1 Relational Database Implementation of a Multi- dimensional database 컴퓨터언어연구실 석사 3 학기 김혜진 U.S. Patent Number: 5,926,818 Date of Patent: Jul.20,1999 inventor:

Slides:



Advertisements
Similar presentations
Lecture-7/ T. Nouf Almujally
Advertisements

Copyright © Starsoft Inc, Data Warehouse Architecture By Slavko Stemberger.
Online Analytical Processing OLAP
OLAP Services Business Intelligence Solutions. Agenda Definition of OLAP Types of OLAP Definition of Cube Definition of DMR Differences between Cube and.
[1] National Institute of Science & Technology TECHNICAL SEMINAR PRESENTATION Murali Mohan Rath CS OnLine Analytical Processing Seminar Presentation.
Database Security and Auditing: Protecting Data Integrity and Accessibility Chapter 5 Database Application Security Models.
Chapter 2 Data Models Database Systems: Design, Implementation, and Management, Seventh Edition, Rob and Coronel.
Multidimensional Database in Context of DB2 OLAP Server Khang Pham Class: CSCI397-16C Instructor: Professor Renner.
Data Sources Data Warehouse Analysis Results Data visualisation Analytical tools OLAP Data Mining Overview of Business Intelligence Data visualisation.
Organizing Data & Information
Advanced Querying OLAP Part 2. Context OLAP systems for supporting decision making. Components: –Dimensions with hierarchies, –Measures, –Aggregation.
Chapter 5 Database Application Security Models
CSE6011 Warehouse Models & Operators  Data Models  relations  stars & snowflakes  cubes  Operators  slice & dice  roll-up, drill down  pivoting.
SESSION 7 MANAGING DATA DATARESOURCES. File Organization Terms and Concepts Field: Group of words or a complete number Record: Group of related fields.
Designing a Data Warehouse
Online Analytical Processing (OLAP) Hweichao Lu CS157B-02 Spring 2007.
Vs. OLAP. Geography heirarchy Sales campaigns Other dimension Products Time Sales, profit, costs, key numbers, etc. Sales organization Star Scheme.
Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web logs Data Engineering Lab 성 유 진.
1 COMP 3503 Deductive Modeling with OLAP with Daniel L. Silver Daniel L. Silver.
Database System Concepts and Architecture Lecture # 3 22 June 2012 National University of Computer and Emerging Sciences.
1.
Intro to MIS – MGS351 Databases and Data Warehouses Chapter 3.
IST722 Data Warehousing Business Intelligence Development with SQL Server Analysis Services and Excel 2013 Michael A. Fudge, Jr.
Chapter 2 Database System Architecture. An “architecture” for a database system. A specification of how it will work, what it will “look like.” The “ANSI/SPARC”
ISLab Flash Team Flash File System Ban,A US Patent 5,404,485 한국외국어대학교 컴퓨터및정보통신공학과 박 성 환.
Database Application Security Models Database Application Security Models 1.
OnLine Analytical Processing (OLAP)
1 Data Warehouses BUAD/American University Data Warehouses.
OLAP & DSS SUPPORT IN DATA WAREHOUSE By - Pooja Sinha Kaushalya Bakde.
Data Warehousing.
BI Terminologies.
MANAGING DATA RESOURCES ~ pertemuan 7 ~ Oleh: Ir. Abdul Hayat, MTI.
13 1 Chapter 13 The Data Warehouse Database Systems: Design, Implementation, and Management, Seventh Edition, Rob and Coronel.
13 1 Chapter 13 The Data Warehouse Database Systems: Design, Implementation, and Management, Seventh Edition, Rob and Coronel.
1 Database Management Systems (DBMS). 2 Database Management Systems (DBMS) n Overview of: ä Database Management Components ä Database Systems Architecture.
Ayyat IT Group Murad Faridi Roll NO#2492 Muhammad Waqas Roll NO#2803 Salman Raza Roll NO#2473 Junaid Pervaiz Roll NO#2468 Instructor :- “ Madam Sana Saeed”
Fox MIS Spring 2011 Data Warehouse Week 8 Introduction of Data Warehouse Multidimensional Analysis: OLAP.
UNIT-II Principles of dimensional modeling
OLAP in DWH Ján Genči PDT. 2 Outline OLAP Definitions and Rules The term OLAP was introduced in a paper entitled “Providing On-Line Analytical.
Database Laboratory Lim, Hyunsook Inventors : Scott Davidson Lowry, Robert M. Lowry Assignee : Electronic Data Systems Corporation Filed : Jul. 1, 1996.
BI Practice March-2006 COGNOS 8BI TOOLS COGNOS 8 Framework Manager TATA CONSULTANCY SERVICES SEEPZ, Mumbai.
What is OLAP?.
13 1 Chapter 13 The Data Warehouse Database Systems: Design, Implementation, and Management, Seventh Edition, Rob and Coronel.
A Data Model for Supporting On-Line Analytical Processing DataBase Lab. 석사 1 학기 홍 은 주.
1 Database Systems, 8 th Edition 1 Chapter 13 Business Intelligence and Data Warehouses Objectives In this chapter, you will learn: –How business intelligence.
1 Online Analytical Processing (OLAP) Anjali Gupta Mithun Arora Aameek Singh Kranthi Kumar.
Fundamentals of Information Systems, Sixth Edition Chapter 3 Database Systems, Data Centers, and Business Intelligence.
SQL Server Analysis Services Understanding Unified Dimension Model (UDM)
병렬분산컴퓨팅연구실 1 Cubing Algorithms, Storage Estimation, and Storage and Processing Alternatives for OLAP 병렬 분산 컴퓨팅 연구실 석사 1 학기 이 은 정
1 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Introduction to Essbase.
1 Database Systems, 8 th Edition Star Schema Data modeling technique –Maps multidimensional decision support data into relational database Creates.
1 Copyright © 2006, Oracle. All rights reserved. Defining OLAP Concepts.
Pindaro Demertzoglou Data Resource Management – MGMT 4170 Lally School of Management Rensselaer Polytechnic Institute.
1 Copyright © 2008, Oracle. All rights reserved. Repository Basics.
CSE6011 Implementing a Warehouse  Monitoring: Sending data from sources  Integrating: Loading, cleansing,...  Processing: Query processing, indexing,...
Geographic Information Systems GIS Data Databases.
Presented By: Pedel Oppong-Abebrese,Pedel Oppong-Abebrese Michael Boadi, William Osei, Nana Amoa OforiMichael BoadiWilliam OseiNana Amoa Ofori DATA WAREHOUSING.
Fundamentals & Ethics of Information Systems IS 201
Attribute-Based Access for Multi-Dimensional Databases
Datamining : Refers to extracting or mining knowledge from large amounts of data Applications : Market Analysis Fraud Detection Customer Retention Production.
Data Warehouse.
MANAGING DATA RESOURCES
Enhance BI Applications and Simplify Development
Types of OLAP Servers.
Unidad II Data Warehousing Interview Questions
A New Storage Engine Specialized for MOLAP
OLAP in DWH Ján Genči PDT.
Chapter 13 The Data Warehouse
Online analytical processing (OLAP) is a category of software technology that enables analysts, managers, and executives to gain insight into data through.
Geographic Information Systems
Presentation transcript:

1 Relational Database Implementation of a Multi- dimensional database 컴퓨터언어연구실 석사 3 학기 김혜진 U.S. Patent Number: 5,926,818 Date of Patent: Jul.20,1999 inventor: William Earl Malloy

2 abstract A method, apparatus, and article of manufacture for using a RDBMS to support OLAP systems. Multi-dimensional database 는 RDB 에서 relational schema 로 표현된다. – 여러 dimension table, 하나의 fact table 포함 –fact table multi-dimensional database 의 모든 dimension- 1 만큼의 column + 나머지 한 dimension 의 각 member 에 대한 value column

3 abstract dimension column 에 의해 identify 된 member 와 value column 의 교차에 따른 value 를 포함한 row 를 적어도 하나 갖는다. –dimension tables 각 dimension 은 하나 이상의 member 를 갖는다.

4 Background of the invention Field –OLAP 을 지원하는 RDBMS description of related art –dimension –multi-dimensional conceptual view –multi-dimensional data analysis – 어떤 vendor 들은 RDBMS product 를 storage manager 로 사용하는 OLAP system 을 제안하고 내 놓았으나 많은 이유로 실패 – 그 결과 RDBMS product 를 storage manager 로 사 용하는 enhanced 된 기술이 필요하게 됨

5 Summary of the invention(I) Multi-dimensional database 는 relational database 에서의 relational schema 로 표 현됨 –multi-dimensional database: dimension 이 하나 이상 –relational schema: 한 fact table 과 여러 dimension table 포함

6 Summary of the invention(II) –fact table multi-dimensional database 의 모든 dimension- 1 만큼의 column + 나머지 한 dimension 의 각 member 에 대한 value column dimension column 에 의해 identify 된 member 와 value column 의 교차에 따른 value 를 포함한 row 를 적어도 하나 갖는다. –dimension tables 각 dimension 은 하나 이상의 member 를 갖는다.

7 Summary of the invention(III) Object –to emulate a multi-dimensional database using a relational database –to provide a relational database implementation of a multi-dimensional database using a relational schema –to map data between the multi-dimensional database and the relational database

8 Detailed Description of the Preferred Embodiment

9 Overview(I) OLAP system: –Arbor Software’s Essbase OLAP software data access, navigation, application design and management and data calculation present invention 은 storing and retrieving data 같은 database operation 을 수행하는 새로운 component 를 포함 – IBM’s DB2 RDBMS software 로 구성 –relational database storage manager 는 OLAP system 이 data 를 직접 relational database 에 저장하도록 해 준다. ->simplified application design, robust calculation capabilities, and flexible data access (prior art ROLAP 과는 다름 )

10 Overview(II) Performance – 이전 ROLAP 제품보다 consistent, fast response automatic table, index and summary management – 자동으로 RDB 에서 star schema 내에서의 table 과 index 를 생 성하고 관리 – 이전 ROLAP 은 DB 구조의 team 을 필요로 함 robust analytical calculation – 이전 ROLAP 제품보다 더 robust

11 Overview(III) multi-user read and write access –multi-user 의 read & write access 를 지원 – 이전 ROLAP 제품은 read-only robust data security –individual data cell level 까지 data security 를 지원 – 이전 제품은 security 없거나, 제한된 수준

12 Figure 1. An exemplary hardware environment

13 Figure 2. Conceptual structure of a multi-dimensional database according to the present invention (i.e. an outline)

14 Figure 3. Logical structure of a multi-dimensional database

15 Physical structure of the multi- dimensional database Two-level data structure 가 정의되어 있는 multi-dimensional data 를 store 하고 retrieve – 한 level 은 user 에 의해 선택된 dimension 들을 포함 하며, dense data block 을 이룸 – 다른 level 은 dimension 을 identify 하여 dense data block 을 선택하기 위해 sparse index 로 사용된 나머 지 dimension combination 을 포함 sparse index file 은 dense data block 을 선택하 는데 쓰이는 정보를 포함

Member Name TIME Multi- Dimensional Member Identifier Relational Member Identifier Member Name PRODUCT Multi- Dimensional Member Identifier Relational Member Identifier Member Name MEASURES Multi- Dimensional Member Identifier Relational Member Identifier Relational Member Name FACT TABLE Figure 4. A structure for storing multi-dimensional data in a relational database structure according to the present invention

17 fact table cube 의 실제 data 값을 유지하기 위해 cube 당 하나 이상의 fact table 은 많은 수의 member 를 지원하기 위함 non-anchor dimension 의 member 의 유효한 combination 에 대한 row 만 가짐

18 dimension tables cube 에서 정의된 각 dimension 에 대해 하나의 dimension table 특정 dimension 에 대한 관련된 모든 information 을 가짐 MemberName RelMemberName RelMemberId MemberId

19 accessing multi-dimensional data RDB 의 multi-dimensional data 에 접근하기 위 해 user 는 OLAP client program 과 interact 이것은 DB operation 에 대한 request 를 만듦 이 request 는 network interface program 을 통 해 OLAP server 에 의해 실행된 OLAP agent and/or OLAP engine 에 전달

20 outline modifications Outline 이 변경되면 RDB 가 변경되므로 MemberId 도 바뀐다. 이에 따라 dimension table 과 fact table 도 변경 member 가 추가되면 available 한 RelMemberId 를 찾아 할당

21 Figure 5. accessing multi-dimensional data from a relational database

22 Conclusion emulate a multi-dimensional database using a relational database star schema 를 사용하여 multi-dimensional database 를 relational database 로 구현 maps data between the multi-dimensional database and the relational database