A Study of Building Data Warehouse Based on Making Use of its System Structure and Data Model

2009 ◽  
Author(s):  
Xianjun Wang ◽  
Xianyi Qian
Keyword(s):  
Data Warehouse ◽  
Data Model ◽  
System Structure

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.

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.

Design and Realization of Remote Sensing Image Publication System Based on SuperMap IS.NET

2014 ◽  
Vol 608-609 ◽  
pp. 363-366
Author(s):  
Xiao Fang Wang
Keyword(s):  
Remote Sensing ◽  
Data Model ◽  
Remote Sensing Image ◽  
Model System ◽  
System Structure ◽  
Key Technology ◽  
Structure System ◽  
The Core ◽  
Image Production

With the more and more expanded city and the heavier remote sensing image production, update, management, building the modern Remote Sensing Image Publication System is placed in the forefront for providing the good services. The paper discussed the NET system of Remote Sensing Image Publication System based on SuperMap IS.NET, and so more, the core contents including data-model, system-structure, system-aim, and key technology about the system are introduced in this paper.

scholarly journals Temporal and Evolving Data Warehouse Design

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Sidra Faisal ◽  
Mansoor Sarwar ◽  
Khurram Shahzad ◽  
Shahzad Sarwar ◽  
Waqar Jaffry ◽  
...  
Keyword(s):  
Data Management ◽  
Data Warehouse ◽  
Data Model ◽  
Temporal Data ◽  
Dimensional Model ◽  
Temporal Aspects ◽  
Time Stamps ◽  
Data Source ◽  
Evolving Data ◽  
Business Requirements

The data model of the classical data warehouse (formally, dimensional model) does not offer comprehensive support for temporal data management. The underlying reason is that it requires consideration of several temporal aspects, which involve various time stamps. Also, transactional systems, which serves as a data source for data warehouse, have the tendency to change themselves due to changing business requirements. The classical dimensional model is deficient in handling changes to transaction sources. This has led to the development of various schemes, including evolution of data and evolution of data model and versioning of dimensional model. These models have their own strengths and limitations, but none fully satisfies the above-stated broad range of aspects, making it difficult to compare the proposed schemes with one another. This paper analyses the schemes that satisfy such challenging aspects faced by a data warehouse and proposes taxonomy for characterizing the existing models to temporal data management in data warehouse. The paper also discusses some open challenges.

scholarly journals Domain/Mapping Model: A Novel Data Warehouse Data Mode

2017 ◽  
Vol 12 (2) ◽  
pp. 166 ◽  
Author(s):  
Ivan Bojicic ◽  
Zoran Marjanovic ◽  
Nina Turajlic ◽  
Marko Petrovic ◽  
Milica Vuckovic ◽  
...  
Keyword(s):  
Data Warehouse ◽  
Data Model ◽  
Heterogeneous Data ◽  
Physical Structure ◽  
Data Sources ◽  
Mapping Model ◽  
Heterogeneous Data Sources ◽  
Domain Mapping ◽  
Source Models ◽  
Data Source

In order for a data warehouse to be able to adequately fulfill its integrative and historical purpose, its data model must enable the appropriate and consistent representation of the different states of a system. In effect, a DW data model, representing the physical structure of the DW, must be general enough, to be able to consume data from heterogeneous data sources and reconcile the semantic differences of the data source models, and, at the same time, be resilient to the constant changes in the structure of the data sources. One of the main problems related to DW development is the absence of a standardized DW data model. In this paper a comparative analysis of the four most prominent DW data models (namely the relational/normalized model, data vault model, anchor model and dimensional model) will be given. On the basis of the results of [1]a, the new DW data model (the Domain/Mapping model- DMM) which would more adequately fulfill the posed requirements is presented.

scholarly journals A Novel Cross-Disciplinary Multi-Institute Approach to Translational Cancer Research: Lessons Learned from Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC)

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Ashokkumar A. Patel ◽  
John R. Gilbertson ◽  
Louise C. Showe ◽  
Jack W. London ◽  
Eric Ross ◽  
...  
Keyword(s):  
Cancer Research ◽  
Data Warehouse ◽  
Data Model ◽  
Collaborative Research ◽  
The State ◽  
Lessons Learned ◽  
Common Data Elements ◽  
Tissue Samples ◽  
Blood Samples ◽  
Data Elements

Background The Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC, http://www.pcabc.upmc.edu ) is one of the first major project-based initiatives stemming from the Pennsylvania Cancer Alliance that was funded for four years by the Department of Health of the Commonwealth of Pennsylvania. The objective of this was to initiate a prototype biorepository and bioinformatics infrastructure with a robust data warehouse by developing a statewide data model (1) for bioinformatics and a repository of serum and tissue samples; (2) a data model for biomarker data storage; and (3) a public access website for disseminating research results and bioinformatics tools. The members of the Consortium cooperate closely, exploring the opportunity for sharing clinical, genomic and other bioinformatics data on patient samples in oncology, for the purpose of developing collaborative research programs across cancer research institutions in Pennsylvania. The Consortium's intention was to establish a virtual repository of many clinical specimens residing in various centers across the state, in order to make them available for research. One of our primary goals was to facilitate the identification of cancer-specific biomarkers and encourage collaborative research efforts among the participating centers. Methods The PCABC has developed unique partnerships so that every region of the state can effectively contribute and participate. It includes over 80 individuals from 14 organizations, and plans to expand to partners outside the State. This has created a network of researchers, clinicians, bioinformaticians, cancer registrars, program directors, and executives from academic and community health systems, as well as external corporate partners - all working together to accomplish a common mission. The various sub-committees have developed a common IRB protocol template, common data elements for standardizing data collections for three organ sites, intellectual property/tech transfer agreements, and material transfer agreements that have been approved by each of the member institutions. This was the foundational work that has led to the development of a centralized data warehouse that has met each of the institutions’ IRB/HIPAA standards. Results Currently, this “virtual biorepository” has over 58,000 annotated samples from 11,467 cancer patients available for research purposes. The clinical annotation of tissue samples is either done manually over the internet or semi-automated batch modes through mapping of local data elements with PCABC common data elements. The database currently holds information on 7188 cases (associated with 9278 specimens and 46,666 annotated blocks and blood samples) of prostate cancer, 2736 cases (associated with 3796 specimens and 9336 annotated blocks and blood samples) of breast cancer and 1543 cases (including 1334 specimens and 2671 annotated blocks and blood samples) of melanoma. These numbers continue to grow, and plans to integrate new tumor sites are in progress. Furthermore, the group has also developed a central web-based tool that allows investigators to share their translational (genomics/proteomics) experiment data on research evaluating potential biomarkers via a central location on the Consortium's web site. Conclusions The technological achievements and the statewide informatics infrastructure that have been established by the Consortium will enable robust and efficient studies of biomarkers and their relevance to the clinical course of cancer.

scholarly journals The HMO Research Network Virtual Data Warehouse: A Public Data Model to Support Collaboration

2014 ◽  
Vol 2 (1) ◽  
pp. 2 ◽  
Author(s):  
Tyler R. Ross ◽  
Daniel Ng ◽  
Jeffrey S. Brown ◽  
Roy Pardee ◽  
Mark C. Hornbrook ◽  
...  
Keyword(s):  
Data Warehouse ◽  
Data Model ◽  
Research Network ◽  
Public Data ◽  
Virtual Data
Sign in / Sign up

Export Citation Format

Share Document