Design and Implementation of Multidimensional Data Model for OLAP Based on Object-Relational DBMS

OLAP을 위한 객체-관계 DBMS 기반 다차원 데이터 모델의 설계 및 구현

  • Published : 2000.06.01

Abstract

Among OLAP(On-Line Analytical Processing) approaches, ROLAP(Relational OLAP) based on the star, snowflake schema which offer the multidimensional analytical method has performance problem and MOLAP (Multidimensional OLAP) based on Multidimensional Database System has scalability problem. In this paper, to solve the limitaions of previous approaches, design and implementation of multidimensional data model based on Object-Relation DBMS was proposed. With the extensibility of Object-Relation DBMS, it is possible to advent multidimensional data model which more expressively define multidimensional concept and analysis functions that are optimized for the defined multidimensional data model. In addition, through the hierarchy between data objects supported by Object-Relation DBMS, the aggregated data model which is inherited from the super-table, multidimensional data model, was designed. One these data models and functions are defined, they behave just like a built-in function, w th the full performance characteristics of Object-Relation DBMS engine.

OLAT(On-Line Analytical Processing) 기법에서 스타 또는 눈송이(snowflake) 스키마에 기반한 ROLAP(Relational OLAP)은 성능 저하라는 문제가 있고, 다차원 데이터베이스에 기반한 MOLAP(Multidinmensional OLAP)은 데이터 크기 증가에 따른 공간 문제가 있다. 본 논문에서는 기존의 OLAP 시스템이 이러한 문제점을 해결하기 위해서 객체-관계 DBMS에 기반한 다차원 데이터 모델을 제안하였다. 객체-관계 DBMS가 가지는 확장성 특징을 사용하여 다차원 데이터 모델에 최적화된 다차원 개념과 함수를 정의할 수 있었다. 또한 객체-관계 DBMS의 객체간 계승 기능을 통하여 상위 테이블을 계승받는 요약 다차원 데이터 큐브의 다차원 데이터 모델을 설계하였다. 이와 같은 OLAP을 위한 데이터 타입과 함수가 정의되면, 새로운 객체-관계 DBMS 엔진과 같이 내장된 기능처럼 동작되어 성능향상이 가능하다. 또한 객체 관계 DBMS의 하나인 Informix Universal Server와 클라이언트 개발 도구를 이용하여 제안된 다차원 데이터 모델을 구현하였다.

Keywords

References

  1. Prism Solutions Technology v.1 no.1 What is a Data Warehouse? W.H. Inmon
  2. The data warehouse challenge M.H. Brackett
  3. Building the Data Warehouse W.H. Inmon
  4. Managing the Data Warehouse W.H. Inmon;J.D. Welch;K.L. Glassey
  5. The Data Warehouse Toolkit R. Kimball
  6. 데이타 웨어하우징과 OLAP 조재희;박성진
  7. The Intranet Data Warehouse R. Tanler
  8. OLAP Solution E. Thomsen
  9. Object-Relational DBMSs: The Next Great Wave M. Stonebraker
  10. Advanced Database Systems C. Zaniolo;S. Ceri;C. Faloutsos;R.T. Snodgrass;V.S. Subrahmanian;R. Zicari
  11. In Proc. on ACM SIGMOD Conf. Implementing Data Cubes Efficiently V. Harinarayan;A. Rajaraman;J.D. Ullman
  12. Microsoft Technical Report MSR-TR-95-22 Data Cube : A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals J. Gray;A. Bosworth
  13. Kenan System Corporation White Paper Modeling Multidimensional Database Technology R. Agrawal;A. Gupta;S. Sarawagi
  14. Oracle White Paper Oracle OLAP Products: Adding values to the Data Warehouse Oracle
  15. Red Brick Systems White Paper Star Schemas and Starjoin Technology Red Brick System
  16. Data Warehouse Report OLAP & ROLAP ? Two Ways of Giving the User Multidimensional Data Access S. Kelly
  17. In Proc. on ACM SIGMOD Multi-Table Joins Through Bitmapped Join Indexes P. O'Neil;G. Graefe
  18. In Proc. on 21st VLDB Eager Aggregation and Lazy Aggregation W.P. Yan;P.A. Larson
  19. Stanford Technical Report No. STANCS-TN-94-1 Generalized Projection : A Powerful Query-Optimization Technique V. Harinarayan;A. Gupta
  20. In Proc. on 21st VLDB Aggregate-Query Processing in Data Warehousing Environments A. Gupta;V. Harinarayan;D. Quass
  21. Informix Universal Server Guide to SQL : Reference Informix