Adequação do processo de desenvolvimento de Data Warehouse para empresa varejista que utiliza software Cots

2014 ◽  
Vol 5 (2) ◽  
pp. 156-171
Author(s):  
Cleisson Fabricio Leite Batista ◽  
Mario Godoy Neto ◽  
Ellen Polliana Ramos Souza
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Data Mart ◽  
Commercial Off The Shelf

A demanda por informação é cada vez mais frequente em pequenas, médias e grandes empresas, que precisam tomar decisões de forma rápida para manterem-se competitivas. Visando atender não somente a demanda de mercado, mas suprir a carência de muitas organizações no que diz respeito à transformação de dados em informação, surgiram as soluções de Business Intelligence (BI) baseadas em dados, tais como Data Warehouse (DW) e Data Mart (DM). O desenvolvimento destas soluções de BI, entretanto, está ainda muito longe da realidade da maioria das empresas brasileiras, em especial daquelas de médio e pequeno porte que, em geral, utilizam software de prateleira ou Commercial off-the-shelf (COTS). Os processos de construção de DW/DM são direcionados para software desenvolvidos sob encomenda, que contam com a participação efetiva dos analistas dos sistemas transacionais, projetistas e administradores de Banco de Dados, não contemplando as especificidades do processo de desenvolvimento de um DW/DM para Pequenas e Médias Empresas (PME) que fazem uso de software COTS. Neste sentido, este artigo relata as oportunidades e desafios enfrentados em um estudo de caso onde foi realizada a construção de Data Warehouse, para uma empresa varejista de médio porte que utiliza COTS na operacionalização dos seus processos de negócio.

scholarly journals Inteligencia de negocios para la Empresa de Servicios de la Unión del Níquel [Business Intelligence for Company of Services to the Union of the Nickel]

2016 ◽  
Author(s):  
Vladimir Alberto Torres Torres ◽  
Édgar Núñez Torres ◽  
Yanet Molina Hernández ◽  
Daykenis Caballero feria ◽  
Yanet Peña González ◽  
...  
Keyword(s):  
Data Integration ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Food Services ◽  
Data Mart ◽  
Design And Implementation ◽  
Toma De Decisiones ◽  
High Level ◽  
A Company

La Inteligencia de Negocios es una estrategia que ha alcanzado un nivel elevado en la competitividad empresarial. Aplicar una solución de Inteligencia de Negocios parte de los sistemas de origen de datos que posee una organización, apoyándose de un conjunto de herramientas encargadas de la extracción, depuración y consolidación de los datos. Esta información será almacenada en un Data Warehouse o en los Data Mart, los cuales son unidades más pequeñas orientadas a áreas específicas o un tema en particular. Esta investigación realiza el diseño e implementación de un Data Mart como solución de Inteligencia de Negocios para los servicios de alimentación prestados por la Empresa de Servicios a la Unión del Níquel (Esuni), radicada en Moa (Cuba). Se emplearon las herramientas Pentaho Bussiness Intelligence, Pentaho Data Integration 4.2.1, Pentaho Schema Workbench, PostgreSQL 9.0 y Embarcadero ERStudio 8.0.que permitieron la construcción del Data Mart y fue seleccionada la metodología Ralph Kimball para el diseño de la arquitectura y Hefesto para el desarrollo del mercado de datos, permitiendo que la información generada por los servicios gastronómicos se encuentre en un lugar específico, depurada y consolidada sirva como soporte a la toma de decisiones en la empresa.Palabras claves: Servicios Gastronómicos, Mercado de datos, Pentaho.The Intelligence of Business is a strategy that has reached a high level when of managerial competitiveness it is. Applying a solution of Business Intelligence it begin with the systems data origin that it possesses a company, leaning on a tools group in charge of the extraction, purification and consolidation of the data. This information will be stored in a Data Warehouse or in Data Mart which are smaller units guided in to specific areas or a particular topic. In this investigation is carried out the design and implementation of a Data Mart like solution of Business Intelligence for gastronomic services for the Company of Services to the Union of Nickel which resides in the municipality of Moa. Several tools were used that allowed the construction of the Data Mart and Hefesto was the methodology selected for the development of the same. Allowing that all the information generated by the gastronomic services is in a specific place purified and consolidated serves like support to the taking of decisions in the gastronomic services of the Esuni.Keywords: Food Services, Data Mart, Pentaho

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.

scholarly journals Business intelligence for social media interaction in the travel industry in Indonesia

2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Michael Yulianto ◽  
Abba Suganda Girsang ◽  
Reinert Yosua Rumagit
Keyword(s):  
Social Media ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Marketing Activity ◽  
Social Media Data ◽  
Current Technology ◽  
Media Interaction ◽  
Travel Industry ◽  
Media Data ◽  
Analyze Data

Electronic ticket (eticket) provider services are growing fast in Indonesia, makingthe competition between companies increasingly intense. Moreover, most of them have the sameservice or feature for serving their customers. To get back the feedback of their customers, manycompanies use social media (Facebook and Twitter) for marketing activity or communicatingdirectly with their customers. The development of current technology allows the company totake data from social media. Thus, many companies take social media data for analyses. Thisstudy proposed developing a data warehouse to analyze data in social media such as likes,comments, and sentiment. Since the sentiment is not provided directly from social media data,this study uses lexicon based classification to categorize the sentiment of users’ comments. Thisdata warehouse provides business intelligence to see the performance of the company based ontheir social media data. The data warehouse is built using three travel companies in Indonesia.As a result, this data warehouse provides the comparison of the performance based on the socialmedia data.

scholarly journals Business Intelligence and Data Warehouse Technologies for Traffic Accident Data Analysis in Botswana

2021 ◽  
Author(s):  
Monkgogi Mudongo ◽  
Edwin Thuma ◽  
Nkwebi Peace Motlogelwa ◽  
Tebo Leburu-Dingalo ◽  
Pulafela Majoo
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Traffic Accidents ◽  
Managerial Decision ◽  
Vehicle Registration ◽  
Accident Severity ◽  
Accident Data ◽  
Causes Of Deaths

Road traffic accidents are a serious problem for the nation of Botswana. A large amount of money is used to compensate those who are affected by road accidents. Traffic accidents are one of the major causes of Deaths in Botswana. It is important for relevant organizations to have a reliable source of data for accurate evaluation of traffic accidents. Similarly, data on vehicle registration must be transformed and be readily available to assist managerial decision makers. In this article, we deploy a Business Intelligence (BI) and Data Warehouse (DW) solution in an attempt to assist the relevant departments in their road traffic accidents and vehicle registration evaluation. In Our evaluation of the traffic accidents our findings suggest that across accident severity, Damage Only accidents had the most interesting recent trend with a 11.93% decrease in the last 3 years on record. Count of Accident Severity for Damage Only accidents dropped from 13,491 to 11,881 between 2018 and 2020 whilst Minor accidents experienced the longest period of growth. Most accidents take place in rural locations and more accidents take place during the weekend. At 28,439, Sunday had the highest number of accidents and was 47.59% higher than Wednesday, which had the lowest count of accidents at 19,269. The results for vehicle registration reveal that the number of vehicle registration decreased for the last 3 years on record. The number of vehicles registered dropped from 65535 to 24457 during its steepest decline between 2019 and 2021.

IT Architecture and Information Quality in Data Warehouse and Business Intelligence Environments

2014 ◽  
pp. 112-127
Author(s):  
Samuel Otero Schmidt ◽  
Edmir Parada Vasques Prado
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Information Quality ◽  
Relevant Literature ◽  
Research Model ◽  
Structured Interviews ◽  
It Architecture ◽  
Financial Company ◽  
The Relationship

Organizations are currently investing more in information technology to store and process a vast amount of information. Generally, this information does not comply with any standard, which hinders the decision-making process. The cause of the difficulties can be attributed to Information Quality (IQ), which has technical characteristics related to the architecture used in Data Warehouse (DW) and Business Intelligence (BI) environments. On the basis of the relevant literature in IQ, DW, and BI, a research model was created to identify the relations between components of DW/BI architecture and IQ dimensions. This research model was applied in a real case study (Big Financial Company in Brazil). This case study involved semi-structured interviews with managers and analysts. This chapter attempts to provide a better understanding of the relations between IT architecture and IQ in DW and BI environments. The authors hope to motivate the discussion around the development of IQ-oriented architectures for BI and the relationship between these concepts.

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.

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.

Do Users Go Both Ways?

2010 ◽  
Vol 1 (3) ◽  
pp. 15-33
Author(s):  
Hamid Nemati ◽  
Brad Earle ◽  
Satya Arekapudi ◽  
Sanjay Mamani
Keyword(s):  
Literature Review ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Information Needs ◽  
Project Team ◽  
Market Place ◽  
Tool Selection ◽  
Road Map ◽  
Perceived Values ◽  
User Groups

A challenging task for a data warehouse team is identifying users by their information needs and skills, and then providing the BI (Business Intelligence) tools that support each group to do their job effectively and efficiently. Recent studies have shown that the BI market place is saturated with a bewildering array of capabilities, functions and software suites. The current lack of consistent interpretation of Business Intelligence has created some confusion in the market place. This paper defines a framework to identify different user groups in an organization and map their needs and requirements to the different functionalities offered by different BI tool vendors. Through literature review, clear definitions of users were created and a set of BI tools that identifies functional needs was established. From that information, a questionnaire was developed that probed for the relationships between user types, tools, functions and other perceived values. Responses from 154 professionals were then used to develop a road map for the data warehouse project team in BI tool selection.

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