scholarly journals An Ontological Approach to Handle Multidimensional Schema Evolution for Data Warehouse

2014 ◽  
Vol 6 (3) ◽  
pp. 33-52 ◽  
Author(s):  
Thenmozhi M ◽  
Vivekanandan K
Keyword(s):  
Data Warehouse ◽  
Schema Evolution ◽  
Ontological Approach

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.

scholarly journals The Development of a Heterogeneous MP Data Model Based on the Ontological Approach

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 813
Author(s):  
Sergey Porshnev ◽  
Andrey Borodin ◽  
Olga Ponomareva ◽  
Sergey Mirvoda ◽  
Olga Chernova
Keyword(s):  
Production Process ◽  
Data Warehouse ◽  
Business Processes ◽  
Chemical Properties ◽  
Heterogeneous Data ◽  
Sources Of Information ◽  
Metallurgical Enterprise ◽  
Metallurgical Production ◽  
Ontological Approach ◽  
Ontological Model

The article discusses the approaches providing symmetric access of all industrial production services to the data of business processes of the enterprise by building a single warehouse of heterogeneous data of a metallurgical production. The warehouse is a part of an automated statistic quality control system for the products of a metallurgical enterprise. The article describes an ontological storage model of data coming from various sources of information in the production process. The concept of “a unit of production of metallurgical production” is introduced that is the connecting component of the entire production life cycle of a metallurgical production. The authors propose an ontological model of the production process, in terms of information flows which are formed in an enterprise at each stage of production. Based on the constructed ontological model, the structure of recording an array of information in the heterogeneous data warehouse is justified and formed. Heterogeneous data warehouse forms a single information space of the enterprise, which serves as the basis for analytical analysis throughout the production and decision—making process. For example, timely response to the deviation reasons from the given physical and chemical properties of the finished product.

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.

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

scholarly journals Academic data warehouse design using a hybrid methodology

2015 ◽  
Vol 12 (1) ◽  
pp. 135-160 ◽  
Author(s):  
Tria Di ◽  
Ezio Lefons ◽  
Filippo Tangorra
Keyword(s):  
Data Warehouse ◽  
Design Process ◽  
Business Intelligence ◽  
Quality Level ◽  
Academic Context ◽  
Factors Affecting ◽  
Academic Organizations ◽  
Intelligence System ◽  
Ontological Approach ◽  
Hybrid Methodology

In the last years, data warehousing has got attention from Universities which are now adopting business intelligence solutions in order to analyze crucial aspects of the academic context. In this paper, we present the architecture of a Business Intelligence system for academic organizations. Then, we illustrate the design process of the data warehouse devoted to the analysis of the main factors affecting the importance and the quality level of every University, such as the evaluation of the Research and the Didactics. The design process we describe is based on a hybrid methodology that is largely automatic and relies on an ontological approach for the integration of the different data sources.

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