (measures). E.g. The "Store" dimension can have attributes such as the street and block number, the city, the region and the country where it is located in addition to its name Features of Dimension Tables: Various features of dimension table are 1. Identified by answering the question: HOW DO BUSINESS PEOPLE DESCRIBE THE DATA THAT RESULTS FROM A BUSINESS PROCESS? 2. Entry into the fact table. 3. DE-NORMALIZED in order to reduce the number of joins in resulting queries 4. Attributes in Dimension table are generally STATIC, DESCRIPTIVE fields describing aspects of the dimension 5. Designed to hold IN-FREQUENT CHANGES to attribute values over time using SCD concepts 6. Used in GROUP BY SQL queries 7. Simplify SQL GROUP BY queries 8. Every column in the dimension table is TYPICALLY either the primary key or a dimensional attribute 9. Every non-key column in the dimension table is typically used in the GROUP BY clause of a SQL Query. 10. Every row in the DIMENSION TABLE represents a unique instance of that DIMENSION and has a unique identifier called the DIMENSION KEY. Star Schema: This schema consists of two components, i) Dimension tables and single central fact table. All the dimension tables are linked to single central fact table. Fact table consists of two components, i) set of keys and ii) set of measures called facts. All the keys are termed as surrogate keys and are linked for the respective dimension tables. Fact tables will have larger set of entries than dimensional table entries. Slides shows how to link four dimension tables with single central fact table. So in Star schema, single fact table is joined to set of dimension tables and it is simple and concise and the structure is symmetric, extensible and optimized. Grain of the star schema is the grain of the central fact table. Grain means at what level aggregation or summarization is computed and stored in the fact table in the

5 form of measures. Star Schema Modeling: There are two ways to model star schema. We can start with dimension tables and then design the fact table. The other way is design fact table first and then model dimension tables. We discuss with an example the star schema design based on the second approach. In fact table, each record contains primary key, which is concatenation of foreign keys to dimension tables and facts or measures uniquely identified by the primary key. In the table shown on the slide, each fact record represents one line item on an invoice to a customer. In order to design a fact table, first we must identify what kind of measures the user needs? Such measures can be identified by conducting user interviews and requirements gathering steps. Star is a way to model transactions. Each transaction is an invoice. If we want users to be able to drill down to the transactions that make up their reports, they may wish to see lists of the actual invoices. Sometimes invoice numbers are integral to this investigation. In star, we can model at any possible level of detail. For instance, we could store information at a weekly level, not a daily level. The level detail captured in a fact table is termed as the level granularity or the grain of the table. It is possible to recreate aggregate summary data from detail data but converse is not possible. That means we can nit recreate details from the summaries. Storing data at the most detailed level results in much higher disk requirements. It is always possible to re-create aggregate summary data from detail data, but we can not re-create details from summaries. That is why choosing grain is very important in fact table design. When size of grain is reduced, then we accommodate more data summaries and similarly when grain is small, we can store less summaries in the data warehouse when disk size is fixed. In order to provide better analysis, we need to carefully decide the grain of the fact table. The steps for fact table design are 1. Identify the measures that the user needs 2. Record in fact table contains primary key which is made up of concatenation of foreign keys to dimension tables. 3. Facts or measures are uniquely identified by primary key. Dimension Table Design: A fact table alone is useless in the star schema design in the sense that What does it mean that we sold 16 units of product_key 14 to customer_key 1714 on date_key ? It means nothing until we can decode these foreign key values. That is the reason why dimension tables are essential to convey meaning to each fact. Therefore Dimension table design provide meaning for each fact. The sales table is joined to at least one dimension table to provide a result set. Suppose if we want to perform a query on total sales for a particular product, we need to join sales table with product table. By plugging the name of the data elements into a sentence of the form ",, and < > broken down by < date element>,, < data element>, and < > ". Using this sentence, we can decide whether the data element is a measure or an element of a dimension. If the data element in question falls before the word "broken", then it is most

6 likely a measure. Otherwise, it is likely that an element of a dimension. Dimension tables are characterized by several general features. We discuss first the key feature called de normalization. Normalized and De-normalized structures: Dimension table are usually highly de normalized. That is, all the information regarding each dimension element appears on a single record in the dimension table. Two diagrams are shown on the slide known as normalized and de-normalized diagrams. In normalized design, a larger table is divided into set of smaller tables and linked via keys. This is not the case in case de-normalized design. In this case all the relationships captured are grouped into a set of single table. That is we join many source tables together into a single de normalized flat table in de-normalized table. Each record in this table fully describe a dimension elements. When loading dimension tables, we join many source tables together to put the result into one, flat, de normalized table. Each record in this table fully describes a dimension element. That is to say, we are pre-joining tables together, satisfying query time resource requirements with load time resources. Normalized and de normalized designs for the same data ( about products) are shown in the slide. Features of Dimension Table: Wide: Compared to tables used in traditional database applications, dimension tables are much wider. Here wide mean that it has a lot of columns. Wider columns provide more descriptive information. Wide dimension tables provide more attributes that can be viewed and summarized. Short: Dimension tables are in general far shorter than fact tables. For instance, each product may be sold thousands of times, and thus appear in thousands of facts records, each appears in only once in dimension record. Oracle can join very easily a short table with a huge table, when the relationship between the two is a foreign key based. Hence short dimension tables are very critical. Hence, in the presence of joins, the star schema design uses relatively less number of resources as compared to many joins in other modeling approaches. Use of Surrogate keys: A key with no independent of business meaning is termed as a Wide: Compared to tables used in traditional database applications, dimension tables are much wider. Here wide mean that it has a lot of columns. Wider columns provide more descriptive information. Wide dimension tables provide more attributes that can be viewed and summarized. Surrogate key. These are usually just a series of sequential numbers assigned to be the primary key for a table. These are useless for end users and are never queried by the end users. In the case of fact-dimension table relationships, surrogate keys are used to provide very efficient relationships between these tables. Creating and working with surrogate keys is more beneficial than using the business keys. We can capture dimension history by inserting multiple rows into dimension tables with the business key but with different information on them. The other benefit is it provide better join performance than business keys. For example, if the date dimension table contains five calendar years of data, then it contains approximately 1826 rows. Oracle can store these number in 2 bytes or less. Instead if we use the calendar date as the key of the table, then

7 we need more storage space for each value. Because of this Oracle has to spend more time when this table is joined to the fact table. Links to source tables: These links aid the dimension table update process. Additional date field and active flag fields: Additional date field will help primarily with system maintenance, rather than end user queries. Date filed indicate when it was last update or when the record is inserted etc. They help in cases when the data is audited. Active flag field is useful for dimension table. This flied indicate which is the current definition of dimension element. Dimension Hierarchies: The simple model we will use to demonstrate the various design alternatives is composed of three dimensions. Only two are shown, Store and Product. The third, Time, is composed of the following attribute hierarchy: date -> month -> quarter -> year. The Store dimension has an attribute hierarchy of store -> district -> region. Products is composed of products -> brand -> manufacturer. Based on this simple model, we can see that the granularity of data is products sold in stores by day.

Data Warehousing Concepts

Data Warehousing Concepts Data Warehousing Concepts JB Software and Consulting Inc 1333 McDermott Drive, Suite 200 Allen, TX 75013. [[[[[ DATA WAREHOUSING What is a Data Warehouse? Decision Support Systems (DSS), provides an analysis

More information

Basics of Dimensional Modeling

Basics of Dimensional Modeling Basics of Dimensional Modeling Data warehouse and OLAP tools are based on a dimensional data model. A dimensional model is based on dimensions, facts, cubes, and schemas such as star and snowflake. Dimensional

More information

Copyright 2007 Ramez Elmasri and Shamkant B. Navathe. Slide 29-1

Copyright 2007 Ramez Elmasri and Shamkant B. Navathe. Slide 29-1 Slide 29-1 Chapter 29 Overview of Data Warehousing and OLAP Chapter 29 Outline Purpose of Data Warehousing Introduction, Definitions, and Terminology Comparison with Traditional Databases Characteristics

More information

1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing

1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing 1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing 2. What is a Data warehouse a. A database application

More information

BUILDING BLOCKS OF DATAWAREHOUSE. G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT

BUILDING BLOCKS OF DATAWAREHOUSE. G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT BUILDING BLOCKS OF DATAWAREHOUSE G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT 1 Data Warehouse Subject Oriented Organized around major subjects, such as customer, product, sales. Focusing on

More information

When to consider OLAP?

When to consider OLAP? When to consider OLAP? Author: Prakash Kewalramani Organization: Evaltech, Inc. Evaltech Research Group, Data Warehousing Practice. Date: 03/10/08 Email: erg@evaltech.com Abstract: Do you need an OLAP

More information

Designing a Dimensional Model

Designing a Dimensional Model Designing a Dimensional Model Erik Veerman Atlanta MDF member SQL Server MVP, Microsoft MCT Mentor, Solid Quality Learning Definitions Data Warehousing A subject-oriented, integrated, time-variant, and

More information

DATA WAREHOUSING AND OLAP TECHNOLOGY

DATA WAREHOUSING AND OLAP TECHNOLOGY DATA WAREHOUSING AND OLAP TECHNOLOGY Manya Sethi MCA Final Year Amity University, Uttar Pradesh Under Guidance of Ms. Shruti Nagpal Abstract DATA WAREHOUSING and Online Analytical Processing (OLAP) are

More information

Week 3 lecture slides

Week 3 lecture slides Week 3 lecture slides Topics Data Warehouses Online Analytical Processing Introduction to Data Cubes Textbook reference: Chapter 3 Data Warehouses A data warehouse is a collection of data specifically

More information

Lost in Space? Methodology for a Guided Drill-Through Analysis Out of the Wormhole

Lost in Space? Methodology for a Guided Drill-Through Analysis Out of the Wormhole Paper BB-01 Lost in Space? Methodology for a Guided Drill-Through Analysis Out of the Wormhole ABSTRACT Stephen Overton, Overton Technologies, LLC, Raleigh, NC Business information can be consumed many

More information

OLAP and OLTP. AMIT KUMAR BINDAL Associate Professor M M U MULLANA

OLAP and OLTP. AMIT KUMAR BINDAL Associate Professor M M U MULLANA OLAP and OLTP AMIT KUMAR BINDAL Associate Professor Databases Databases are developed on the IDEA that DATA is one of the critical materials of the Information Age Information, which is created by data,

More information

DATA WAREHOUSE AND OLAP TECHNOLOGIES. Outline. Data Warehouse Data Warehouse OLAP. A data warehouse is a:

DATA WAREHOUSE AND OLAP TECHNOLOGIES. Outline. Data Warehouse Data Warehouse OLAP. A data warehouse is a: DATA WAREHOUSE AND OLAP TECHNOLOGIES Keep order, and the order shall save thee. Latin maxim Outline 2 Data Warehouse Definition Architecture OLAP Multidimensional data model OLAP cube computing Data Warehouse

More information

Data Warehouse: Introduction

Data Warehouse: Introduction Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of base and data mining group,

More information

Data W a Ware r house house and and OLAP Week 5 1

Data W a Ware r house house and and OLAP Week 5 1 Data Warehouse and OLAP Week 5 1 Midterm I Friday, March 4 Scope Homework assignments 1 4 Open book Team Homework Assignment #7 Read pp. 121 139, 146 150 of the text book. Do Examples 3.8, 3.10 and Exercise

More information

Data Warehouse design

Data Warehouse design Data Warehouse design Design of Enterprise Systems University of Pavia 11/11/2013-1- Data Warehouse design DATA MODELLING - 2- Data Modelling Important premise Data warehouses typically reside on a RDBMS

More information

ETL Process in Data Warehouse. G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT

ETL Process in Data Warehouse. G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT ETL Process in Data Warehouse G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT Outline ETL Extraction Transformation Loading ETL Overview Extraction Transformation Loading ETL To get data out of

More information

Introduction to Data Warehousing. Ms Swapnil Shrivastava swapnil@konark.ncst.ernet.in

Introduction to Data Warehousing. Ms Swapnil Shrivastava swapnil@konark.ncst.ernet.in Introduction to Data Warehousing Ms Swapnil Shrivastava swapnil@konark.ncst.ernet.in Necessity is the mother of invention Why Data Warehouse? Scenario 1 ABC Pvt Ltd is a company with branches at Mumbai,

More information

Data warehouse Architectures and processes

Data warehouse Architectures and processes Database and data mining group, Data warehouse Architectures and processes DATA WAREHOUSE: ARCHITECTURES AND PROCESSES - 1 Database and data mining group, Data warehouse architectures Separation between

More information

Data Warehousing and Data Mining

Data Warehousing and Data Mining Data Warehousing and Data Mining Part I: Data Warehousing Gao Cong gaocong@cs.aau.dk Slides adapted from Man Lung Yiu and Torben Bach Pedersen Course Structure Business intelligence: Extract knowledge

More information

Introduction to Databases, Fall 2004 IT University of Copenhagen. Lecture 6, part 2: OLAP and data cubes. October 8, Lecturer: Rasmus Pagh

Introduction to Databases, Fall 2004 IT University of Copenhagen. Lecture 6, part 2: OLAP and data cubes. October 8, Lecturer: Rasmus Pagh Introduction to Databases, Fall 2004 IT University of Copenhagen Lecture 6, part 2: OLAP and data cubes October 8, 2004 Lecturer: Rasmus Pagh Today s lecture, part II Information integration. On-Line Analytical

More information

Introduction. Introduction to Data Warehousing

Introduction. Introduction to Data Warehousing Introduction to Data Warehousing Pasquale LOPS Gestione della Conoscenza d Impresa A.A. 2003-2004 Introduction Data warehousing and decision support have given rise to a new class of databases. Design

More information

Demystified CONTENTS Acknowledgments xvii Introduction xix CHAPTER 1 Database Fundamentals CHAPTER 2 Exploring Relational Database Components

Demystified CONTENTS Acknowledgments xvii Introduction xix CHAPTER 1 Database Fundamentals CHAPTER 2 Exploring Relational Database Components Acknowledgments xvii Introduction xix CHAPTER 1 Database Fundamentals 1 Properties of a Database 1 The Database Management System (DBMS) 2 Layers of Data Abstraction 3 Physical Data Independence 5 Logical

More information

www.dotnetsparkles.wordpress.com

www.dotnetsparkles.wordpress.com Database Design Considerations Designing a database requires an understanding of both the business functions you want to model and the database concepts and features used to represent those business functions.

More information

Sizing Logical Data in a Data Warehouse A Consistent and Auditable Approach

Sizing Logical Data in a Data Warehouse A Consistent and Auditable Approach 2006 ISMA Conference 1 Sizing Logical Data in a Data Warehouse A Consistent and Auditable Approach Priya Lobo CFPS Satyam Computer Services Ltd. 69, Railway Parallel Road, Kumarapark West, Bangalore 560020,

More information

DATA WAREHOUSING - OLAP

DATA WAREHOUSING - OLAP http://www.tutorialspoint.com/dwh/dwh_olap.htm DATA WAREHOUSING - OLAP Copyright tutorialspoint.com Online Analytical Processing Server OLAP is based on the multidimensional data model. It allows managers,

More information

Data Warehouse Snowflake Design and Performance Considerations in Business Analytics

Data Warehouse Snowflake Design and Performance Considerations in Business Analytics Journal of Advances in Information Technology Vol. 6, No. 4, November 2015 Data Warehouse Snowflake Design and Performance Considerations in Business Analytics Jiangping Wang and Janet L. Kourik Walker

More information

PowerDesigner WarehouseArchitect The Model for Data Warehousing Solutions. A Technical Whitepaper from Sybase, Inc.

PowerDesigner WarehouseArchitect The Model for Data Warehousing Solutions. A Technical Whitepaper from Sybase, Inc. PowerDesigner WarehouseArchitect The Model for Data Warehousing Solutions A Technical Whitepaper from Sybase, Inc. Table of Contents Section I: The Need for Data Warehouse Modeling.....................................4

More information

Logical Design of Data Warehouses

Logical Design of Data Warehouses Logical Design of Data Warehouses Krzysztof Dembczyński Institute of Computing Science Laboratory of Intelligent Decision Support Systems Politechnika Poznańska (Poznań University of Technology) Software

More information

Modeling: Operational, Data Warehousing & Data Marts

Modeling: Operational, Data Warehousing & Data Marts Course Description Modeling: Operational, Data Warehousing & Data Marts Operational DW DMs GENESEE ACADEMY, LLC 2013 Course Developed by: Hans Hultgren DATA MODELING IMMERSION Modeling: Operational, Data

More information

Chapter 7 Multidimensional Data Modeling (MDDM)

Chapter 7 Multidimensional Data Modeling (MDDM) Chapter 7 Multidimensional Data Modeling (MDDM) Fundamentals of Business Analytics Learning Objectives and Learning Outcomes Learning Objectives 1. To assess the capabilities of OLTP and OLAP systems 2.

More information

Data warehousing with PostgreSQL

Data warehousing with PostgreSQL Data warehousing with PostgreSQL Gabriele Bartolini http://www.2ndquadrant.it/ European PostgreSQL Day 2009 6 November, ParisTech Telecom, Paris, France Audience

More information

Unlock your data for fast insights: dimensionless modeling with in-memory column store. By Vadim Orlov

Unlock your data for fast insights: dimensionless modeling with in-memory column store. By Vadim Orlov Unlock your data for fast insights: dimensionless modeling with in-memory column store By Vadim Orlov I. DIMENSIONAL MODEL Dimensional modeling (also known as star or snowflake schema) was pioneered by

More information

DATA WAREHOUSE CONCEPTS DATA WAREHOUSE DEFINITIONS

DATA WAREHOUSE CONCEPTS DATA WAREHOUSE DEFINITIONS DATA WAREHOUSE CONCEPTS A fundamental concept of a data warehouse is the distinction between data and information. Data is composed of observable and recordable facts that are often found in operational

More information

Understanding Data Warehousing. [by Alex Kriegel]

Understanding Data Warehousing. [by Alex Kriegel] Understanding Data Warehousing 2008 [by Alex Kriegel] Things to Discuss Who Needs a Data Warehouse? OLTP vs. Data Warehouse Business Intelligence Industrial Landscape Which Data Warehouse: Bill Inmon vs.

More information

Migrating a Discoverer System to Oracle Business Intelligence Enterprise Edition

Migrating a Discoverer System to Oracle Business Intelligence Enterprise Edition Migrating a Discoverer System to Oracle Business Intelligence Enterprise Edition Milena Gerova President Bulgarian Oracle User Group mgerova@technologica.com Who am I Project Manager in TechnoLogica Ltd

More information

14. Data Warehousing & Data Mining

14. Data Warehousing & Data Mining 14. Data Warehousing & Data Mining Data Warehousing Concepts Decision support is key for companies wanting to turn their organizational data into an information asset Data Warehouse "A subject-oriented,

More information

Overview of Data Warehousing and OLAP

Overview of Data Warehousing and OLAP Overview of Data Warehousing and OLAP Chapter 28 March 24, 2008 ADBS: DW 1 Chapter Outline What is a data warehouse (DW) Conceptual structure of DW Why separate DW Data modeling for DW Online Analytical

More information

Part 22. Data Warehousing

Part 22. Data Warehousing Part 22 Data Warehousing The Decision Support System (DSS) Tools to assist decision-making Used at all levels in the organization Sometimes focused on a single area Sometimes focused on a single problem

More information

Terminology and Definitions. Data Warehousing and OLAP. Data Warehouse characteristics. Data Warehouse Types. Typical DW Implementation

Terminology and Definitions. Data Warehousing and OLAP. Data Warehouse characteristics. Data Warehouse Types. Typical DW Implementation Data Warehousing and OLAP Topics Introduction Data modelling in data warehouses Building data warehouses View Maintenance OLAP and data mining Reading Lecture Notes Elmasriand Navathe, Chapter 26 Ozsu

More information

Dimensional Modeling

Dimensional Modeling Dimensional Modeling Krzysztof Dembczyński Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland Software Development Technologies Master studies, first semester

More information

The Benefits of Data Modeling in Data Warehousing

The Benefits of Data Modeling in Data Warehousing WHITE PAPER: THE BENEFITS OF DATA MODELING IN DATA WAREHOUSING The Benefits of Data Modeling in Data Warehousing NOVEMBER 2008 Table of Contents Executive Summary 1 SECTION 1 2 Introduction 2 SECTION 2

More information

Lection 3-4 WAREHOUSING

Lection 3-4 WAREHOUSING Lection 3-4 DATA WAREHOUSING Learning Objectives Understand d the basic definitions iti and concepts of data warehouses Understand data warehousing architectures Describe the processes used in developing

More information

CHAPTER 3. Data Warehouses and OLAP

CHAPTER 3. Data Warehouses and OLAP CHAPTER 3 Data Warehouses and OLAP 3.1 Data Warehouse 3.2 Differences between Operational Systems and Data Warehouses 3.3 A Multidimensional Data Model 3.4Stars, snowflakes and Fact Constellations: 3.5

More information

Mario Guarracino. Data warehousing

Mario Guarracino. Data warehousing Data warehousing Introduction Since the mid-nineties, it became clear that the databases for analysis and business intelligence need to be separate from operational. In this lecture we will review the

More information

Data Warehousing. Jens Teubner, TU Dortmund jens.teubner@cs.tu-dortmund.de. Winter 2015/16. Jens Teubner Data Warehousing Winter 2015/16 1

Data Warehousing. Jens Teubner, TU Dortmund jens.teubner@cs.tu-dortmund.de. Winter 2015/16. Jens Teubner Data Warehousing Winter 2015/16 1 Jens Teubner Data Warehousing Winter 2015/16 1 Data Warehousing Jens Teubner, TU Dortmund jens.teubner@cs.tu-dortmund.de Winter 2015/16 Jens Teubner Data Warehousing Winter 2015/16 13 Part II Overview

More information

Data Warehousing Systems: Foundations and Architectures

Data Warehousing Systems: Foundations and Architectures Data Warehousing Systems: Foundations and Architectures Il-Yeol Song Drexel University, http://www.ischool.drexel.edu/faculty/song/ SYNONYMS None DEFINITION A data warehouse (DW) is an integrated repository

More information

Week 13: Data Warehousing. Warehousing

Week 13: Data Warehousing. Warehousing 1 Week 13: Data Warehousing Warehousing Growing industry: $8 billion in 1998 Range from desktop to huge: Walmart: 900-CPU, 2,700 disk, 23TB Teradata system Lots of buzzwords, hype slice & dice, rollup,

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Foundations of Business Intelligence: Databases and Information Management Content Problems of managing data resources in a traditional file environment Capabilities and value of a database management

More information

Data Warehouse Logical Design. Letizia Tanca Politecnico di Milano (with the kind support of Rosalba Rossato)

Data Warehouse Logical Design. Letizia Tanca Politecnico di Milano (with the kind support of Rosalba Rossato) Data Warehouse Logical Design Letizia Tanca Politecnico di Milano (with the kind support of Rosalba Rossato) Data Mart logical models MOLAP (Multidimensional On-Line Analytical Processing) stores data

More information

Lecture Data Warehouse Systems

Lecture Data Warehouse Systems Lecture Data Warehouse Systems Eva Zangerle SS 2013 PART A: Architecture Chapter 1: Motivation and Definitions Motivation Goal: to build an operational general view on a company to support decisions in

More information

META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING

META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING Ramesh Babu Palepu 1, Dr K V Sambasiva Rao 2 Dept of IT, Amrita Sai Institute of Science & Technology 1 MVR College of Engineering 2 asistithod@gmail.com

More information

Balancing of Business Data Model and Transactional Data Model

Balancing of Business Data Model and Transactional Data Model Balancing of Business Data Model and ransactional Data Model Y. Sri Veni, Y. Pratap Abstract he business information oriented system and enterprise data both are equally essential and important for an

More information

Application Of Business Intelligence In Agriculture 2020 System to Improve Efficiency And Support Decision Making in Investments.

Application Of Business Intelligence In Agriculture 2020 System to Improve Efficiency And Support Decision Making in Investments. Application Of Business Intelligence In Agriculture 2020 System to Improve Efficiency And Support Decision Making in Investments Anuraj Gupta Department of Electronics and Communication Oriental Institute

More information

INFORMATICA POWERCENTER TRAINING

INFORMATICA POWERCENTER TRAINING INFORMATICA POWERCENTER 9.6.1 TRAINING POWERCENTER 9.6.1 DURATION 35hrs AVAILABLE BATCHES WEEKDAYS (7.30AM TO 8.30AM) & WEEKENDS (10AM TO 1PM) MODE OF TRAINING AVAILABLE ONLINE INSTRUCTOR LED CLASSROOM

More information

BUSINESS INTELLIGENCE

BUSINESS INTELLIGENCE BUSINESS INTELLIGENCE Understanding Oracle BI Components and Repository Modeling Basics by Abhinav Banerjee 2 INTRODUCTION The importance of Business Intelligence (BI) is rising by the day. BI systems,

More information

Web Contents for Database Design Book

Web Contents for Database Design Book Web Contents for Database Design Book Link to the Authors Web Site The Perpetual Technologies web site, http://www.perptech.com, contains information about relational database technology, with specialization

More information

TRANSFORMING YOUR BUSINESS

TRANSFORMING YOUR BUSINESS September, 21 2012 TRANSFORMING YOUR BUSINESS PROCESS INTO DATA MODEL Prasad Duvvuri AST Corporation Agenda First Step Analysis Data Modeling End Solution Wrap Up FIRST STEP It Starts With.. What is the

More information

Data Warehousing and OLAP

Data Warehousing and OLAP 1 Data Warehousing and OLAP Hector Garcia-Molina Stanford University Warehousing Growing industry: $8 billion in 1998 Range from desktop to huge: Walmart: 900-CPU, 2,700 disk, 23TB Teradata system Lots

More information

Database Design Patterns. Winter 2006-2007 Lecture 24

Database Design Patterns. Winter 2006-2007 Lecture 24 Database Design Patterns Winter 2006-2007 Lecture 24 Trees and Hierarchies Many schemas need to represent trees or hierarchies of some sort Common way of representing trees: An adjacency list model Each

More information

www.ijreat.org Published by: PIONEER RESEARCH & DEVELOPMENT GROUP (www.prdg.org) 28

www.ijreat.org Published by: PIONEER RESEARCH & DEVELOPMENT GROUP (www.prdg.org) 28 Data Warehousing - Essential Element To Support Decision- Making Process In Industries Ashima Bhasin 1, Mr Manoj Kumar 2 1 Computer Science Engineering Department, 2 Associate Professor, CSE Abstract SGT

More information

Module 1: Introduction to Data Warehousing and OLAP

Module 1: Introduction to Data Warehousing and OLAP Raw Data vs. Business Information Module 1: Introduction to Data Warehousing and OLAP Capturing Raw Data Gathering data recorded in everyday operations Deriving Business Information Deriving meaningful

More information

Sterling Business Intelligence

Sterling Business Intelligence Sterling Business Intelligence Concepts Guide Release 9.0 March 2010 Copyright 2009 Sterling Commerce, Inc. All rights reserved. Additional copyright information is located on the documentation library:

More information

Data Warehousing: Data Models and OLAP operations. By Kishore Jaladi kishorejaladi@yahoo.com

Data Warehousing: Data Models and OLAP operations. By Kishore Jaladi kishorejaladi@yahoo.com Data Warehousing: Data Models and OLAP operations By Kishore Jaladi kishorejaladi@yahoo.com Topics Covered 1. Understanding the term Data Warehousing 2. Three-tier Decision Support Systems 3. Approaches

More information

Mastering Data Warehouse Aggregates. Solutions for Star Schema Performance

Mastering Data Warehouse Aggregates. Solutions for Star Schema Performance Brochure More information from http://www.researchandmarkets.com/reports/2248199/ Mastering Data Warehouse Aggregates. Solutions for Star Schema Performance Description: - This is the first book to provide

More information

Data Warehousing, OLAP, and Data Mining

Data Warehousing, OLAP, and Data Mining Data Warehousing, OLAP, and Marek Rychly mrychly@strathmore.edu Strathmore University, @ilabafrica & Brno University of Technology, Faculty of Information Technology Advanced Databases and Enterprise Systems

More information

Data Warehousing and OLAP Technology for Knowledge Discovery

Data Warehousing and OLAP Technology for Knowledge Discovery 542 Data Warehousing and OLAP Technology for Knowledge Discovery Aparajita Suman Abstract Since time immemorial, libraries have been generating services using the knowledge stored in various repositories

More information

Data Warehousing. Read chapter 13 of Riguzzi et al Sistemi Informativi. Slides derived from those by Hector Garcia-Molina

Data Warehousing. Read chapter 13 of Riguzzi et al Sistemi Informativi. Slides derived from those by Hector Garcia-Molina Data Warehousing Read chapter 13 of Riguzzi et al Sistemi Informativi Slides derived from those by Hector Garcia-Molina What is a Warehouse? Collection of diverse data subject oriented aimed at executive,

More information

Presented by: Jose Chinchilla, MCITP

Presented by: Jose Chinchilla, MCITP Presented by: Jose Chinchilla, MCITP Jose Chinchilla MCITP: Database Administrator, SQL Server 2008 MCITP: Business Intelligence SQL Server 2008 Customers & Partners Current Positions: President, Agile

More information

Modeling of Data Warehouses

Modeling of Data Warehouses Modeling of Data Warehouses Krzysztof Dembczyński Institute of Computing Science Laboratory of Intelligent Decision Support Systems Politechnika Poznańska (Poznań University of Technology) Intelligent

More information

Trivadis White Paper. Comparison of Data Modeling Methods for a Core Data Warehouse. Dani Schnider Adriano Martino Maren Eschermann

Trivadis White Paper. Comparison of Data Modeling Methods for a Core Data Warehouse. Dani Schnider Adriano Martino Maren Eschermann Trivadis White Paper Comparison of Data Modeling Methods for a Core Data Warehouse Dani Schnider Adriano Martino Maren Eschermann June 2014 Table of Contents 1. Introduction... 3 2. Aspects of Data Warehouse

More information

LITERATURE SURVEY ON DATA WAREHOUSE AND ITS TECHNIQUES

LITERATURE SURVEY ON DATA WAREHOUSE AND ITS TECHNIQUES LITERATURE SURVEY ON DATA WAREHOUSE AND ITS TECHNIQUES MUHAMMAD KHALEEL (0912125) SZABIST KARACHI CAMPUS Abstract. Data warehouse and online analytical processing (OLAP) both are core component for decision

More information

A Critical Review of Data Warehouse

A Critical Review of Data Warehouse Global Journal of Business Management and Information Technology. Volume 1, Number 2 (2011), pp. 95-103 Research India Publications http://www.ripublication.com A Critical Review of Data Warehouse Sachin

More information

Alexander Nikov. 5. Database Systems and Managing Data Resources. Learning Objectives. RR Donnelley Tries to Master Its Data

Alexander Nikov. 5. Database Systems and Managing Data Resources. Learning Objectives. RR Donnelley Tries to Master Its Data INFO 1500 Introduction to IT Fundamentals 5. Database Systems and Managing Data Resources Learning Objectives 1. Describe how the problems of managing data resources in a traditional file environment are

More information

GRADUATE ENTREPRENEUR ANALYTICAL REPORTS (GEAR) USING DATA WAREHOUSE MODEL: A CASE STUDY AT CEDI, UNIVERSITI UTARA MALAYSIA (UUM).

GRADUATE ENTREPRENEUR ANALYTICAL REPORTS (GEAR) USING DATA WAREHOUSE MODEL: A CASE STUDY AT CEDI, UNIVERSITI UTARA MALAYSIA (UUM). GRADUATE ENTREPRENEUR ANALYTICAL REPORTS (GEAR) USING DATA WAREHOUSE MODEL: A CASE STUDY AT CEDI, UNIVERSITI UTARA MALAYSIA (UUM). Muhamad Shahbani Abu Bakar 1 and Hayder Naser Khraibet. 1 INTRODUCTION

More information

This tutorial is designed for those who want to learn the basics of OBIEE and take advantage of its features to develop quality BI reports.

This tutorial is designed for those who want to learn the basics of OBIEE and take advantage of its features to develop quality BI reports. About the Tutorial Oracle Business Intelligence Enterprise Edition (OBIEE) is a Business Intelligence (BI) tool by Oracle Corporation. Its proven architecture and common infrastructure producing and delivering

More information

KDOT s Spatially Enabled Data Warehouse. Paul Bayless KDOT Data Warehouse Manager and Bill Schuman GeoDecisions Project Manager

KDOT s Spatially Enabled Data Warehouse. Paul Bayless KDOT Data Warehouse Manager and Bill Schuman GeoDecisions Project Manager KDOT s Spatially Enabled Data Warehouse Paul Bayless KDOT Data Warehouse Manager and Bill Schuman GeoDecisions Project Manager Goals of the Session Describe what a data warehouse is and why it is of value

More information

Optimizing Your Data Warehouse Design for Superior Performance

Optimizing Your Data Warehouse Design for Superior Performance Optimizing Your Data Warehouse Design for Superior Performance Lester Knutsen, President and Principal Database Consultant Advanced DataTools Corporation Session 2100A The Problem The database is too complex

More information

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP Data Warehousing and End-User Access Tools OLAP and Data Mining Accompanying growth in data warehouses is increasing demands for more powerful access tools providing advanced analytical capabilities. Key

More information

Dimensional Modeling for Data Warehouse

Dimensional Modeling for Data Warehouse Modeling for Data Warehouse Umashanker Sharma, Anjana Gosain GGS, Indraprastha University, Delhi Abstract Many surveys indicate that a significant percentage of DWs fail to meet business objectives or

More information

Data Warehouse Architecture

Data Warehouse Architecture Anwendungssoftwares a -Warehouse-, -Mining- und OLAP-Technologien Warehouse Architecture Overview Warehouse Architecture Sources and Quality Mart Federated Information Systems Operational Store Metadata

More information

ETL TESTING TRAINING

ETL TESTING TRAINING ETL TESTING TRAINING DURATION 35hrs AVAILABLE BATCHES WEEKDAYS (6.30AM TO 7.30AM) & WEEKENDS (6.30pm TO 8pm) MODE OF TRAINING AVAILABLE ONLINE INSTRUCTOR LED CLASSROOM TRAINING (MARATHAHALLI, BANGALORE)

More information

Deductive Data Warehouses and Aggregate (Derived) Tables

Deductive Data Warehouses and Aggregate (Derived) Tables Deductive Data Warehouses and Aggregate (Derived) Tables Kornelije Rabuzin, Mirko Malekovic, Mirko Cubrilo Faculty of Organization and Informatics University of Zagreb Varazdin, Croatia {kornelije.rabuzin,

More information

Enterprise Data Warehouse (EDW) UC Berkeley Peter Cava Manager Data Warehouse Services October 5, 2006

Enterprise Data Warehouse (EDW) UC Berkeley Peter Cava Manager Data Warehouse Services October 5, 2006 Enterprise Data Warehouse (EDW) UC Berkeley Peter Cava Manager Data Warehouse Services October 5, 2006 What is a Data Warehouse? A data warehouse is a subject-oriented, integrated, time-varying, non-volatile

More information

COURSE OUTLINE. Track 1 Advanced Data Modeling, Analysis and Design

COURSE OUTLINE. Track 1 Advanced Data Modeling, Analysis and Design COURSE OUTLINE Track 1 Advanced Data Modeling, Analysis and Design TDWI Advanced Data Modeling Techniques Module One Data Modeling Concepts Data Models in Context Zachman Framework Overview Levels of Data

More information

CHAPTER 4 Data Warehouse Architecture

CHAPTER 4 Data Warehouse Architecture CHAPTER 4 Data Warehouse Architecture 4.1 Data Warehouse Architecture 4.2 Three-tier data warehouse architecture 4.3 Types of OLAP servers: ROLAP versus MOLAP versus HOLAP 4.4 Further development of Data

More information

Outline. Data Warehousing. What is a Warehouse? What is a Warehouse?

Outline. Data Warehousing. What is a Warehouse? What is a Warehouse? Outline Data Warehousing What is a data warehouse? Why a warehouse? Models & operations Implementing a warehouse 2 What is a Warehouse? Collection of diverse data subject oriented aimed at executive, decision

More information

Chapter 5. Warehousing, Data Acquisition, Data. Visualization

Chapter 5. Warehousing, Data Acquisition, Data. Visualization Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization 5-1 Learning Objectives

More information

Trends in Data Warehouse Data Modeling: Data Vault and Anchor Modeling

Trends in Data Warehouse Data Modeling: Data Vault and Anchor Modeling Trends in Data Warehouse Data Modeling: Data Vault and Anchor Modeling Thanks for Attending! Roland Bouman, Leiden the Netherlands MySQL AB, Sun, Strukton, Pentaho (1 nov) Web- and Business Intelligence

More information

DMM301 Benefits and Patterns of a Logical Data Warehouse with SAP BW on SAP HANA

DMM301 Benefits and Patterns of a Logical Data Warehouse with SAP BW on SAP HANA DMM301 Benefits and Patterns of a Logical Data Warehouse with SAP BW on SAP HANA Ulrich Christ/Product Management SAP EDW (BW/HANA) Public Disclaimer This presentation outlines our general product direction

More information

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Oman College of Management and Technology Course 803401 DSS Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization CS/MIS Department Information Sharing

More information

Chapter 6 Basics of Data Integration. Fundamentals of Business Analytics RN Prasad and Seema Acharya

Chapter 6 Basics of Data Integration. Fundamentals of Business Analytics RN Prasad and Seema Acharya Chapter 6 Basics of Data Integration Fundamentals of Business Analytics Learning Objectives and Learning Outcomes Learning Objectives 1. Concepts of data integration 2. Needs and advantages of using data

More information

Enterprise Solutions. Data Warehouse & Business Intelligence Chapter-8

Enterprise Solutions. Data Warehouse & Business Intelligence Chapter-8 Enterprise Solutions Data Warehouse & Business Intelligence Chapter-8 Learning Objectives Concepts of Data Warehouse Business Intelligence, Analytics & Big Data Tools for DWH & BI Concepts of Data Warehouse

More information

Fluency With Information Technology CSE100/IMT100

Fluency With Information Technology CSE100/IMT100 Fluency With Information Technology CSE100/IMT100 ),7 Larry Snyder & Mel Oyler, Instructors Ariel Kemp, Isaac Kunen, Gerome Miklau & Sean Squires, Teaching Assistants University of Washington, Autumn 1999

More information

Data Warehousing and Data Mining in Business Applications

Data Warehousing and Data Mining in Business Applications 133 Data Warehousing and Data Mining in Business Applications Eesha Goel CSE Deptt. GZS-PTU Campus, Bathinda. Abstract Information technology is now required in all aspect of our lives that helps in business

More information

Data Warehousing. Read chapter 13 of Riguzzi et al Sistemi Informativi. Slides derived from those by Hector Garcia-Molina

Data Warehousing. Read chapter 13 of Riguzzi et al Sistemi Informativi. Slides derived from those by Hector Garcia-Molina Data Warehousing Read chapter 13 of Riguzzi et al Sistemi Informativi Slides derived from those by Hector Garcia-Molina What is a Warehouse? Collection of diverse data subject oriented aimed at executive,

More information

BUILDING OLAP TOOLS OVER LARGE DATABASES

BUILDING OLAP TOOLS OVER LARGE DATABASES BUILDING OLAP TOOLS OVER LARGE DATABASES Rui Oliveira, Jorge Bernardino ISEC Instituto Superior de Engenharia de Coimbra, Polytechnic Institute of Coimbra Quinta da Nora, Rua Pedro Nunes, P-3030-199 Coimbra,

More information

BIG DATA COURSE 1 DATA QUALITY STRATEGIES - CUSTOMIZED TRAINING OUTLINE. Prepared by:

BIG DATA COURSE 1 DATA QUALITY STRATEGIES - CUSTOMIZED TRAINING OUTLINE. Prepared by: BIG DATA COURSE 1 DATA QUALITY STRATEGIES - CUSTOMIZED TRAINING OUTLINE Cerulium Corporation has provided quality education and consulting expertise for over six years. We offer customized solutions to

More information

Advanced Data Management Technologies

Advanced Data Management Technologies ADMT 2015/16 Unit 2 J. Gamper 1/44 Advanced Data Management Technologies Unit 2 Basic Concepts of BI and Data Warehousing J. Gamper Free University of Bozen-Bolzano Faculty of Computer Science IDSE Acknowledgements:

More information

Business Intelligence. 1. Introduction September, 2013.

Business Intelligence. 1. Introduction September, 2013. Business Intelligence 1. Introduction September, 2013. The content of the first lecture Introduction to data warehousing and business intelligence Star join 2 Data hierarchy Strategical data Operational

More information

Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

More information