scholarly journals Schema Evolution for Data Warehouse: A Survey

2011 ◽  
Vol 22 (6) ◽  
pp. 6-14 ◽  
Author(s):  
Meenakshi Arora ◽  
Anjana Gosain
Keyword(s):  
Data Warehouse ◽  
Schema Evolution

Schema Evolution Models and Languages for Multidimensional Data Warehouses

2009 ◽  
pp. 119-128
Author(s):  
Edgard Benítez-Guerrero ◽  
Ericka-Janet Rechy-Ramírez
Keyword(s):  
Data Analysis ◽  
Data Warehouse ◽  
Historical Data ◽  
Multidimensional Systems ◽  
Multidimensional Data ◽  
Schema Evolution ◽  
Time Dimension ◽  
On Line ◽  
Analytical Processing ◽  
Factual Data

A Data Warehouse (DW) is a collection of historical data, built by gathering and integrating data from several sources, which supports decisionmaking processes (Inmon, 1992). On-Line Analytical Processing (OLAP) applications provide users with a multidimensional view of the DW and the tools to manipulate it (Codd, 1993). In this view, a DW is seen as a set of dimensions and cubes (Torlone, 2003). A dimension represents a business perspective under which data analysis is performed and organized in a hierarchy of levels that correspond to different ways to group its elements (e.g., the Time dimension is organized as a hierarchy involving days at the lower level and months and years at higher levels). A cube represents factual data on which the analysis is focused and associates measures (e.g., in a store chain, a measure is the quantity of products sold) with coordinates defined over a set of dimension levels (e.g., product, store, and day of sale). Interrogation is then aimed at aggregating measures at various levels. DWs are often implemented using multidimensional or relational DBMSs. Multidimensional systems directly support the multidimensional data model, while a relational implementation typically employs star schemas(or variations thereof), where a fact table containing the measures references a set of dimension tables.

Dynamic Workload for Schema Evolution in Data Warehouses

2010 ◽  
pp. 28-46
Author(s):  
Fadila Bentayeb ◽  
Cécile Favre ◽  
Omar Boussaid
Keyword(s):  
Data Warehouse ◽  
Management System ◽  
Heterogeneous Data ◽  
Database Management System ◽  
Main Issue ◽  
Data Sources ◽  
Schema Evolution ◽  
Data Warehouses ◽  
Workload Management ◽  
On Line

A data warehouse allows the integration of heterogeneous data sources for identified analysis purposes. The data warehouse schema is designed according to the available data sources and the users’ analysis requirements. In order to provide an answer to new individual analysis needs, the authors previously proposed, in recent work, a solution for on-line analysis personalization. They based their solution on a user-driven approach for data warehouse schema evolution which consists in creating new hierarchy levels in OLAP (on-line analytical processing) dimensions. One of the main objectives of OLAP, as the meaning of the acronym refers, is the performance during the analysis process. Since data warehouses contain a large volume of data, answering decision queries efficiently requires particular access methods. The main issue is to use redundant optimization structures such as views and indices. This implies to select an appropriate set of materialized views and indices, which minimizes total query response time, given a limited storage space. A judicious choice in this selection must be cost-driven and based on a workload which represents a set of users’ queries on the data warehouse. In this chapter, the authors address the issues related to the workload’s evolution and maintenance in data warehouse systems in response to new requirements modeling resulting from users’ personalized analysis needs. The main issue is to avoid the workload generation from scratch. Hence, they propose a workload management system which helps the administrator to maintain and adapt dynamically the workload according to changes arising on the data warehouse schema. To achieve this maintenance, the authors propose two types of workload updates: (1) maintaining existing queries consistent with respect to the new data warehouse schema and (2) creating new queries based on the new dimension hierarchy levels. Their system helps the administrator in adopting a pro-active behaviour in the management of the data warehouse performance. In order to validate their workload management system, the authors address the implementation issues of their proposed prototype. This latter has been developed within client/server architecture with a Web client interfaced with the Oracle 10g DataBase Management System.

RANCANGAN DATA WAREHOUSE PADA SEKOLAH TINGGI TEKNIK POLIPROFESI MEDAN

KURVATEK ◽  
2018 ◽  
Vol 3 (1) ◽  
pp. 63-69
Author(s):  
Siti Jamilah Tarigan ◽  
Wing Wahyu Winarno ◽  
Henderi Safei
Keyword(s):  
Data Warehouse

Pengambilan keputusan dan perencanaan bidang akademik sering kali tidak berdasarkan pada informasi yang lengkap. Jajaran pengambil keputusan (rektorat atau tingkat eksekutif) hanya bisa melihat sebuah data dalam satu dimensi. Pengambil keputusan akan lebih baik jika informasi dapat disajikan dari berbagai dimensi. Perguruan tinggi telah memiliki data operasional yang lengkap dari kegiatan akademik, kepegawaian, dan penerimaan mahasiswa yang telah dikumpulkan lebih dari 4 tahun. Data warehouse adalah suatu koleksi optimasi database untuk mendukung keputusan. Konsep ini mengintegrasikan antara sistem lama dan sistem baru sehingga tidak terjadi duplikasi data. Data yang telah diintegrasikan dapat diolah dalam berbagai bentuk laporan sesuai dengan kebutuhan.Tujuan dari penelitian ini adalah bagaimana data yang ada bisa menghasilkan informasi yang akurat dan multidimensi sehingga pengambilan keputusan lebih cepat dan akurat. Penelitian ini menggunakan analisis data OLAP, dan skema bintang. Kesimpulan dari penelitian ini adalah rancangan yang dihasilkan bisa membantu pihak akademik dalam membuat keputusan berdasarkan data dan informasi yang mulitidimensi.

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