Web-Based Implementation of Data Warehouse Schema Evolution

2015 ◽  
pp. 313-322 ◽  
Author(s):  
Jai W. Kang ◽  
Fnu Basrizal ◽  
Qi Yu ◽  
Edward P. Holden
Keyword(s):  
Data Warehouse ◽  
Schema Evolution ◽  
Web Based

Integrating Web-Based Data into a Data Warehouse

2003 ◽  
Author(s):  
Zhenyu Huang ◽  
Lei-da Chen ◽  
Mark Frolick
Keyword(s):  
Data Warehouse ◽  
Web Based

scholarly journals PENGEMBANGAN DATA WAREHOUSE CALL CENTER DENGAN METODOLOGI KIMBALL NINE-STEP PADA BINA SARANA INFORMATIKA

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
ADJAT SUDRADJAT
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Data Warehouse ◽  
Call Center ◽  
Educational Institution ◽  
Public Information ◽  
Strategic Information ◽  
Web Based ◽  
Academic Services ◽  
Operational Data

ABSTRACT - As an educational institution, Bina Sarana Informatika of course requires summary of information which is comprehensive and sustainable as a support to management in doing evaluation, planning and decision-making in the field of academic services. But the information system from operational data processing today can’t meet the needs, because it’s only able to produce detailed reports periodically. The research studies the development of a data warehouse for Call Center on The Division of Public Information of Bina Sarana Informatika in order to explore the strategic information contained in the operational database and present them in the form of summary information which is useful as input in improving the quality of academic services. By using a nine steps kimball approach, the research produce a data warehouse which is equipped with a web-based presentation application that can be easily accessed by all stakeholders of Bina Sarana Informatika. The development of data warehouse has been able to extract operational data into strategic information summaries that are useful to Bina Sarana Informatika management as supporting in doing evaluation, planning and decision-making in the field of academic services. Keywords : Nine Steps Kimball, Call Center, Data Warehouse, Decision Support. ABSTRAKSI - Sebagai sebuah institusi pendidikan, Bina Sarana Informatika tentu membutuhkan ringkasan informasi yang komprehensif dan berkesinambungan sebagai penunjang bagi manajemen dalam melakukan evaluasi, perencanaan dan pengambilan keputusan di bidang pelayanan akademik. Namun sistem informasi yang berasal dari pengolahan data operasional saat ini tidak dapat memenuhi kebutuhan tersebut, karena hanya mampu menghasilkan laporan–laporan yang bersifat detail dan periodik. Penelitian ini mengkaji pengembangan data warehouse Call Center pada Divisi Informasi Publik Bina Sarana Informatika untuk menggali informasi strategis yang terdapat pada database operasional dan menyajikannya dalam bentuk ringkasan informasi yang berguna sebagai masukan dalam usaha peningkatan kualitas pelayanan akademik. Dengan menggunakan metodologi kimball nine-step, penelitian menghasilkan sebuah data warehouse dilengkapi dengan aplikasi presentasi berbasis web yang dapat diakses dengan mudah oleh seluruh stakeholder Bina Sarana Informatika. Pengembangan data warehouse telah mampu mengekstrak data operasional menjadi ringkasan informasi strategis yang berguna bagi manajemen Bina Sarana Informatika sebagai penunjang dalam melakukan evaluasi, perencanaan dan pengambilan keputusan di bidang pelayanan akademik. Kata Kunci : Nine Steps Kimball, Call Center, Data Warehouse, Decision Support.

On Modeling and Analysis of Multidimensional Geographic Databases

2013 ◽  
pp. 91-107 ◽  
Author(s):  
Sandro Bimonte
Keyword(s):  
Data Warehouse ◽  
Complex Nature ◽  
Web Based ◽  
Modeling And Analysis ◽  
Multidimensional Databases ◽  
Geographic Data ◽  
Analytical Review ◽  
Flexible Framework ◽  
Web Based System ◽  
Support Decision Making

Data warehouse and OLAP systems are tools to support decision-making. Geographic information systems (GISs) allow memorizing, analyzing and visualizing geographic data. In order to exploit the complex nature of geographic data, a new kind of decision support system has been developed: spatial OLAP (SOLAP). Spatial OLAP redefines main OLAP concepts: dimension, measure and multidimensional operators. SOLAP systems integrate OLAP and GIS functionalities into a unique interactive and flexible framework. Several research tools have been proposed to explore and the analyze spatio-multidimensional databases. This chapter presents a panorama of SOLAP models and an analytical review of research SOLAP tools. Moreover, the authors describe their Web-based system: GeWOlap. GeWOlap is an OLAP-GIS integrated solution implementing drill and cut spatio-multidimensional operators, and it supports some new spatio-multidimensional operators which change dynamically the structure of the spatial hypercube thanks to spatial analysis operators.

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.

Web Data Warehousing Convergence

2009 ◽  
pp. 278-303
Author(s):  
D. Xuan Le ◽  
J. Wenny Rahayu ◽  
David Taniar
Keyword(s):  
Data Warehouse ◽  
Object Oriented ◽  
Hierarchical Structures ◽  
Structured Data ◽  
Integration Technique ◽  
Web Data ◽  
Web Based ◽  
Data Semantics ◽  
Object Relational ◽  
Data Source

This paper proposes a data warehouse integration technique that combines data and documents from different underlying documents and database design approaches. The well-defined and structured data such as Relational, Object- oriented and Object Relational data, semi-structured data such as XML, and unstructured data such as HTML documents are integrated into a Web data warehouse system. The user specified requirement and data sources are combined to assist with the definitions of the hierarchical structures, which serve specific requirements and represent a certain type of data semantics using object-oriented features including inheritance, aggregation, association and collection. A conceptual integrated data warehouse model is then specified based on a combination of user requirements and data source structure, which creates the need for a logical integrated data warehouse model. A case study is then developed into a prototype in a Web-based environment that enables the evaluation. The evaluation of the proposed integration Web data warehouse methodology includes the verification of correctness of the integrated data, and the overall benefits of utilizing this proposed integration technique.

Development of a statewide highway safety data warehouse: Massachusetts data warehouse and web-based access

2005 ◽  
Vol 36 (5) ◽  
pp. 465-467
Author(s):  
Heather A. Rothenberg ◽  
Robin Riessman ◽  
Davin Flatten
Keyword(s):  
Data Warehouse ◽  
Safety Data ◽  
Highway Safety ◽  
Web Based

scholarly journals Development of a Data Warehouse for Riverine and Coastal Flood Risk Management

2014 ◽  
Vol XL-2 ◽  
pp. 41-48 ◽  
Author(s):  
H. McGrath ◽  
E. Stefanakis ◽  
M. Nastev
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Data Warehouse ◽  
Structural Damage ◽  
Flood Hazard ◽  
Flood Prediction ◽  
Web Based ◽  
Emergency Responders ◽  
Spring Freshet ◽  
On Line

In New Brunswick flooding occurs typically during the spring freshet, though, in recent years, midwinter thaws have led to flooding in January or February. Municipalities are therefore facing a pressing need to perform risk assessments in order to identify communities at risk of flooding. In addition to the identification of communities at risk, quantitative measures of potential structural damage and societal losses are necessary for these identified communities. Furthermore, tools which allow for analysis and processing of possible mitigation plans are needed. Natural Resources Canada is in the process of adapting Hazus-MH to respond to the need for risk management. This requires extensive data from a variety of municipal, provincial, and national agencies in order to provide valid estimates. The aim is to establish a data warehouse to store relevant flood prediction data which may be accessed thru Hazus. Additionally, this data warehouse will contain tools for On-Line Analytical Processing (OLAP) and knowledge discovery to quantitatively determine areas at risk and discover unexpected dependencies between datasets. The third application of the data warehouse is to provide data for online visualization capabilities: web-based thematic maps of Hazus results, historical flood visualizations, and mitigation tools; thus making flood hazard information and tools more accessible to emergency responders, planners, and residents. This paper represents the first step of the process: locating and collecting the appropriate datasets.

Sign in / Sign up

Export Citation Format

Share Document