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.

scholarly journals Building a Diabetes Data Warehouse to Support Decision making in healthcare industry

2014 ◽  
Vol 16 (2) ◽  
pp. 138-143 ◽  
Author(s):  
Abubakar Ado ◽  
◽  
Ahmed Aliyu ◽  
Saifullahi Aminu Bello ◽  
Abdulra’uf Garba Sharifai ◽  
...  
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Healthcare Industry ◽  
Support Decision Making

Design and Implementation of a Data Warehouse to Support Decision-Making in a Health Environment

2018 ◽  
Vol 147 (3) ◽  
pp. 45-53
Author(s):  
Simon G. Cornejo ◽  
Karina Caro ◽  
Luis-Felipe Rodriguez ◽  
Roberto Aguilar A. ◽  
Cynthia B. Perez ◽  
...  
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Design And Implementation ◽  
Support Decision Making

scholarly journals Implemetasi Data Warehouse pada Bagian Pemasaran Perguruan Tinggi

2012 ◽  
Vol 3 (1) ◽  
pp. 315
Author(s):  
Eka Miranda ◽  
Rudy Rudy ◽  
Eli Suryani
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Data Warehouse ◽  
Ad Hoc ◽  
External Data ◽  
Study Results ◽  
Marketing Activities ◽  
Warehouse Design ◽  
Transactional Data ◽  
Support Decision Making

Transactional data are widely owned by higher education institutes, but the utilization of the data to support decision making has not functioned maximally. Therefore, higher education institutes need analysis tools to maximize decision making processes. Based on the issue, then data warehouse design was created to: (1) store large-amount data; (2) potentially gain new perspectives of distributed data; (3) provide reports and answers to users’ ad hoc questions; (4) perform data analysis of external conditions and transactional data from the marketing activities of universities, since marketing is one supporting field as well as the cutting edge of higher education institutes. The methods used to design and implement data warehouse are analysis of records related to the marketing activities of higher education institutes and data warehouse design. This study results in a data warehouse design and its implementation to analyze the external data and transactional data from the marketing activities of universities to support decision making.

scholarly journals Analisis dan perancangan data warehouse evaluasi mahasiswa studi kasus pada jurusan Teknik Informatika UMM

Repositor ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 59
Author(s):  
Mohammad Agung Alifferiza Maulana ◽  
Maskur S.Kom, M.Kom. ◽  
Wildan Soeharso
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Business Processes ◽  
Student Evaluations ◽  
Multidimensional Analysis ◽  
Quality Of Education ◽  
Management Level ◽  
Concise Information ◽  
Support Decision Making

 AbstrakDalam rangka meningkat kualitas pendidikan secara berkelanjutan diperlukan suatu trobosan yang mampu melakukan analisa proses bisnis yang bertujuan untuk memudahkah eksekutif dalam melakukan pengambilan keputusan. Untuk mengatasi hal tersebut dibangun sebuah data warehouse yang berdasarkan laporan borang akreditasi khusus standard 3 mengenai mahasiswa. Penerapan data warehouse berguna untuk mendukung pengambilan keputusan di tingkat management. Model data warehouse yang di gunakan dalam penelitian ini adalah menggunakan Skema Star, dan menggunakan nine Step Metodology. Dengan pembangunan data warehouse, diharapkan dapat menghasilkan informasi yang berkenaan dengan evaluasi mahasiswa berdasarkan standard akreditasi dengan lebih cepat, sesuai dengan kebutuhan, serta menghasilkan informasi yang lebih ringkas. Dengan menggunakan sistem data warehouse juga dapat dihasilkan analisis multidimensi  yang bersifat informasi analitis. Sehingga bermanfaat dalam pengambilan keputusan berkenaan dengan evaluasi mahasiswa.AbstractIn order to improve the quality of education sustainably, a breakthrough is needed. A breakthrough that able to analyze business processes that aim to make the processes itself easier for executives to make decisions. Data warehouse was built to overcome that, based on the report of the third standard  special accreditation forms about students. The application of data warehouse is useful to support decision-making at the management level. The data warehouse model used in this study is using Star Scheme and uses the Nine-Step Methodology. With the construction of a data warehouse, it is expected to produce information relating to student evaluations based on accreditation standards more quickly, according to needs, and produce more concise information. Using a data warehouse system can also produce a multidimensional analysis, which is an analytical information. So it can be useful in decision making regarding to the student evaluations.

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).

Data Warehouse Performance

2011 ◽  
pp. 318-322
Author(s):  
Beixin (Betsy) Lin ◽  
Yu Hong ◽  
Zu-Hsu Lee
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Business Processes ◽  
Successful Implementation ◽  
End User ◽  
Bottom Line ◽  
User Query ◽  
Tactical Decision ◽  
Support Decision Making

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, Rudin, Buss, & Sousa, 1998). 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).

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