Relevant bibliographies by topics / Business intelligence Software

Academic literature on the topic 'Business intelligence Software'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Business intelligence Software"

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CRISTESCU, Marian Pompiliu. "Traditional Enterprise Business Intelligence Software Compared to Software as a Service Business Intelligence." Informatica Economica 20, no. 1/2016 (March 30, 2016): 39–47. http://dx.doi.org/10.12948/issn14531305/20.1.2016.04.

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Post, Gerald V., and Albert Kagan. "Business Intelligence." International Journal of Business Intelligence Research 3, no. 3 (July 2012): 16–28. http://dx.doi.org/10.4018/jbir.2012070102.

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Data mining and business intelligence tools have been adding features and gaining uses, and statistical tools developed for data mining tasks often require advanced knowledge and training to apply. Development of these selected tools requires tradeoffs in ease of use and power. This study asks users to evaluate the various tools and attributes to identify the relative value of the various components and provide direction for improvements and new tools. Evaluating multi-attribute software is a challenging task, and this study provides a method of evaluating the data and analyzing tradeoffs. A structured equation model (SEM) is applied to the process. Each of the existing tools evaluated have different relative strengths, so it is important to match the organization’s primary tasks to the relative strengths of the tool.
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Bimonte, Sandro, Michel Schneider, and Omar Boussaid. "Business Intelligence Indicators." International Journal of Data Warehousing and Mining 12, no. 4 (October 2016): 75–98. http://dx.doi.org/10.4018/ijdwm.2016100104.

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Nowadays, more and more data are available for decisional analysis and decision-making based on different indicators. Although different decision-making technologies have been developed, the authors note the lack of a conceptual framework for the definition and implementation of these indicators. In this paper, they propose a first classification of these indicators. Furthermore, motivated by the need for formalism for the definition of these indicators at a conceptual level, they present the Business Intelligence Indicators (BI2) UML profile to represent indicators for OLAP, OLTP and streaming technologies. They also present their implementation in existing industrial tools. In addition, they show how these indicators can coexist in the same environment to exchange data through a chaining model and its implementation.
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Čižman, Anton, and Janko Černetič. "Improving Manufacturing Efficiency with Business Intelligence Software." IFAC Proceedings Volumes 30, no. 24 (September 1997): 153–56. http://dx.doi.org/10.1016/s1474-6670(17)42246-4.

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Maté, Alejandro, Juan Trujillo, Félix García, Manuel Serrano, and Mario Piattini. "Empowering global software development with business intelligence." Information and Software Technology 76 (August 2016): 81–91. http://dx.doi.org/10.1016/j.infsof.2016.04.011.

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Chugh, Ritesh, and Srimannarayana Grandhi. "Why Business Intelligence?" International Journal of E-Entrepreneurship and Innovation 4, no. 2 (April 2013): 1–14. http://dx.doi.org/10.4018/ijeei.2013040101.

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Business Intelligence (BI) is one of the fastest growing software sector and software vendors are rapidly developing multiple BI tools to support the growing data analysis needs of organisations. In order to be sustainable in a briskly changing turbulent environment, organisations need to have access to information about their operational performance. BI tools play a vital role in supporting the decision makers at different organisational levels. As these tools are becoming critical in decision making, it has become not only an information technology concern but also a management concern. Without proper governance it would be impractical to achieve the value that BI tools offer. Adopting a BI governance framework in organisations will lead to common principles and clear ownership over information. Additionally, appropriate alignment between corporate governance and BI governance can yield more benefits. This paper provides an insight into the importance and value of BI tools. Key functionalities of BI tools have also been highlighted. Different challenges in gaining true value from BI tools have been examined. Four phases of developing a BI governance framework have been illustrated. The alignment between BI governance and corporate governance has also been explored with a recommended model. Exploratory analysis of two organisations (Premier Healthcare Alliance & BellSouth Telecommunications) to identify how they have utilised BI tools and adopted BI governance has been briefly carried out. The paper posits that if the steps for developing a BI framework are adopted by organisations and the BI framework is aligned with the corporate framework, BI deployment and usage will be successful with reduced risk levels.
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Banica, Logica, Liviu Cristian Stefan, and Mariana Jurian. "Business Intelligence For Educational Purpose." Balkan Region Conference on Engineering and Business Education 1, no. 1 (August 15, 2014): 333–38. http://dx.doi.org/10.2478/cplbu-2014-0049.

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AbstractThe paper follows three main directions: business intelligence – as a software tool, companies – as an application field and top management – as target of intelligent efforts. From this symbiosis does result an advantage, scientific and data based educational tool, having the goal to give the students a tool to explore data collections and analysis methods in order to improve the management of a company and forecast its evolution. The purpose of Business Intelligence (BI) software is to help the firms on acquiring knowledge about highlights and dangerous trends, to observe the connections and to forecast the future market evolutions. From this perspective, we consider that students need to learn theory and practical application about BI. After an overview of the BI main concepts, we choose to use the facilities of Jaspersoft BI software; to model the most frequently used analysis requirements, displaying the most relevant data and key indicators, following the steps of a BI system.
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Mendoza, Rubén A. "Business Intelligence 2.0." International Journal of Business Intelligence Research 1, no. 4 (October 2010): 63–76. http://dx.doi.org/10.4018/jbir.2010100104.

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Business Intelligence 2.0 is an umbrella term used to refer to a collection of tools that help organizations extend their BI capabilities using Internet platforms. BI 2.0 tools can enable the automatic discovery of distributed software services and data stores, greatly increasing the range of market options for an organization. The development cycle for these tools is still in its early stage, and much work remains. However, some technologies and standards are already well understood in order to make a significant impact. This paper provides an overview of the eXtensible Markup Language (XML) and related technologies supporting the deployment of web services and service-oriented architectures (SOA). The author summarizes the critical importance of these technologies to the emergence of BI 2.0 tools. This paper also explores the current state of Internet-enabled BI activities and strategic considerations for firms considering BI 2.0 options.
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Tofan, Dragoş Ovidiu. "Business Intelligence Security." Review of Economic and Business Studies 9, no. 1 (June 1, 2016): 157–69. http://dx.doi.org/10.1515/rebs-2016-0030.

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AbstractExcess information characteristic to the current environment leads to the need for a change of the organizations’ perspective and strategy not only through the raw data processing, but also in terms of existing applications generating new information. The overwhelming evolution of digital technologies and web changes led to the adoption of new and adapted internal policies and the emergence of regulations at level of governments or different social organisms. Information security risks arising from the current dynamics demand fast solutions linked to hardware, software and also to education of human resources. Business Intelligence (BI) solutions have their specific evolution in order to bring their contribution to ensure the protection of data through specific components (Big Data, cloud, analytics). The current trend of development of BI applications on mobile devices brings with it a number of shortcomings related to information security and require additional protective measure regarding flows, specific processing and data storage.
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ASLAN, İpek, Aşkın DEMİRAG, and Erkut AKKARTAL. "Collaboration of Business Intelligence and Cloud Computing and Selecting the Best Cloud Business Intelligence Solution." AJIT-e Online Academic Journal of Information Technology 12, no. 46 (August 28, 2021): 29–45. http://dx.doi.org/10.5824/ajite.2021.03.002.x.

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In this study, business intelligence concept and architecture were explained from data sources to reporting with many advantages provided to institutions in the first part. Then, both cloud computing technology with its service and deployment models and the characteristics of cloud computing experienced clarified in the second part of the study. The relationship between cloud computing and business intelligence and the concept arisen from this collaboration, cloud business intelligence, were represented with its benefits and obstacles experienced by companies using this technology in the third part. Four service providers as alternatives serving cloud business intelligence solutions were selected and the criteria were determined according to the needs of the company, that would like to use a cloud business intelligence software. After all the criteria are prioritized and the alternatives are determined, the best software was chosen by using the Analytic Hierarchical Process software, called Expert Choice.
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Dissertations / Theses on the topic "Business intelligence Software"

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VENKATESH, UMA DEVI. "LITERATURE REVIEW ON BUSINESS INTELLIGENCE : BUSINESS INTELLIGENCE SOFTWARE FOR EDUCATIONAL INSTITUTIONS." Thesis, Högskolan i Halmstad, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-33523.

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Kang, Jian. "Automating business intelligence recovery in software evolution." Thesis, De Montfort University, 2009. http://hdl.handle.net/2086/2414.

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The theme of this thesis is to pave a path to vertically extract business intelligence (BI) from software code to business intelligence base, which is a tank of BI. Business intelligence is the atomic unit to build a piece of program comprehensibility in business logic point of view. It outstands because it covers all reverse engineering levels from code to specification. It refers to technologies for the localisation, extraction, analysis of business intelligence in software system. Such an approach naturally requires information transformation from software system to business intelligence base, and hence a novel set of automatic business intelligence recovery methods are needed. After a brief introduction of major issues covered by this thesis, the state of art of the area coined by the author as “business intelligence elicitation from software system”, in particular, the kinds of business intelligence that can be elicited from software system and their corresponding reverse engineering technical solutions are presented. Several new techniques are invented to pave the way towards realising this approach and make it light-weight. In particular, a programming-style-based method is proposed to partition a source program into business intelligence oriented program modules; concept recovery rules are defined to recover business intelligence concepts from the names embedded in a program module; formal concept analysis is built to model the recovered business intelligence and present business logic. The future research of this task is viewed as “automating business intelligence accumulation in Web” which is defined to bridge work in this thesis to nowadays Web computing trends. A prototype tool for recovering business intelligence from a Web-based mobile retailing system is then presented, followed by case study giving evaluation on the approach in different aspects. Finally, conclusions are drawn. Original contributions of this research work to the field of software reverse engineering are made explicit and future opportunities are explored.
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Sabanovic, Adis. "Business Intelligence Software Customers’ Understanding, Expectations and Needs." Thesis, Kristianstad University College, School of Health and Society, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-4707.

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Modern companies operate in incredibly complex and dynamic environments. This is clearly characterized by constant changes in technology and in various market forces as well as by enormous amounts of data and information that need to be gathered and analyzed every day. Governmental regulations and ongoing competitor pressures, among other external and internal factors, are issues that managers and decision makers in a company must take into a consideration when making decisions. The need for BI systems is growing stronger and businesses in various industries demand such tools that will help them stay on the edge in order to be competitive. Hence the purpose of this paper is to find out what their companies desire when choosing a BI system to work with. What are their needs and what do they expect and understand from this technological system that will hopefully make them work easier and gain their knowledge about the business they operate in.

A web questionnaire is aimed at 67 Swedish companies from various industries and the answers have been summarized and analyzed in different cross tables for comparison reasons. Respondents from the Manufacturing industry were those with the highest response rate. A model called The PET-model of BI implementation was created, as a result of the theoretical findings, and this model is used to finalize the results and the conclusions of this paper.

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Adelakun, Olawale, and Thomas Kemper. "Software-as-a-Service Business Intelligence : Adoption Criteria and Business Value." Thesis, Jönköping University, JIBS, Business Informatics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-12963.

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The area of Business Intelligence (BI) is both broad and multifaceted and is becoming an increasingly pervasive tool utilized within organizations allowing them to gain greater insight into their business operations as well as well as the way in which their customers interact with them. By enabling businesses to perform powerful, effective analytics and reporting, BI tools allow them to maximize use of their data and facilitate better planning, forecasting and the ability to have a more targeted and efficient value chain. Usage of BI tools allow organizations to not only achieve but leverage their competitive advantage. SMEs are no different in their pursuit for competitive advantage and market share but often is the case that they lack the resources in order to make the substantial investments into the software and infrastructure required to host a solution on-premise. The Software-as-a-Service (SaaS) model of allowing users to have access to powerful tools and services without having to purchase the solutions or the infrastructure needed to host it on-premise allows SMEs an ideal opportunity to perform many or all of the functionalities that on-premise BI provides. SaaS BI is a relatively new concept only a few years old, but improvements in functionality and features, reliability of service levels and lower costs are allowing it to gain traction and it is projected to increase its momentum in the next few years. The aim of this paper is to investigate the factors that lead to adoption/non-adoption, assigned importance and perceived business value of SaaS BI within SMEs. These issues will be addressed through identification of the key decision criteria that influence SMEs to adopt SaaS BI solutions over an on-premise solution and vice versa. Greater insight into the decision making process, usage and value will be investigated with the cooperation of two vendors within the BI field. A two pronged approach targeting both SaaS and on-premise BI vendors and the users is adopted in order to find out the perspective on either end and whether or not they are incongruent. Semi-structured interviews were targeted at both an on-premise vendor and a SaaS vendor and their customers. A questionnaire was deployed to clients of both these vendors. Analysis was then conducted on the findings using an integrated selection model encompassing BI and SaaS theories and concepts outlined in the paper.



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Filipčík, Zdeněk. "Nástroje Business Intelligence jako Open Source." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-150123.

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This master's thesis focuses on the issues of selecting the Business Intelligence tools distributed under open source licenses. The main objective of this work is to compare the different instruments in all layers of the Business Intelligence architecture. The first part is focused on the definition of key concepts of the main topic. Provides necessary theoretical basis for understanding the context used in the practical part, which is dedicated to comparison tools realized according to the established methodology. These tools are selected and compared with predetermined criteria. Then each tool is introduced and verbally and numerically compared with others. At the conclusion of each comparison group of instruments referred to their final assessment and recommendations. This work is intended to provide the information base needed to select the proper tools to create a complete BI solution.
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Gluchowski, Peter, Marcus Hofmann, Frieder Jacobi, Robert Krawatzeck, and André Müller. "Business-Intelligence-Umfrage 2011: Softwaregestütztes Lebenszyklusmanagement und aktuelles Dokumentationsgeschehen für Business-Intelligence-Systeme." Universitätsbibliothek Chemnitz, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-75452.

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Am Lehrstuhl Wirtschaftsinformatik II der TU Chemnitz arbeitet die Nachwuchsforschergruppe Computer-Aided Warehouse Engineering (CAWE), die seit August 2010 besteht, an einem vollständig modellgetriebenen Vorgehen zur Unterstützung des Lebenszyklus von Business-Intelligence-Systemen (BI-Systemen). Neben der Durchführung von Grundlagenforschung hat die Nachwuchsforschergruppe die Erstellung eines Software-Prototyps zum Ziel. Eine wichtige Funktionalität ist die automatische Erzeugung von Systemdokumentationen für verschiedene BI-Systeme mit dem Schwerpunkt auf den Architekturkomponenten. Im Rahmen des Forschungsprojektes führte die CAWE Nachwuchsforschergruppe unter der Leitung von Prof. Dr. Peter Gluchowski in 2011 eine bundesweite Umfrage bei mittelständischen bis großen Unternehmen zu folgendem Thema durch: „Softwaregestütztes Lebenszyklusmanagement und aktuelles Dokumentationsgeschehen für Business Intelligence-Systeme“.
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Slavětínský, Radek. "Analýza cloudových řešení Business Intelligence pro SME." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-358847.

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The thesis is focused on the analysis of presently offered products supporting Business Intelligence (BI) which are affordable for small and medium-sized enterprises (SMEs). Current BI solutions available to SMEs are mostly offered via Cloud computing, specifically in the form of Software as a Service (SaaS) as it requires low initial acquisition costs. The objectives of this thesis are to analyse the work in applications for BI in cloud that can be used by SMEs and to analyse in detail the comparison the worldwide extended reporting tools distributed as SaaS in the lower price category. The theoretical part provides a description of the Cloud computing and the BI system. In the practical part are selected following products: IBM Watson Analytics, Qlik Sense Cloud, Zoho Reports, Tableau Public and Microsoft Power BI. Practical testing of these applications was based on evaluation of the selected metrics with weights calculated by using the Fuller's triangle. Analyses and the information form the basis for comparison of selected applications. The contribution of this thesis is in discovering the strengths and weaknesses of these BI solutions. The output of this thesis can be used as a source for the selection of BI applications for SMEs.
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Bashir, Yusuf. "Next generation business intelligence software, areas for growth & opportunities for innovation." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65795.

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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 54-55).
In today's world, as the volume of business and consumer data continues to grow at an unprecedented pace, there is increasing desire to utilize that data in new and innovative and ways to provide insight and improve decision making. For businesses, data is being generated from transactions, machine logs, digital media and feeds from sensors and wireless devices at a volume and velocity not seen before. When combined with data from external sources such as partners, or from the Internet from blogs, social networking sites, YouTube, Facebook and Twitter, it has the capability to provide organizations with new insight, a more holistic picture of customer and stakeholder behavior and new ways of gaining competitive advantage. Consumers are being presented with applications of increasing analytical sophistication, leading to growing comfort in making fact-based decisions. New devices will help monitor energy usage within the home and provide insight on the optimal times to schedule devices and run household appliances. As data volumes continue to grow, systems will need to automate the uncovering of patterns and trends in data if they are to scale. Business Intelligence (BI) software, which has traditionally been used to gain insight from data, will need to evolve and new capabilities developed to support these significant changes. Areas of growth and opportunities for new innovation within the BI software industry will be explored that will enable stakeholders to take full advantage of this new and exciting opportunity.
by Yusuf Bashir.
S.M.
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Gina, Bonginkosi Phila. "Factors That Drive the Selection of Business Intelligence Tools in South African Financial Services Providers." Master's thesis, Faculty of Commerce, 2021. http://hdl.handle.net/11427/33741.

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Innovation and technology advancements in information systems (IS) result in multiple product offerings and business intelligence (BI) software tools in the market to implement business intelligence systems (BIS). As a result, a high proportion of organisations fail to employ appropriate and suitable software tools meeting organisational needs, resulting in a prime number of BI solution failures and abandoned projects are therefore recorded. Due to such project failures, benefits associated with BI are not realised hence organisations loose enormous investments on BI solutions and competitive advantage. The study aims at discovering and exploring critical factors influencing the selection of BI tools when embarking on the selection process. This is a quantitative research study and questionnaire surveyed data was collected from 92 participants working in South African financial services providers listed on the Johannesburg Stock Exchange (JSE) appearing in the top 100 based on market capitalization. The data was analysed quantitative by employing the use of SPSS and SmartPLS-3 software's to test the significance of influential factors using the proposed conceptual model that emerged from the literature. The findings showed that a combination of domain technical and non-technical factors is critical. Therefore, software tool technical factors (functionality, ease of use, compatibility, availability of an integrated hardware/software package, and availability of source code), vendor technical factors (availability of technical support, technical skills, quality of product, availability of user manual for important information, tutorial for learning and troubleshooting guide, and experience in using product developed by the same vendor), and opinion non-technical factors (end-users, subordinates, outside personnel acquaintances, and improvement in customer service) emerged as significant combination of influential factors to be considered. The study contributes to both academia and industry by providing influential determinants for software tool selection. It is hoped that the findings presented will contribute to a greater understanding of factors influencing the selection of BI tools to researchers and practitioners alike. Furthermore, organisations seeking to select and deliver appropriate BI tools will be better equipped to drive such endeavours.
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Santos, Igor Peterson Oliveira. "Um framework de testes unitários para procedimentos de carga em ambientes de business intelligence." Universidade Federal de Sergipe, 2016. http://ri.ufs.br:8080/xmlui/handle/123456789/3390.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Business Intelligence (BI) relies on Data Warehouse (DW), a historical data repository designed to support the decision making process. Despite the potential benefits of a DW, data quality issues prevent users from realizing the benefits of a BI environment and Data Analytics. Problems related to data quality can arise in any stage of the ETL (Extract, Transform and Load) process, especially in the loading phase. This thesis presents an approach to automate the selection and execution of previously identified test cases for loading procedures in BI environments and Data Analytics based on DW. To verify and validate the approach, a unit test framework was developed. The overall goal is achieve data quality improvement. The specific aim is reduce test effort and, consequently, promote test activities in DW process. The experimental evaluation was performed by two controlled experiments in the industry. The first one was carried out to investigate the adequacy of the proposed method for DW procedures development. The Second one was carried out to investigate the adequacy of the proposed method against a generic framework for DW procedures development. Both results showed that our approach clearly reduces test effort and coding errors during the testing phase in decision support environments.
A qualidade de um produto de software está diretamente relacionada com os testes empregados durante o seu desenvolvimento. Embora os processos de testes para softwares aplicativos e sistemas transacionais já apresentem um alto grau de maturidade, estes devem ser investigados para os processos de testes em um ambiente de Business Intelligence (BI) e Data Analytics. As diferenças deste ambiente em relação aos demais tipos de sistemas fazem com que os processos e ferramentas de testes existentes precisem ser ajustados a uma nova realidade. Neste contexto, grande parte das aplicações de Business Intelligence (BI) efetivas depende de um Data Warehouse (DW), um repositório histórico de dados projetado para dar suporte a processos de tomada de decisão. São as cargas de dados para o DW que merecem atenção especial relativa aos testes, por englobar procedimentos críticos em relação à qualidade. Este trabalho propõe uma abordagem de testes, baseada em um framework de testes unitários, para procedimentos de carga em um ambiente de BI e Data Analytics. O framework proposto, com base em metadados sobre as rotinas de carga, realiza a execução automática de casos de testes, por meio da geração de estados iniciais e a análise dos estados finais, bem como seleciona os casos de testes a serem aplicados. O objetivo é melhorar a qualidade dos procedimentos de carga de dados e reduzir o tempo empregado no processo de testes. A avaliação experimental foi realizada através de dois experimentos controlados executados na indústria. O primeiro avaliou a utilização de casos de testes para as rotinas de carga, comparando a efetividade do framework com uma abordagem manual. O segundo experimento efetuou uma comparação com um framework genérico e similar do mercado. Os resultados indicaram que o framework pode contribuir para o aumento da produtividade e redução dos erros de codificação durante a fase de testes em ambientes de suporte à decisão.
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Books on the topic "Business intelligence Software"

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Czernicki, Bart. Silverlight 4 Business Intelligence Software. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-3061-8.

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service), SpringerLink (Online, ed. Silverlight 4 Business Intelligence Software. Berkeley, CA: Bart Czernicki, 2010.

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Sorensen, Dean. Business performance intelligence software: A market evaluation. Morristown, NJ]: Financial Executives Research Foundation, 2003.

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service), SpringerLink (Online, ed. Next-Generation Business Intelligence Software with Silverlight 3. Berkeley, CA: Apress, 2010.

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Czernicki, Bart. Next-Generation Business Intelligence Software with Silverlight 3. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-2488-4.

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Intelligent agent software engineering. Hershey, PA: Idea Group Pub., 2003.

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Decision support systems for business intelligence. 2nd ed. Hoboken, N.J: Wiley, 2010.

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Volitich, Dan. IBM Cognos 8 Business Intelligence. New York: McGraw-Hill, 2008.

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Shafer, Dan. Designing intelligent front ends for business software. New York: Wiley, 1989.

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Bouthillier, France. Assessing competitive intelligence software: A guide to evaluating CI technology. Medford, N.J: Information Today, 2003.

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Book chapters on the topic "Business intelligence Software"

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Czernicki, Bart. "Mobile Intelligence." In Silverlight 4 Business Intelligence Software, 391–421. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-3061-8_12.

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Czernicki, Bart. "Business Intelligence 2.0 Defined." In Silverlight 4 Business Intelligence Software, 1–25. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-3061-8_1.

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Czernicki, Bart. "Applying Collective Intelligence." In Silverlight 4 Business Intelligence Software, 251–81. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-3061-8_8.

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Czernicki, Bart. "Integrating with Business Intelligence Systems." In Silverlight 4 Business Intelligence Software, 367–89. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-3061-8_11.

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Czernicki, Bart. "Business Intelligence 2.0 Defined." In Next-Generation Business Intelligence Software with Silverlight 3, 1–26. Berkeley, CA: Apress, 2009. http://dx.doi.org/10.1007/978-1-4302-2488-4_1.

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Czernicki, Bart. "Enhancing Visual Intelligence in Silverlight." In Silverlight 4 Business Intelligence Software, 219–50. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-3061-8_7.

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Czernicki, Bart. "Surfacing Silverlight Business Intelligence in SharePoint." In Silverlight 4 Business Intelligence Software, 423–42. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-3061-8_13.

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Czernicki, Bart. "Silverlight as a Business Intelligence Client." In Silverlight 4 Business Intelligence Software, 53–96. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-3061-8_3.

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Czernicki, Bart. "Adding Interactivity to Business Intelligence Data." In Silverlight 4 Business Intelligence Software, 97–134. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-3061-8_4.

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Czernicki, Bart. "Integrating with Business Intelligence Systems." In Next-Generation Business Intelligence Software with Silverlight 3, 367–93. Berkeley, CA: Apress, 2009. http://dx.doi.org/10.1007/978-1-4302-2488-4_11.

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Conference papers on the topic "Business intelligence Software"

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Ouf, Shimaa, Mona Nasr, Mohamed Amr, Mohamed Mosaad, Khaled Kamal, Fatma Mostafa, Rehab Said, and Sherook Mohamed. "Business intelligence software as a service (SAAS)." In 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN). IEEE, 2011. http://dx.doi.org/10.1109/iccsn.2011.6014350.

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Baransel, Ayse Eser, and Cesur Baransel. "Architecturing Business Intelligence for SMEs." In 2012 IEEE 36th Annual Computer Software and Applications Conference - COMPSAC 2012. IEEE, 2012. http://dx.doi.org/10.1109/compsac.2012.82.

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Ouf, Shimaa, and Mona Nasr. "Business Intelligence in the Cloud." In 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN). IEEE, 2011. http://dx.doi.org/10.1109/iccsn.2011.6014351.

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McAllister, Michael. "Success factors of Business Intelligence." In 2009 6th IEEE International Working Conference on Mining Software Repositories. MSR 2009. IEEE, 2009. http://dx.doi.org/10.1109/msr.2009.5069473.

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"AGENTS FOR MANAGING BUSINESS-TO-BUSINESS INTERACTIONS - Software Agents for Managing Business-to-Business Collaborations." In 3rd International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003180002380244.

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Tuncer, Isilay, Sevval Az, Askin Karakas, and Mehmet Gokturk. "Context-Based Corporate Business Intelligence Assistant." In 2019 1st International Informatics and Software Engineering Conference (UBMYK). IEEE, 2019. http://dx.doi.org/10.1109/ubmyk48245.2019.8965648.

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Hans, Robert T., and Ernest Mnkandla. "Modeling software engineering projects as a business: A business intelligence perspective." In AFRICON 2013. IEEE, 2013. http://dx.doi.org/10.1109/afrcon.2013.6757849.

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Kang, Jian, Jianjun Pu, Jianchu Huang, Zihou Zhou, and Hongji Yang. "Business Intelligence Recovery from Legacy Code." In 2008 32nd Annual IEEE International Computer Software and Applications Conference. IEEE, 2008. http://dx.doi.org/10.1109/compsac.2008.35.

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Puder, Arno, and Thomas Odenwald. "Augmenting Smart Items With Business Intelligence." In 2007 2nd International Conference on Communication Systems Software and Middleware. IEEE, 2007. http://dx.doi.org/10.1109/comswa.2007.382480.

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Chen, Qiming, Parvathi Chundi, Umeshwar Dayal, and Meichun Hsu. "Dynamic software agents for business intelligence applications (poster)." In the second international conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/280765.280893.

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