Three-Dimensional Engineering Geology Data Warehouse Constructed by Power Designer

2013 ◽  
Vol 753-755 ◽  
pp. 3112-3115
Author(s):  
Jing Li Huang ◽  
Qing Wang ◽  
Qiu Ling Lang
Keyword(s):  
Data Base ◽  
Data Warehouse ◽  
Data Model ◽  
Engineering Geology ◽  
Three Dimensional ◽  
Soil Mass ◽  
Design Model ◽  
Underground Space ◽  
Geological Data ◽  
Rock And Soil

The Three-dimensional engineering geology data warehouse is constructed by Power Desinger16.1, with the theme as the rock and mass availability in urban underground space, and with the source data as the borehole data of engineering Investigation. Use the Model-driven Architecture method, reverse engineer the Access data base, extract existed data model, combine research theme to construct the Star data structure model. And check the SQL script in SQL Server2005, to ensure normal operation. 0 Forewords The traditional transaction-oriented designed engineering geology data base has the function to storage original data from work, to draw of geological section and to provide simple check and analysis, but without the decision support function in view of a subject. The purpose of construction a 3D engineering geological data warehouse is to build a decision support system in view of availability of rock and soil mass in urban underground space. Based on the data extraction, data integration, data cleaning and data transformation, the 3D engineering geological data warehouse could achieve the integrated management of massive geological data and to provide reliable data source for the rock and soil mass utilization system in urban underground space. The main feature of 3D engineering geological data base is subject-oriented, integrated, time-varying, relatively stable, and is magnanimous collection of engineering geological spatial data and attribute data. According to the design pattern of traditional data base, the construction of 3D engineering geological data warehouse can be divided into three stages: concept design model, logic design model and physical design model. But the 3D engineering geological data warehouse exist iterative in the construction process. Currently, there are many CASE tools to help developers quickly achieving the data base design, such as Rational Rose by Rational company, Erwin and Bpwin by CA company, Power Designer by Sybase company, Office Visio by Microsoft company, and Oracle Designer by Oracle company. The paper uses the Powerdesigner16.1 to achieve the logical data model (LDM) and physical data model (PDM).

AN ARCHITECTURE FOR DATA WAREHOUSING SUPPORTING DATA INDEPENDENCE AND INTEROPERABILITY

2001 ◽  
Vol 10 (03) ◽  
pp. 377-397 ◽  
Author(s):  
LUCA CABIBBO ◽  
RICCARDO TORLONE
Keyword(s):  
Data Warehouse ◽  
Data Model ◽  
Data Warehousing ◽  
Heterogeneous Data ◽  
Multidimensional Data ◽  
Multidimensional Databases ◽  
Level Of Aggregation ◽  
High Level ◽  
Data Independence ◽  
Logical Architecture

We report on the design of a novel architecture for data warehousing based on the introduction of an explicit "logical" layer to the traditional data warehousing framework. This layer serves to guarantee a complete independence of OLAP applications from the physical storage structure of the data warehouse and thus allows users and applications to manipulate multidimensional data ignoring implementation details. For example, it makes possible the modification of the data warehouse organization (e.g. MOLAP or ROLAP implementation, star scheme or snowflake scheme structure) without influencing the high level description of multidimensional data and programs that use the data. Also, it supports the integration of multidimensional data stored in heterogeneous OLAP servers. We propose [Formula: see text], a simple data model for multidimensional databases, as the reference for the logical layer. [Formula: see text] provides an abstract formalism to describe the basic concepts that can be found in any OLAP system (fact, dimension, level of aggregation, and measure). We show that [Formula: see text] databases can be implemented in both relational and multidimensional storage systems. We also show that [Formula: see text] can be profitably used in OLAP applications as front-end. We finally describe the design of a practical system that supports the above logical architecture; this system is used to show in practice how the architecture we propose can hide implementation details and provides a support for interoperability between different and possibly heterogeneous data warehouse applications.

Transforming the Conceptual Data Model to SQL

2006 ◽  
pp. 83-106
Author(s):  
Toby Teorey ◽  
Sam Lightstone ◽  
Tom Nadeau
Keyword(s):  
Data Model ◽  
Conceptual Data

scholarly journals Data Warehouse Schemas using Multidimensional Data Model for Retail

2017 ◽  
Vol 2 (1) ◽  
pp. 84
Author(s):  
Kheri Arionadi Shobirin ◽  
Adi Panca Saputra Iskandar ◽  
Ida Bagus Alit Swamardika
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Data Warehouse ◽  
Data Model ◽  
Transaction Processing ◽  
Multidimensional Data ◽  
Complex Query ◽  
On Line ◽  
Analytical Processing ◽  
Operational Data

A data warehouse are central repositories of integrated data from one or more disparate sources from operational data in On-Line Transaction Processing (OLTP) system to use in decision making strategy and business intelligent using On-Line Analytical Processing (OLAP) techniques. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Multidimensional data models as an integral part of OLAP designed to solve complex query analysis in real time.

scholarly journals Integration of biosafety surveillance through Biosafety Surveillance Conceptual Data Model

2019 ◽  
Vol 1 (2) ◽  
pp. 98-104
Author(s):  
Songwang Wang ◽  
Yangfei Li ◽  
Qiang Chen ◽  
Xiaoyu Feng ◽  
Xiju Shi ◽  
...  
Keyword(s):  
Data Model ◽  
Conceptual Data
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