scholarly journals Procesamiento Analítico con Minería de Datos / Analytical Processing with Data Mining

2016 ◽  
Vol 5 (9) ◽  
pp. 22
Author(s):  
Angelino Feliciano Morales ◽  
René Edmundo Cuevas Valencia ◽  
José Mario Martínez Castro
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Business Intelligence ◽  
El Sistema ◽  
Toma De Decisiones ◽  
Analytical Processing

Este trabajo describe la utilidad e importancia de la herramienta OLAP en Business Intelligence con el fin de recomendarla a los administradores de empresas para su toma de decisiones. La tecnología OLAP permite el rápido acceso a datos mediante data warehouse, agilizando el analisis de la información. Los cubos proveen de un rápido mecanismo de búsqueda de datos y de un tiempo de respuesta uniforme, independientemente de la cantidad de datos o de la complejidad del procedimiento de búsqueda. Tomando en cuenta su funcionamiento y estructura, el sistema OLAP se clasifica en tres categorías: ROLAP, MOLAP y HOLAP. Actualmente el sistema OLAP que más se utiliza es el denominado ROLAP.

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2009 ◽  
pp. 223-229
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2011 ◽  
pp. 1013-1020
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

Evolutionary Intelligent Data Warehousing Approach to Knowledge Discovery Systems: Dynamic Cubing

2019 ◽  
Vol 13 ◽  
Author(s):  
Harkiran Kaur ◽  
Kawaljeet Singh ◽  
Tejinder Kaur
Keyword(s):  
Knowledge Discovery ◽  
Data Warehouse ◽  
Business Intelligence ◽  
End Users ◽  
Processing Stage ◽  
Information Update ◽  
On Line ◽  
Analytical Processing ◽  
Warehouse Operations ◽  
Made In

Background: Numerous E – Migrants databases assist the migrants to locate their peers in various countries; hence contributing largely in communication of migrants, staying overseas. Presently, these traditional E – Migrants databases face the issues of non – scalability, difficult search mechanisms and burdensome information update routines. Furthermore, analysis of migrants’ profiles in these databases has remained unhandled till date and hence do not generate any knowledge. Objective: To design and develop an efficient and multidimensional knowledge discovery framework for E - Migrants databases. Method: In the proposed technique, results of complex calculations related to most probable On-Line Analytical Processing operations required by end users, are stored in the form of Decision Trees, at the pre- processing stage of data analysis. While browsing the Cube, these pre-computed results are called; thus offering Dynamic Cubing feature to end users at runtime. This data-tuning step reduces the query processing time and increases efficiency of required data warehouse operations. Results: Experiments conducted with Data Warehouse of around 1000 migrants’ profiles confirm the knowledge discovery power of this proposal. Using the proposed methodology, authors have designed a framework efficient enough to incorporate the amendments made in the E – Migrants Data Warehouse systems on regular intervals, which was totally missing in the traditional E – Migrants databases. Conclusion: The proposed methodology facilitate migrants to generate dynamic knowledge and visualize it in the form of dynamic cubes. Applying Business Intelligence mechanisms, blending it with tuned OLAP operations, the authors have managed to transform traditional datasets into intelligent migrants Data Warehouse.

Analysis on Ship Equipment Consumption Data Based on Data Mining

2013 ◽  
Vol 846-847 ◽  
pp. 1141-1144
Author(s):  
Dan Dan Chen ◽  
Zhi Gang Yao
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Regression Analysis ◽  
Data Warehouse ◽  
Comprehensive Analysis ◽  
Online Analytical Processing ◽  
Consumption Data ◽  
Ship Maintenance ◽  
Analytical Processing

A comprehensive analysis on a large amount of ship equipment consumption data accumulated over the years is achieved through the establishment of data warehouse, online analytical processing, regression analysis, cluster analysis, etc. by means of data mining. The analysis results present important references for equipment guarantee department in terms of equipment preparation and carrying, etc. and provide the comprehensive analysis and utilization on massive ship maintenance support data with technical means.

scholarly journals Business Intelligence Applications; sustaining Retail Businesses in Rivers State, Nigeria

2021 ◽  
Vol 04 (03) ◽  
Author(s):  
Patrick N Nwinyokpugi ◽  
Keyword(s):  
Data Mining ◽  
Performance Management ◽  
Data Warehouse ◽  
Data Visualization ◽  
Business Intelligence ◽  
Business Sustainability ◽  
Retail Business ◽  
Retail Outlets ◽  
Rivers State ◽  
Intelligence Application

The contemporary retail outlets are no more run by manual practices given the electronic nature of company to customers (C-C); customers to customers (C-C) transactions globally. The application of business intelligence has given an edge to retail outlets operation. This study therefore, strives to examine the relationship between business intelligence application and retail business sustainability in Rivers State, Nigeria. The study used descriptive research technique through the adoption of cross- sectional survey design. Nine judgmentally sampled retail outlets especially large scale malls & superstores were studied in Port Harcourt base on their inherent electronic driven operations. Using structured closed ended questionnaire, 45 census senior managers of these sampled retail outlets were studied. Data gathered were analysed using the Pearson Product Moment Correlation Coefficient (PPMCC) statistics and presented with the aid of SPSS version 20.0 for easy interpretation. The results of analysed data showed that, the dimensions of business intelligence application which included but not limited to customers’ performance management, data warehouse, data mining and advanced data visualization significantly correlated positively with the measures of retail business sustainability being profitability and customers’ patronage. The finding also showed a high moderating effect of organizational culture on business intelligence application and retail business sustainability in Rivers State, Nigeria. Relying on the empirical findings, the study concluded that business intelligence application has positive significant relationship with retail business sustainability. It is therefore recommended that, the dimensions of business intelligence: customers’ performance management, data warehouse, data mining & advance data visualization identified in this study should be utilized as it enhances retail business operational sustainability.

scholarly journals The impact of supply chain management on business intelligence

2017 ◽  
Vol 7 (2) ◽  
Author(s):  
Audrey Langlois ◽  
Benjamin Chauvel
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Private Companies ◽  
Chain Management ◽  
Conceptual Paper ◽  
The Impact

This conceptual paper investigates the impact of the supply chain on businessintelligence (BI) in private companies. The article focuses on these two subjects in order tobroadly understand the concept of business intelligence, supply chain and characteristicsimplement such as OLAP, data warehouse or data mining. It looks at the joint advantages ofthe business intelligence and supply chain concepts and revisits the traditional BI concept. Wefound that the supply chain includes many data samples collected from the first supplier to thelast customer, which have to be analysed by the company in order to be more efficient. Basedon these observations the authors argue for why it makes sense to see the BI function as anextension of supply chain management, but moreover they show how difficult it has become toseparate BI from other IT intensive processes in the organization.

Building Multi-Dimensional Cube to Analyze Public Examination Results: A Business Intelligence Approach

2014 ◽  
Vol 622 ◽  
pp. 11-22
Author(s):  
J. Macklin Abraham Navamani ◽  
A. Kannammal ◽  
P. Ranjit Jeba Thangaiah
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Tamil Nadu ◽  
Educational Data Mining ◽  
Public Examination ◽  
Policy Framing ◽  
Government Organizations ◽  
Research Fields ◽  
Research Activities

In recent years educational data mining and data warehouse has become one of the challenging research fields. It is used to turn the raw data available in educational field into actionable information. Educational data can provide improved understanding of students’ knowledge and better assessments of their progress. Educational data mining and warehousing could help educational government organizations in taking timely and data analysis based management decisions, thus contributing to gain competitive advantages in their successful policy framing. This paper focuses on the research activities for building data warehouse/data mart to store and analyze the public examination results of higher grade students by Directorate of Government Examinations belonging to Tamil Nadu, India. The data warehousing concept comprises of architectures, tools, and algorithms for bringing together data from various sources into a single repository and making it useful for the management to directly query and extract useful information for analysis. In this paper the need of data warehouse / business intelligence for a government educational organization has been explored.

scholarly journals DataWarehouse Design Acceptance of Goods In PT Transmart Using Tools Pentaho and Tableu

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Syam Gunawan
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Design Method ◽  
Graphical Display ◽  
Central Park ◽  
Purchase Order ◽  
Analytical Processing ◽  
Points Of View ◽  
Existing Data ◽  
Analyze Data

The purpose of this research is to process the data of receipt of goods at PT Transmart Central Park, analyze the existing database in the information system of receipt of goods to get the necessary information and design the data warehouse to integrate the existing data so that obtained information to take a decision. Business Intelligence (BI) is one of information technology that can be solution to collect, store and analyze company data. Online Analytical Processing (OLAP) consists of a set of tools in use to help the process of analysis and comparison of data in the database. OLAP tools and methods help users analyze data in a data warehouse by providing a variety of dynamic graphical display data. The data warehouse design method is implemented by applying the 9 steps (Nine-Step Methodology) used by Ralph Kimball in designing the star scheme. The results achieved are data warehouse that provides the desired information and provide summary information in the form of tables and charts, can be compared between the purchase order data with receiving report is global, relevant, and integrated that can be seen from various points of view so useful for the leaders to make a decision.Keywords  : Bussinesn Intelligent,Datawarehouse,OlapNine-Step Methodology

scholarly journals AN EMPIRICAL ANALYSIS ON DATA MINING SYSTEM AND ITS TECHNIQUES .

2020 ◽  
pp. 30-36
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
The Other ◽  
Huge Amount ◽  
Mining System ◽  
New Horizons ◽  
Analytical Processing ◽  
Data Mining System ◽  
Information Harvesting ◽  
Data Dredging

Data mining is an extraction of knowledge discovery from huge amount of data which is previously unknown and potentially useful for analytical processing and decision making. The other acronyms of data mining are such as Data archeology, Data dredging, Information harvesting and Business Intelligence. The various data mining techniques are used to find the hidden interestingness or new patter to store the data. These techniques and approaches of data mining can efficiently build the new environment for analyzing and predictions. This paper highlights data mining process and its various techniques to find the interestingness. Finally, concluded with its limitations. The objective of the paper is opens new horizons for researchers of forthcoming generations.

scholarly journals Diseño de un modelo físico de Data Warehouse para la gestión de incidencias para una empresa de telecomunicaciones, aplicando la metodología Hefesto

2019 ◽  
Vol 4 (7) ◽  
pp. 95
Author(s):  
Angélica Maribel Jaramillo-Tacuri ◽  
Segundo Leopoldo Pauta-Ayabaca
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Toma De Decisiones

<p style="text-align: justify;">La utilización de los modelos actuales de Entidad-Relación mediante los datos estructurados convierte el proceso de obtener los datos históricos en un problema complejo para realizar las consultas analíticas, esta tarea es casi imposible de analizarla y obtener los resultados esperados en el menor tiempo posible. El diseño de un Data Warehouse es el primer paso para integrar la información de varias fuentes de datos, lo que permite guardar históricos, almacenando grandes cantidades de información, y en conjunto, aplicando la metodología adecuada, los datos son integrados y depurados para luego ser procesados. Se convierte en una solución completa y fiable para aplicar Business Intelligence y para brindar el soporte necesario para una correcta toma de decisiones. Es por esto que este artículo va a proponer un diseño de una arquitectura de datos (Data Warehouse) que establecerá la integración, procesamiento y almacenamiento de la información mediante la aplicación de la metodología Hefesto, que guiará cada una de las fases y las actividades que se aplicarán en el proceso. Esta investigación va a permitir a la empresa tener una Data Warehouse con datos que puedan ser convertidos en información mediante cuadros de mando integral. Para que apoyen la gestión del área comercial y de soporte, con el objetivo de maximizar la satisfacción del cliente y evitar su deserción en la empresa. Adicional permitirá al área de soporte identificar los problemas que más se repiten y plantear un correctivo para estos.</p>

Sign in / Sign up

Export Citation Format

Share Document