Data Mart

Data is raw facts and figures and information is meaningful data that would be helpful for a person or company. Business intelligence extracts information from raw data through tools like data mining, perspective analysis, online analytical processing etc. Business Intelligence (BI) can be used to support a wide range of business decisions ranging from operational to strategic.
The textbook will provide comprehensive information to readers about business intelligence and analytics.

Mash Industries ltd is a Kenyan manufacturing company specializing in the production of hygiene products. The company was established in 1962 in Nairobi, Kenya the company is committed to developing new products for the east African market, as well as innovating products to satisfy the ever changing needs & preferences of the fast growing middle-class consumers. The company also manufactures branded products such as soft tissues and sanitary towels for various other supermarket chains, that sell them as own brands. Mash Industries ltd has branches across east Africa, including in Uganda, Tanzania, and Rwanda. They have a catalogue of over 10 brand products. Mash Industries ltd is involved in wholesale of its products through its warehouse outlets, as well as retail sales by way of its chain of five retail shops across the city of Nairobi.

Inmon e Kimball possuem diferentes abordagens sobre DWs e DMs. Para Inmon, Data Warehouses e Data Marts tem estruturas essencialmente diferentes, onde, é difícil integrar um conjunto de Data Marts e mesmo que consiga não resultará num DW. (1998) Data Mart deriva do Data Warehouse. Já para Kimball, o DW é constituído pela união de todos os seus Data Marts. (1997)

The organizations always need to manage their operations, process the data electronically, and find a platform that help them to support their strategic decisions. The success of the human resource department can reflect overall organizational success. The human resource department professionals try to ensure finding the right person at the right time for the job that fits the person according to skills and qualifications. This task needs a platform that supports making the right decision based on historical managerial information. The data mart is a departmental based decision support system that used departmental data to help decision-makers to support short term decisions. Human resource (HR) data mart is the base stone for building an enterprise data warehouse. The paper presents the implementation process of HR data mart starting from implementing data mart schema to online analytical processing (OLAP) reports. The data mart is implemented on retired employees' data of Basra Oil Company for over 15 years. A human resource data mart can provide a base platform to perform a different analysis operation to support the right decisions. Different OLAP reports are implemented to help analysts and decision-makers to get the answers for their questions as OLAP queries. Two categories of reports are implemented offline reports using Microsoft Excel Pivot Table 2010 and web OLAP reports using SQL Server Reporting Service 2014 (SSRS). The tools used to implement data mart vary from SQL Server Management Services (SSMS) 2014, SQL Server Integration Service 2014 (SSIS), SQL Server Analytical Service 2014 (SSAS), SQL Server Reporting Service 2014 (SSRS), SQL Server Data Tools 2013 (SSDT), and Microsoft Excel Pivot Table 2010.

A data warehouse integrates data from various and heterogeneous data sources and creates a consolidated view of the data that is optimized for reporting and analysis. Today, business and technology are constantly evolving, which directly affects the data sources. New data sources can emerge while some can become unavailable. The DW or the data mart that is based on these data sources needs to reflect these changes. Various solutions to adapt a data warehouse after the changes in the data sources and the business requirements have been proposed in the literature [1]. However, research in the problem of DW evolution has focused mainly on managing changes in the dimensional model while other aspects related to the ETL, and maintaining the history of changes has not been addressed. The paper presents a Meta Data vault model that includes a data vault based data warehouse and a master data management. A major area of focus in this research is to keep both history of changes and a "single version of the truth," through an MDM, integrated with the DW. The paper also outlines the load patterns used to load data into the data warehouse and materialized views to deliver data to end-users. To test the proposed model, we have used big data sets from the biomedical field and for each modification of the data source schema, we outline the changes that need to be made to the EDW, the data marts and the ETL.

A data warehouse integrates data from various and heterogeneous data sources and creates a consolidated view of the data that is optimized for reporting and analysis. Today, business and technology are constantly evolving, which directly affects the data sources. New data sources can emerge while some can become unavailable. The DW or the data mart that is based on these data sources needs to reflect these changes. Various solutions to adapt a data warehouse after the changes in the data sources and the business requirements have been proposed in the literature [1]. However, research in the problem of DW evolution has focused mainly on managing changes in the dimensional model while other aspects related to the ETL, and maintaining the history of changes has not been addressed. The paper presents a Meta Data vault model that includes a data vault based data warehouse and a master data management. A major area of focus in this research is to keep both history of changes and a " single version of the truth, " through an MDM, integrated with the DW. The paper also outlines the load patterns used to load data into the data warehouse and materialized views to deliver data to end-users. To test the proposed model, we have used big data sets from the biomedical field and for each modification of the data source schema, we outline the changes that need to be made to the EDW, the data marts and the ETL. KEYWORDS

One of the best ways to enhance the performance of all companies and manage Customer Satisfaction is to get the consumers' complaints and analyze them in order to fix them. These complaints represent the consumers' behavior to the companies and how these company's response to them. Besides, customers' satisfaction is the main goal of all companies and this goal cannot achieve if they do not handle the customers' complaints. The paper represents a framework of complaint data mart construction where the source data are thousands of complaints about services and financial products of companies. The data mart represents the first step to implement an enterprise data warehouse (DW) to support strategic decisions. Reports are constructed to help analysts and decision-makers to support their decisions related to consumers' complaints and how to improve service quality. Two different categories of on-line analytical processing (OLAP) reports are used, offline and web OLAP reports. The two types of reports provide a deep view of the data and present the analysts with flexible charts that can be used in supporting strategic decisions. SQL Server Management Studio (SSMS), SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL Server Reporting Services (SSRS) 2014 beside SQL Server Data Tools (SSDT) 2013 is used to build the data mart staging table, schema, cube, and OLAP reports. MS Excel Pivot table 2010 is used also to import the cube and build offline reports and implementing OLAP processes. This data mart can be utilized by consumers themselves besides decision-makers and analysts. The data mart can measure how the companies fix complaints issues and prevent them from occurring again and identify the factors that influence financial customers' satisfaction.

In this paper, we study some problems linked to the integration of data in a spatio-temporal data warehouse. In many cases, the specifications of the data sets have evolved over time, especially when the observed period is large. Under those circumstances, data sources have ...

The organizations always need to manage their operations, process the data electronically, and find a platform that help them to support their strategic decisions. The success of the human resource department can reflect overall organizational success. The human resource department professionals try to ensure finding the right person at the right time for the job that fits the person according to skills and qualifications. This task needs a platform that supports making the right decision based on historical managerial information. The data mart is a departmental based decision support system that used departmental data to help decision-makers to support short term decisions. Human resource (HR) data mart is the base stone for building an enterprise data warehouse. The paper presents the implementation process of HR data mart starting from implementing data mart schema to online analytical processing (OLAP) reports. The data mart is implemented on retired employees' data of Basr...

O projeto de um data warehouse é uma empreitada de complexidade elevada para qualquer instituição, independentemente de seu porte e de seus orçamentos. Os custos, prazos e diversidades de conhecimentos envolvidos aumentam a pressão por resultados bem-sucedidos e rápidos.
Objetivando auxiliar profissionais de Business Intelligence que pretendem implementar data warehouse em sua instituição eu, arquiteto de informação e engenheiro de atributos, por mais de 30 anos, te apresento a metodologia PDW – Processo de Data Warehousing. A abordagem está centrada em um modelo de marcos que direciona o processo de desenvolvimento de data marts, enquanto propõe um conjunto de artefatos para a coleta, registro e documentação dos aspectos funcionais, não-funcionais e multidimensionais que integram a solução.
Resultado de minhas experiências no desenvolvimento de data marts e em modelagens de sistemas de informação a metodologia incorpora as melhores práticas do modelo de Melhoria de Processo de Software Brasileiro MPS.BR, do processo RUP – Rational Unified Process, da linguagem Unified Modeling Language – UML, do gerenciamento de projetos segundo o PMI, da modelagem dimensional e da clássica modelagem de dados Entidade-Relacionamento – ER. A PDW está organizada em três livros:
• O primeiro livro, este volume, apresenta a revisão da bibliografia concernente a data warehouse, conceitos sustentadores e estruturantes da proposta metodológica.
• O segundo livro apresenta a metodologia e orienta o usuário sobre sua aplicação.
• O terceiro livro contempla templates e demais artefatos acessórios da metodologia.

Conteúdo do livro:
• Conceitos direcionadores, sustentadores e estruturantes
• Data warehouse
• Modelagem dimensional
• OLAP (On-Line Analytical Processing)
• Dimensão tempo - Históricos não são coisa do passado•Principais conceitos de representação temporal
• Modelagem de dados temporal
• OLTP versus OLAP: cotidiano e estratégia
• Comparativo de metodologias para desenvolvimento de data warehouse
• Modelagem de entidade e relacionamento
• UML
• PDW e o PMI
• Engenharia de Requisitos para Data Warehousing
• Análise e Negociação de Requisitos
• Documentação de Requisitos
• Modelagem de Requisitos
• Conformidade de Requisitos
• Validação de Requisitos

A quem se destina este livro:
Profissionais de tecnologia da informação e de businsess intelligence envolvidos em projetos de desenvolvimento de data mart ou de data warehouse se beneficiarão deste livro por ser um roteiro prático e completo sobre como planejar e executar estes desenvolvimentos.

This article describes a Causal Analysis and Resolution of
a problem for the elaboration of a Data Mart – DM
specialized in Production Control and Planning – PCP.
The unified information in the proposed DM is loaded
form Management Information Systems – MIS, with
historic data from software development project. To allow
the integrated load from different data sources, it was
developed a Extract, Transform and Load – ETL process,
whose implementation has integrated data from the MIS
of Function Point Analysis and Personal Software
Process – PSP in a software factory. That allowed the
construction of a Decision Support System – DSS over the
constructed DM. This DSS allows the use of Online
Analytical Processing – OLAP to support quantitatively
the Process Areas of Measurement and Analysis – MA of
Capability Maturity Model Integration – CMMi

Modern, molecular bio-medicine is driving a growing demand for extensively annotated tissue bank specimens. With careful clinical, pathologic and outcomes annotation, samples can be better matched to the research question at hand and experimental results better understood and verified. However, the difficulty and expense of detailed specimen annotation is well beyond the capability of most banks and has made access to well documented tissue a major limitation in medical re-search. In this context, we have implemented automated annotation of banked tissue by integrating data from three clinical systems--the cancer registry, the pathology LIS and the tissue bank inventory system--through a classical data warehouse environment. The project required modification of clinical systems, development of methods to identify patients between and map data elements across systems and the creation of de-identified data in data marts for use by researchers. The result has been much more extensive a...

Every educator wants an effective system of learning, class with communicative and active students accompanied by a quality assessment. In the era of modern learning, e-learning system utilized by school institutions as a learning medium that can control the assessment quality and learning effectiveness of student. The current system is using iLearning Media, the implementation process is done there are still shortcomings in terms of interest and involvement of the student, it is necessary to redesign the system so that the effectiveness of learning to do with intelligence. This research aims to harness and develop the current system in order to know the assessment quality and learning effectiveness. The methods conducted in this research are OLAP method by utilizing datamart and requirement user analysis. The retrieval of data by distributing a questionnaire to be tested for validity and reliability. Further from this research will determine how much interest students and anyone who is active in the following subjects, then produce valid data on the implementation of the system to the assessment quality and learning effectiveness.

Keywords: Quality Assessment, Learning Effectiveness, OLAP, Datamart

This paper introduces HealthAgents, an EC-funded research project to improve the classification of brain tumours through multi-agent decision support over a secure and distributed network of local databases or Data Marts. HealthAgents will not only develop new pattern recognition methods for distributed classification and analysis of in vivo MRS and ex vivo/in vitro HRMAS and DNA data, but also define a method to assess the quality and usability of a new candidate local database containing a set of new cases, based on a compatibility score. Using its Multi-Agent architecture, HealthAgents intends to apply cutting-edge agent technology to the Biomedical field and develop the HealthAgents network, a globally distributed information and knowledge repository for brain tumour diagnosis and prognosis.

This article presents earlier results of our research works in the area of modeling Business Intelligence Systems. The basic idea of this research area is presented first. We then show the necessity of including certain users' parameters in Information systems that are used in Business Intelligence systems in order to integrate a better response from such systems. We identified two main types of attributes that can be missing from a base and we showed why they needed to be included. A user model that is based on a cognitive user evolution is presented. This model when used together with a good definition of the information needs of the user (decision maker) will accelerate his decision making process.

This article presents earlier results of our research works in the area of modeling Business Intelligence Systems. The basic idea of this research area is presented first. We then show the necessity of including certain users' parameters in Information systems that are used in Business Intelligence systems in order to integrate a better response from such systems. We identified two main types of attributes that can be missing from a base and we showed why they needed to be included. A user model that is based on a cognitive user evolution is presented. This model when used together with a good definition of the information needs of the user (decision maker) will accelerate his decision making process.

In this paper, we study some problems linked to the integration of data in a spatio-temporal data warehouse. In many cases, the specifications of the data sets have evolved over time, especially when the observed period is large. Under those circumstances, data sources have temporal, spatial and semantic heterogeneity. In order to explore and analyse spatio-temporal data sets in a SOLAP (Spatial On Line Analytical Processing) application, we propose two approaches to model heterogeneous data in multidimensional structures. The first solution consists in a unique temporally integrated cube with all the data of all epochs. The second solution consists in creating a specific cube (data mart) for each specific view that users want to analyse. The final objective is to support geographic knowledge discovery through data exploration of detailed data for an epoch and of integrated comparable data for time- variant studies. Using a practical example in the field of forestry, we evaluate the...

National-level, objective assessment in higher education has been a practice in Brazil since 1996, surviving political shifts that frequently dismantle public policies. This paper presents the Emergo Project – the assessment of Psychology students using a data mart with multiple-choice questions from national exams and students’ answers. We run two annual examinations, giving individual feedback and discussing aggregate results with faculty and students. We identified patterns for the evolution of correct answers across semester enrolled – Growing, Decreasing, Peak, Constant, and Other. Actual results in the national exam suggest that the feedback and discussions might have helped achieving superior performance standards.