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

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.

scholarly journals Implementasi Aplikasi Business Intelligence Untuk Memonitor Efisiensi Pengelolaan Rumah Sakit

2020 ◽  
Vol 4 (3) ◽  
pp. 495
Author(s):  
Herwanto Herwanto ◽  
Ali Khumaidi
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Hospital Performance ◽  
Hospital Environment ◽  
Accurate Information ◽  
Hospital Operations ◽  
The Right ◽  
Measuring Tool ◽  
A Company

The need for fast and accurate information has become the need of every company, including hospitals. This is one of the factors that makes a company superior to other companies. In making the right and accurate decisions, leaders need information that is presented clearly, easily understood, on time, and in accordance with needs. To support the presentation of such information a database, data warehouse and other applications that are easy to understand are needed. Hospitals as a socio-economic institution are not only required to provide solutions to health problems but are also demanded to always improve the quality of their services. For this purpose to be achieved, its management must be efficient. Service indicators can be used as a measuring tool to assess the level of management efficiency. Business Intelligence (BI) application is one form of implementation that is able to facilitate management to monitor hospital performance. This research explores the use of information technology to build BI applications, reviewing the right approach in building BI applications, as well as several important aspects that must be considered for the system work for the hospital environment. There are two main stages in building this application, namely: building a data warehouse originating from an electronic medical record database and hospital operations, and building a BI application. With the formation of this BI application, it is very useful for hospital management in managing their institutions better

Business Intelligence Architecture in Support of Data Quality

2014 ◽  
pp. 363-381
Author(s):  
Tom Breur
Keyword(s):  
Data Integration ◽  
Data Quality ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Stakeholder Management ◽  
Source Data ◽  
Information Products ◽  
Quality Issues

Business Intelligence (BI) projects that involve substantial data integration have often proven failure-prone and difficult to plan. Data quality issues trigger rework, which makes it difficult to accurately schedule deliverables. Two things can bring improvement. Firstly, one should deliver information products in the smallest possible chunks, but without adding prohibitive overhead for breaking up the work in tiny increments. This will increase the frequency and improve timeliness of feedback on suitability of information products and hence make planning and progress more predictable. Secondly, BI teams need to provide better stewardship when they facilitate discussions between departments whose data cannot easily be integrated. Many so-called data quality errors do not stem from inaccurate source data, but rather from incorrect interpretation of data. This is mostly caused by different interpretation of essentially the same underlying source system facts across departments with misaligned performance objectives. Such problems require prudent stakeholder management and informed negotiations to resolve such differences. In this chapter, the authors suggest an innovation to data warehouse architecture to help accomplish these objectives.

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>

scholarly journals An Efficient Multi-Phase Blocking Strategy for Entity Resolution in Big Data

2020 ◽  
Vol 9 (9) ◽  
pp. 254-263
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Data Warehouse ◽  
Real World ◽  
Business Intelligence ◽  
Time Complexity ◽  
Entity Resolution ◽  
Second Phase ◽  
Multi Phase ◽  
State Of Art

Entity Resolution (ER) is the process of identifying records that refer to the same real-world entity. It plays a key role in many applications as data warehouse, data integration, and business intelligence. Comparing every record with all corresponding records is infeasible especially for a big dataset. To overcome such a problem, blocking techniques have been implemented. In this paper, we propose a novel Efficient Multi-Phase Blocking Strategy (EMPBS) for resolving duplicates in big data. As per our knowledge, some state of art blocking techniques may result in overlapping blocks (i.e. Q-grams) which cause redundant comparisons and hence increase the time complexity. Our proposed blocking strategy has disjoint blocks and less time complexity compared to Q-grams and slandered blocking techniques. In addition, EMPBS is general and requires no restrictions on the type of blocking keys. EMPBS consists of three phases. The first one generates three single efficient blocking keys. The second phase takes the output of the first phase as an input to construct a compound key. The compound key is composed of concatenation of two single blocking keys. Three compound blocking keys are the output of this phase that will be used as an input for the last phase, which is generating the Efficient Multi-Phase Blocking Key (EMPBK). EMPBK is constructed using the union of two compound blocking keys. The implementation of EMPBS presents promising results in terms of Reduction Ratio (RR) as it achieves a higher value of RR than adopting only a single blocking key, while at the same time maintains nearly the same precision and recall. EMPBS reduced about 84% of the average number of comparisons accomplished in a single blocking key. To evaluate EMPBS, we developed a Duplicate Generation tool (DupGen) that accepts a clean semi-structured file as an input and generates labeled duplicate records according to certain criteria.

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.

Perancangan dan Implementasi Data Warehouse Penjualan (Studi Kasus: Northwind Sample Database)

2021 ◽  
Vol 10 (1) ◽  
pp. 175
Author(s):  
Ni Putu Novia Ardiyanti ◽  
Muhammad Firdaus Zulkarnain ◽  
I Wayan Wijaya Kusuma Sandi ◽  
I Dewa Ngurah Tri Hendrawan ◽  
Ida Bagus Made Mahendra
Keyword(s):  
Data Integration ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Design Methodology

Analisa yang kompleks sangat dibutuhkan dalam pengambilan keputusan bisnis suatu perusahaan. Untuk mendukung proses analisa tersebut, digunakan data warehouse yang dianggap efektif membantu proses analisis, perancangan dan pengambilan keputusan bisnis. Umumnya perusahaan akan membangun data warehouse untuk menyimpan data operasional yang berguna dalam proses analisa bisnis, sehingga informasi yang diinginkan perusahaan dapat diperoleh dengan lebih mudah. Pada penelitian ini akan dilakukan proses perancangan dan implementasi data warehouse, yang menggunakan database northwind sebagai sumber datanya. Untuk merancang data warehouse, digunakan metode nine-step design methodology dan Pentaho Data Integration Software untuk proses implementasinya. Dari hasil perancangan dan implementasi tersebut akan terbentuk sebuah tabel fakta penjualan yang berisi informasi – informasi yang berguna untuk membantu analisis dan pengambilan keputusan bisnis perusahaan, yang pada penelitian ini divisualisasikan dengan menggunakan aplikasi Microsoft Power Business Intelligence.

scholarly journals Tableau Business Intelligence Using the 9 Steps of Kimball’s Data Warehouse & Extract Transform Loading of the Pentaho Data Integration Process Approach in Higher Education

2021 ◽  
Vol 3 (1) ◽  
pp. 1-11
Author(s):  
Indrabudhi Lokaadinugroho ◽  
Abba Suganda Girsang ◽  
Burhanudin Burhanudin
Keyword(s):  
Data Integration ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Process Approach ◽  
Integration Process ◽  
Analysis Phase ◽  
Source Data ◽  
Data Source ◽  
Collection Phase ◽  
Descriptive Method

This paper discusses about how to build a data warehouse (DW) in business intelligence (BI) for a typical marketing division in a university. This study uses a descriptive method that attempts to describe the object or subject under study as it is, with the aim of systematically describing the facts and characteristics of the object under study precisely. In the elaboration of the methodology, there are four phases that include the identification and source data collection phase, the analysis phase, the design phase, and then the results phase of each detail in accordance with the nine steps of Kimball’s data warehouse and the Pentaho Data Integration (PDI). The result is a tableau as a tool of BI that does not have complete ETL tools. So, the process approach in combining PDI and DW as a data source certainly makes a tableau as a BI tool more useful in presenting data thus minimizing the time needed to obtain strategic data from 2-3 weeks to 77 minutes.

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.

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