scholarly journals Analisis Dan Perancangan Data Warehouse Pada PT Pelita Tatamas Jaya

2010 ◽  
Vol 1 (2) ◽  
pp. 461
Author(s):  
Choirul Huda ◽  
Jumas Ranope ◽  
Marly Lumenta ◽  
Kevin Kevin
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Inventory Control ◽  
Research Methodology ◽  
Data Warehousing ◽  
Business Leaders ◽  
Library Research ◽  
Decision Making Processes ◽  
Current Systems ◽  
Support Decision Making

The purpose of this research is to assist in providing information to support decision-making processes in sales, purchasing and inventory control at PT Tatamas Pelita Jaya. With the support of data warehouse, business leaders can be more helpful in making decisions more quickly and precisely. Research methodology includes analysis of current systems, library research, designing a data warehousing schema using bintang. The result of this research is the availability of a data warehouse that can generate information quickly and precisely, thus helping the company in making decisions. The conclusion of this research is the application of data warehouse can be a media aide related parties on PT Tatamas Pelita Jaya in decision making. 

scholarly journals Analisis Dan Perancangan Data Warehouse Pada PT Gajah Tunggal Prakarsa

2010 ◽  
Vol 1 (2) ◽  
pp. 437
Author(s):  
Choirul Huda ◽  
Bram Pangestu ◽  
Jimmy Lai ◽  
Riantoro Teja
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Data Warehouse ◽  
Research Methodology ◽  
Analysis Study ◽  
Library Research ◽  
Star Schema ◽  
Current Systems

The purpose of this helpful in making decisions more quickly and precisely. Research methodology includes analysis study was to analyze the data base support in helping decisions making, identifying needs and designing a data warehouse. With the support of data warehouse, company leaders can be more of current systems, library research, designing a data warehouse using star schema. The result of this research is the availability of a data warehouse that can generate information quickly and precisely, thus helping the company in making decisions. The conclusion of this research is the application of data warehouse can be a media aide related parties on PT. Gajah Tunggal initiative in decision making. 

scholarly journals O conceito de datawarehousing aplicado à gestão de informações em bibliotecas The data warehousing concept applied to information management in libraries

2011 ◽  
Vol 8 (2) ◽  
pp. 114
Author(s):  
Maurício Ferreira Santana
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Data Warehouse ◽  
Information Management ◽  
Data Warehousing ◽  
Historical Information ◽  
Amount Of Information ◽  
Analytical Results ◽  
Decision Making Processes

Propõe que a arquitetura de data warehouse seja um referencial para implantação em bibliotecas. Esta proposta tem origem na preocupação com o grande volume de informações existente nesses setores, em nível operacional, gerencial e estratégico, e com uma forma efetiva de geração de informações históricas de acervo, clientes (usuários) e custos para o processo decisório. Através de revisão bibliográfica sobre a arquitetura de data warehouse, apresenta-se a arquitetura proposta por Ralph Kimball, em esquema dimensional, tomando-se como exemplo o processo “aquisição”. Espera-se que bibliotecas possam se valer desta arquitetura para obter resultados analíticos similares aos de empresas que já lançam mão desta tecnologia.AbstractPropose the data warehouse architecture as a reference to be applied in libraries. This proposal has began with the concern about the large amount of information existing in these sectors, at the operational, managerial and strategic levels, and as an effective way to generate historical information of collection, customers (users) and costs, for decision making processes. Through literature review about data warehouse architecture, it is presented the architecture proposed by Ralph Kimball, in a dimensional scheme, taking as example the process of "acquisition". It is expected that libraries can use this architecture to obtain analytical results similar to those of companies that already make use of such technology. 

Organizational Data Warehousing

2011 ◽  
pp. 692-698
Author(s):  
John Wang ◽  
Xiaohua Hu ◽  
Dan Zhu
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Data Warehouse ◽  
Query Language ◽  
Support Systems ◽  
Data Warehousing ◽  
End Users ◽  
Executive Information Systems ◽  
Support Decision Making ◽  
Scale Data

A data warehouse (or smaller-scale data mart) is a specially prepared repository of data created to support decision making. Data are extracted from source systems, cleaned/scrubbed, transformed, and placed in data stores (Gorla, 2003). A data warehouse has data suppliers who are responsible for delivering data to the ultimate end users of the warehouse, such as analysts, operational personnel, and managers. The data suppliers make data available to end users either through structured query language (SQL) queries or custom-built decision-support applications, including decision support systems (DSS) and executive information systems (EIS).

Knowledge Discovery and Geographical Databases

2005 ◽  
pp. 308-312
Author(s):  
Sami Faïz
Keyword(s):  
Decision Making ◽  
Knowledge Discovery ◽  
Data Warehouse ◽  
Data Warehousing ◽  
Geographic Information ◽  
Data Sources ◽  
Multi Scale ◽  
Volatile Collection ◽  
Great Progress ◽  
Decision Making Processes

Geographic data are characterized by huge volumes, lack of standards, multiplicity of data sources, multi-scale requirements, and variability in time. These characteristics make geographic information complex and uncertain. At the same time, the important growth of the quantity of data manipulated and the necessity to make rapid decisions imposed the appearance and the great progress of new tools like data warehousing techniques. Data warehouse is usually defined as a subject-oriented, integrated, time-variant, and non-volatile collection of data in support of decision-making processes.

Data Warehouse Support for Policy Enforcement Rule Formulation

2019 ◽  
pp. 255-273
Author(s):  
Deepika Prakash
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Conversion Process ◽  
Tool Support ◽  
Policy Enforcement ◽  
Decision Making Problem ◽  
Operational Decision Making ◽  
Early Information ◽  
Support Decision Making ◽  
Policy Violation

It is believed that a data warehouse is for operational decision making. Recently, a proposal was made to support decision making for formulating policy enforcement rules that enforce policies. These rules are expressed in the WHEN-IF-THEN form. Guidelines are proposed to elicit two types of actions, triggering actions that cause the policy violation and the corresponding correcting actions. The decision-making problem is that of selecting the most appropriate correcting action in the event of a policy violation. This selection requires information. The elicited information is unstructured and is “early.” This work is extended by proposing a method to directly convert early information into its multi-dimensional form. For this, an early information mode is proposed. The proposed conversion process is a fully automated one. Further, the tool support is extended to accommodate the conversion process. The authors also apply the method to a health domain.

Using OLAP Tools for e-HRM

2011 ◽  
pp. 252-265
Author(s):  
Alysson Bolognesi Prado ◽  
Carmen Freitas ◽  
Thiago Ricardo Sbrici
Keyword(s):  
Decision Making ◽  
Human Resources ◽  
Data Warehouse ◽  
End Users ◽  
Web Portal ◽  
Business Information ◽  
On Line ◽  
Analytical Processing ◽  
Resource Data ◽  
Support Decision Making

In the growing challenge of managing people, Human Resources need effective artifacts to support decision making. On Line Analytical Processing is intended to make business information available for managers, and HR departments can now encompass this technology. This paper describes a project in which the authors built a Data Warehouse containing actual Human Resource data. This paper provides data models and shows their use through OLAP software and their presentation to end-users using a web portal. The authors also discuss the progress, and some obstacles of the project, from the IT staff’s viewpoint.

Data Warehouse Performance

2011 ◽  
pp. 580-585 ◽  
Author(s):  
Beixin ("Betsy") Lin ◽  
Yu Hong ◽  
Zu-Hsu Lee
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Performance Tuning ◽  
Data Warehouses ◽  
User Query ◽  
Data Volume ◽  
Predictive Data Mining ◽  
Support Decision Making ◽  
Over Time

A data warehouse is a large electronic repository of information that is generated and updated in a structured manner by an enterprise over time to aid business intelligence and to support decision making. Data stored in a data warehouse is non-volatile and time variant and is organized by subjects in a manner to support decision making (Inmon et al., 2001). Data warehousing has been increasingly adopted by enterprises as the backbone technology for business intelligence reporting and query performance has become the key to the successful implementation of data warehouses. According to a survey of 358 businesses on reporting and end-user query tools, conducted by Appfluent Technology, data warehouse performance significantly affects the Return on Investment (ROI) on Business Intelligence (BI) systems and directly impacts the bottom line of the systems (Appfluent Technology, 2002). Even though in some circumstances it is very difficult to measure the benefits of BI projects in terms of ROI or dollar figures, management teams are still eager to have a “single version of the truth,” better information for strategic and tactical decision making, and more efficient business processes by using BI solutions (Eckerson, 2003). Dramatic increases in data volumes over time and the mixed quality of data can adversely affect the performance of a data warehouse. Some data may become outdated over time and can be mixed with data that are still valid for decision making. In addition, data are often collected to meet potential requirements, but may never be used. Data warehouses also contain external data (e.g. demographic, psychographic, etc.) to support a variety of predictive data mining activities. All these factors contribute to the massive growth of data volume. As a result, even a simple query may become burdensome to process and cause overflowing system indices (Inmon et al., 1998). Thus, exploring the techniques of performance tuning becomes an important subject in data warehouse management.

Activity-Based Methodology for Development and Analysis of Integrated DoD Architectures

2007 ◽  
pp. 85-113 ◽  
Author(s):  
S. Ring
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Process Models ◽  
Fully Integrated ◽  
The Core ◽  
Analysis Strategies ◽  
Decision Making Processes ◽  
Integrated Architecture ◽  
Architecture Development ◽  
Support Decision Making

This chapter describes the activity-based methodology (ABM), an efficient and effective approach to-ward development and analysis of DoD integrated architectures that will enable them to align with and fully support decision-making processes and mission outcomes. ABM consists of a tool-independent disciplined approach to developing fully integrated, unambiguous, and consistent DODAF Operational, System, and Technical views in supporting both “as-is” architectures (where all current elements are known) and “to-be” architectures (where not all future elements are known). ABM enables architects to concentrate on the Art and Science of architectures—that is identifying core architecture elements, their views, how they are related together, and the resulting analysis used for decision-making purposes. ABM delivers significant architecture development productivity and quality gains by generating several DoDAF products and their elements from the core architecture elements. ABM facilitates the transition from integrated “static” architectures to executable “dynamic” process models for time-dependent assessments of complex operations and resource usage. Workflow steps for creating integrated architecture are detailed. Numerous architecture analysis strategies are presented that show the value of integrated architectures to decision makers and mission outcomes.

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