Journal articles: '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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Viaene, Stijn. "Linking Business Intelligence into Your Business." IT Professional 10, no. 6 (November 2008): 28–34. http://dx.doi.org/10.1109/mitp.2008.128.

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Mussa, Munir De Sá, Saulo Chaves de Souza, Eduardo Francisco da Silva Freire, Renata Gomes Cordeiro, and Henrique Rego Monteiro da Hora. "BUSINESS INTELLIGENCE IN EDUCATION: AN APPLICATION OF PENTAHO SOFTWARE." Revista Produção e Desenvolvimento 4, no. 2 (July 8, 2018): 29–41. http://dx.doi.org/10.32358/rpd.2018.v4.274.

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Poba-Nzaou, Placide, Sylvestre Uwizeyemungu, and Mariem Saada. "Critical Barriers to Business Intelligence Open Source Software Adoption." International Journal of Business Intelligence Research 10, no. 1 (January 2019): 59–79. http://dx.doi.org/10.4018/ijbir.2019010104.

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Over the past few years, managers have been hard pressed to become more data-driven, and one of the prerequisites in doing so is through the adoption of Business Intelligence (BI) tools. However (1) the adoption of BI tools remains relatively low (2) the acquisition costs of proprietary BI tools are relatively high and (3) the level of satisfaction with these BI tools remain low. Given the potential of open source BI (OSBI) tools, there is a need for analyzing barriers that prevent organizations from adopting OSBI. Drawing a systematic review and a Qualitative Survey of BI Experts, this study proposes a framework that categorizes and structures 23 barriers to OSBI adoption by organizations including 4 that were identified by BI Experts but not explicitly found in the literature. This paper contributes to OSS and Information Systems (IS) research literature on BI adoption in general and provides specific insights to practitioners.

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Jiang, Wen Rong, and Jian Chen. "Using Business Intelligence in Supply Chain Management." Advanced Materials Research 171-172 (December 2010): 769–72. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.769.

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The paper defines the connotation and technology of business intelligence, discusses its concrete applications in supply chain management and studies the rules that the enterprises should follow while using business intelligence software. Through integrating the Business Intelligence with supply chain management, these problems can be solved effectively.

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LISHCHYNSKA, Lydmyla, and Natalia DOBROVOLSKA. "PROSPECTIVE SOFTWARE TOOLS FOR DATA ANALYSIS IN BUSINESS." Herald of Khmelnytskyi National University 305, no. 1 (February 23, 2022): 78–83. http://dx.doi.org/10.31891/2307-5732-2022-305-1-78-79.

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The article presents the results of a research of promising software tools for data analysis that can be used for domestic business, their characteristics, identifying features, advantages and disadvantages of various business intelligence tools, recommendations for implementation. Data analysis tools provide an opportunity to significantly improve the company’s performance, but only when the selection of the tool, its implementation and maintenance take into account all the features of the company’s business processes. Business analytics software tools help you analyze your datasets to make informed management decisions, improve processes, and shape and achieve business goals.To choose the right business intelligence tools, you need to consider the most important factors: visual functionality, flexibility, integration, customization, mobility, learning requirements, pricing, IT support, cloud or local BI deployment, and more. In addition, you need to identify the functions that are most important to your company, will allow you to get a balanced option: meet current needs and anticipate further growth. The main functions offered by BI suppliers: debugging information panels; built-in analytics; functions of joint work; remote access by mobile devices; independent data preparation; advanced analytics, etc. Areas of use of data mining are virtually unlimited, in particular, its use by commercial enterprises that implement projects based on information repositories can gain significant advantages over competitors, as well as in strategic planning. Data is a powerful tool for business transformation, forcing companies to use modern business intelligence tools and seek experts to help understand, decipher and interpret hidden trends in the industry and the market. This is especially true when working with Вig data.

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Lakhan, Nidhi. "Applications of Data Science and AI in Business." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 4115–18. http://dx.doi.org/10.22214/ijraset.2022.43343.

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Abstract: Just like natural human intelligence, Artificial Intelligence (AI) can be explained as the intelligence demonstrated by machines, or rather how the machines and models are trained to predict outcomes and give answers in a way that mimic human intelligence. The goal of artificial intelligence is to provide software that can reason on input and explain the output through its various methods. Artificial Intelligence has found its applications in almost every sector in every industry – ranging from finance to marketing to general business management. Similarly, Data Science deals with decision making based on the insights gathered from data analysis rather than merely replying upon the decision maker's intuition and experience. This paper explores with how Data Science and Artificial Intelligence has transformed the way businesses operate and hence changing the strategic decision making and operations landscape in medium scale as well as large sized businesses. Keywords: Data Science, Artificial Intelligence, Business Analytics, Neural Networks, Recommender Systems.

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Gadish, David. "Online Technology to Manage the Real Estate Owned (REO) Process." International Journal of Business Intelligence Research 5, no. 1 (January 2014): 31–49. http://dx.doi.org/10.4018/ijbir.2014010103.

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The WinREO real estate management software provides a case for extracting business intelligence benefits for a small/medium-sized business systematically as part of other business functions. The software was developed by breaking down the complex REO sales business process into a series of data points, which can then be captured from users as part of a workflow engine. These many tables of data are used to construct tailored workflows and combined to produce meaningful reports for various users inside the organization. Tools for collaboration on the basis of shared data, performance measurement and knowledge management bring additional business intelligence benefits to a small- or medium-sized real estate office through continuous collection of data in various sections of a the software suite. This document details the software's functions, noting the various instance and implementations of data collection for these purposes.

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Zalieckaitė, Laima, and Rimvydas Skyrius. "Analitinės programinės įrangos diegimo į mokymo procesą tyrimas." Informacijos mokslai 61 (January 1, 2012): 144–55. http://dx.doi.org/10.15388/im.2012.0.1071.

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Besikeičianti ir sudėtinga ekonominė aplinka verčia verslo organizacijas sparčiai reaguoti į rinkos pokyčius ir ieškoti naujų galimybių, o tam reikalinga kokybiška ir operatyvia informacija grindžiami sprendimai. Didelė organizacijų saugomų duomenų apimtis ir įvairialypės informacijos šaltinių kiekis lemia, kad organizacijos privalo taikyti analitine programine įranga palaikomą verslo analitiką. Kita vertus, šio tipo priemonės tampa sprendimų priėmimo aplinka, kuri pateikia paprastą, interaktyvią ir intuityviai suvokiamą analitiką. Analitinės programinės įrangos rinkos plėtra ir funkcinių galimybių tobulėjimas liudija jos paklausą organizacijų vadyboje, o į tai turi reaguoti akademinės įstaigos, rengiančios vadybos specialistus. Šiame straipsnyje analizuojamos analitinės programinės įrangos taikymo mokymo procese prielaidos, problemos ir galimi įgyvendinimo metodai.Reikšminiai žodžiai: analitinė programinė įranga, verslo analitika, intelektualus analitinis duomenų apdorojimas.Research on the Implementation of Business Intelligence Software into Study ProcessLaima Zalieckaitė, Rimvydas SkyriusSummaryThe dynamic and complicated economic environment is driving businesses to respond swiftly to market changes and look for new opportunities; this drive requires decisions based on reliable and timely information. To handle huge volumes of accumulated data and a variety of information sources, companies have to apply business intelligence approaches. The set of business intelligence tools serves as a decision support environment providing simple, interactive and intuitive analytical functions. The growth of the business intelligence software market and functionality indicates its growing demand for business management; this growth has to be considered by academic institutions engaged in management studies. This paper examines the possible ways of using the business intelligence software in the study process, the possible problems and implementation modes.

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Lönnqvist, Antti, and Virpi Puhakka. "The Measurement of Business Intelligence." EDPACS 40, no. 3 (November 24, 2009): 1–14. http://dx.doi.org/10.1080/07366980903446611.

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Arvisais-Anhalt, Simone, Christoph U. Lehmann, Justin A. Bishop, Jyoti Balani, Laurie Boutte, Marjorie Morales, Jason Y. Park, and Ellen Araj. "Searching Full-Text Anatomic Pathology Reports Using Business Intelligence Software." Journal of Pathology Informatics 13 (2022): 100014. http://dx.doi.org/10.1016/j.jpi.2022.100014.

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Fombellida, J., I. Martín-Rubio, S. Torres-Alegre, and D. Andina. "Tackling business intelligence with bioinspired deep learning." Neural Computing and Applications 32, no. 17 (February 28, 2018): 13195–202. http://dx.doi.org/10.1007/s00521-018-3377-5.

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Et. al., Pooja Sharma,. "Requirement Analysis For Business Process Re-Engineering With Computational Intelligence." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 1663–66. http://dx.doi.org/10.17762/turcomat.v12i5.2143.

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There are enormous software applications which are developed for the usage of the software users. The software user may be categorized as public users who use online services through internet, and the private users who use the specific software inside a specific organization only. The private users can be the business organizations, Educational institutions, Health care centers and other government organizations. Each organization has a specific requirement on the base of which the software and the customer relationship management frame work has to be designed. Organization specific software are developed, tested and delivered to the service users by the software development companies and where under maintenance by the development team in the timely manner. At times, when these registered users are in need of alterations and modifications in the software which are used, then the business process reengineering is required to be done. Thus business process reengineering plays a vital role in the maintainability and the sustainability of the software product based on the user requirement. To create an effective Business process Re-engineering Framework, the user (or) the stakeholder requirements are considered as the prerequisite. Based on the requirement the software product would be re-engineering, the stakeholders requirements has to be initially preprocessed and the stake holders has to be grouped and prioritized with the ranking of the requirement modules that has to be implemented. Finally the risk assessment has to be done to estimate the critical conditions or the changes that would be faced while re- engineering and the requirements will be clustered for effective execution.

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Noruozzadeh, Mohamad Ali. "The Effect of Applying Business Intelligence on Improvement of Information Technology Management (Case Study: Software Companies in Tehran Province)." Revista Eletrônica em Gestão, Educação e Tecnologia Ambiental 25 (March 22, 2021): e6. http://dx.doi.org/10.5902/2236117063799.

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The purpose of this study was investigating the effect of applying business intelligence in improving information technology management of organization based on data of software companies in Tehran province using data mining techniques in Clementine environment entitled BI-IT.DM. The data is based on a sample of 12,000 data and reports from software companies in Tehran province during the years 2013 to 2018, available in the Organization of Computer Guild of Iran. The initial and proposed model of research was developed after conducting a case study in software companies in Tehran province. The most important results of this study are that the main predictors of the model are predictors of "business intelligence for business IT services strategy with data code (A5)" with predictive weight of 0.34%, predictor "business intelligence for continuous improvement of business IT services with data code (A1) with predictive weight 0.30%, predictor "business intelligence for business IT services transfer with data code (A3). Finally, in order to apply business intelligence to improve the organization's information technology management, the top companies were categorized into six groups (Class A-F), with optimal ratios of the likelihood of successful use of BI in the organization.

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Rostek, Katarzyna, Michał Wiśniewski, and Agnieszka Kucharska. "Cloud Business Intelligence for Smes Consortium." Foundations of Management 4, no. 1 (June 1, 2012): 105–22. http://dx.doi.org/10.2478/fman-2013-0006.

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Abstract Business Intelligence technology for over 20 years is the market leader in analytical processing of data. As numerous market researches demonstrate Business Intelligence has substantial affect on global competitiveness of enterprises and on the stability of their position in the market, which is particularly important in times of economic downturn. Although main users of this technology are large companies and corporations, software vendors are still looking for solutions that are also available for the SME (Small and Middle Enterprises) sector and non-profit enterprises. One option available recently is possibility to use Cloud Computing environment. The article considers the opportunities and risks posed by the organization of Cloud Business Intelligence system on the example of using it in SME sector.

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Niu, Li, Jie Lu, Guangquan Zhang, and Dianshuang Wu. "FACETS: A cognitive business intelligence system." Information Systems 38, no. 6 (September 2013): 835–62. http://dx.doi.org/10.1016/j.is.2013.02.002.

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Orcajo Hernández, Jordina, and Pau Fonseca i Casas. "Business Intelligence’s Self-Service Tools Evaluation." Technologies 10, no. 4 (August 10, 2022): 92. http://dx.doi.org/10.3390/technologies10040092.

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The software selection process in the context of a big company is not an easy task. In the Business Intelligence area, this decision is critical, since the resources needed to implement the tool are huge and imply the participation of all organization actors. We propose to adopt the systemic quality model to perform a neutral comparison between four business intelligence self-service tools. To assess the quality, we consider eight characteristics and eighty-two metrics. We built a methodology to evaluate self-service BI tools, adapting the systemic quality model. As an example, we evaluated four tools that were selected from all business intelligence platforms, following a rigorous methodology. Through the assessment, we obtained two tools with the maximum quality level. To obtain the differences between them, we were more restrictive increasing the level of satisfaction. Finally, we got a unique tool with the maximum quality level, while the other one was rejected according to the rules established in the methodology. The methodology works well for this type of software, helping in the detailed analysis and neutral selection of the final software to be used for the implementation.

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Tunowski, Remigiusz, and Jacek Jaworski. "Impact of Business Intelligence Software Implementation on Financial Condition of Bank on Ing Bank Śląski Case Study." Zeszyty Naukowe Uniwersytetu Szczecińskiego Finanse Rynki Finansowe Ubezpieczenia 79 (2016): 465–76. http://dx.doi.org/10.18276/frfu.2016.79-36.

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Golfarelli, Matteo, and Stefano Rizzi. "What-if Simulation Modeling in Business Intelligence." International Journal of Data Warehousing and Mining 5, no. 4 (October 2009): 24–43. http://dx.doi.org/10.4018/jdwm.2009080702.

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Optimizing decisions has become a vital factor for companies. In order to be able to evaluate beforehand the impact of a decision, managers need reliable provisional systems. Though data warehouses enable analysis of past data, they are not capable of giving anticipations of future trends. What-if analysis fills this gap by enabling users to simulate and inspect the behavior of a complex system under some given hypotheses. A crucial issue in the design of what-if applications is to find an adequate formalism to conceptually express the underlying simulation model. In this paper the authors report on how, within the framework of a comprehensive design methodology, this can be accomplished by extending UML 2 with a set of stereotypes. Their proposal is centered on the use of activity diagrams enriched with object flows, aimed at expressing functional, dynamic, and static aspects in an integrated fashion. The paper is completed by examples taken from a real case study in the commercial area.

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Castellanos, Malu, Florian Daniel, Irene Garrigós, and Jose-Norberto Mazón. "Business Intelligence and the Web." Information Systems Frontiers 15, no. 3 (May 31, 2013): 307–9. http://dx.doi.org/10.1007/s10796-013-9435-8.

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Vizgaitytė, Gintarė, and Skyrius Rimvydas. "BUSINESS INTELLIGENCE IN THE PROCESS OF DECISION MAKING: CHANGES AND TRENDS." Ekonomika 91, no. 3 (January 1, 2012): 147–57. http://dx.doi.org/10.15388/ekon.2012.0.881.

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The concept of business intelligence has drawn substantial attention of both academicians and practitioners. As one of the fastest eveloping business application areas, business intelligence has also created a trail of confusion regarding its role, potential, sources of value creation. This study aimed at elucidating the role of business intelligence in the near future through the analysis of the current research and practical trends. The current prevailing trends in business intelligence technologies and systems are mobile business intelligence, location intelligence, software as a service, big data and predictive analytics. The paper points out that human factors in business intelligence are often underestimated, while a thorough consideration of their role should create additional ground for effective business intelligence applications. Some of the important considerations regarding human factors, such as support of problem-solving patterns and an efficient coordination of business intelligence activities, are presented.

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Gwoździewicz, Sylwia, Dariusz Prokopowicz, Jan Grzegorek, and Martin Dahl. "APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTELLIGENCE SOFTWARE IN INTEGRATED RISK MANAGEMENT IN ORGANIZATION." International Journal of New Economics and Social Sciences 8, no. 2 (December 30, 2018): 12–14. http://dx.doi.org/10.5604/01.3001.0012.9925.

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Currently, business analytics uses computerized platforms containing ready-made reporting formulas in the field of Business Intelligence. In recent years, software companies supporting enterprise management offer advanced applications of information-analytical Business Intelligence class systems consisting of modular development of these systems and combining business intelligence software with platforms that use data warehouse technology, multi-dimensional analytical processing software and data mining and processing applications. This article describes an example of this type of computerized analytical platform for business entities, which is included in analytical applications that allow quick access to necessary, aggregated and multi-criteria processed information. The software allows entrepreneurs and corporate managers as well as entities from the SME sector on the one hand to use embedded patterns of reports or analyzes, and on the other hand to self-develop and configure analyzes carried out, tailored to the specifics of a specific entity. Such analytical applications make it possible to build integrated risk management systems in the organization.

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Grabova, Oksana, Jerome Darmont, Jean-Hugues Chauchat, and Iryna Zolotaryova. "Business intelligence for small and middle-sized entreprises." ACM SIGMOD Record 39, no. 2 (December 6, 2010): 39–50. http://dx.doi.org/10.1145/1893173.1893180.

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Goethals, Frank G. "Data warehousing and business intelligence for e-commerce." ACM SIGMOD Record 31, no. 2 (June 2002): 71–72. http://dx.doi.org/10.1145/565117.565132.

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Hart, Mike, Farhan Esat, Michael Rocha, and Zaid Khatieb. "Introducing Students to Business Intelligence: Acceptance and Perceptions of OLAP Software." Issues in Informing Science and Information Technology 4 (2007): 105–23. http://dx.doi.org/10.28945/936.

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Radhakrishna, Vangipuram, Gunupudi Rajesh Kumar, and Shadi Aljawarneh. "Optimising business intelligence results through strategic application of software process model." International Journal of Intelligent Enterprise 4, no. 1/2 (2017): 128. http://dx.doi.org/10.1504/ijie.2017.087013.

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Aljawarneh, Shadi, Vangipuram Radhakrishna, and Gunupudi Rajesh Kumar. "Optimising business intelligence results through strategic application of software process model." International Journal of Intelligent Enterprise 4, no. 1/2 (2017): 128. http://dx.doi.org/10.1504/ijie.2017.10008160.

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Schwartz, A., M. Silver, M. Antoline, and K. Brodmann. "111 Utilization of Business Intelligence Software for an Emergency Department Dashboard." Annals of Emergency Medicine 70, no. 4 (October 2017): S45. http://dx.doi.org/10.1016/j.annemergmed.2017.07.137.

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Banica, Logica, Daniela Pirvu, and Cristian Stefan. "DISTRIBUTED BUSINESS INTELLIGENCE A NEW APPROACH IN ECONOMICAL EDUCATION." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 3 (November 30, 2012): 413–18. http://dx.doi.org/10.24297/ijct.v3i3a.2947.

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The focus area of this paper is twofold: covering the theoretical background about cloud-based Business Intelligence, and the practical approach using the Jaspersoft environment. Also, by choosing the main application area, we emphasized the advantage of using such instruments as an educational tool, with the goal of giving the students an opportunity to discover data collections and explore analysis methods. After an overview of the Business Intelligence and Cloud Computing main concepts, we have pointed out the benefits of using BI in the Software as a Service (SaaS) philosophy, and we used the facilities of Jaspersoft BI software to model frequently used analysis requirements on several virtual firms

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Arjunan, Tamilselvan. "Building Business Intelligence Data Extractor using NLP and Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (October 31, 2022): 23–28. http://dx.doi.org/10.22214/ijraset.2022.46945.

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Abstract: The goal of the Business Intelligence data extractor (BID- Extractor) tool is to offer high-quality, usable data that is freely available to the public. To assist companies across all industries in achieving their objectives, we prefer to use cuttingedge, business-focused web scraping solutions. The World wide web contains all kinds of information of different origins; some of those are social, financial, security, and academic. Most people access information through the internet for educational purposes. Information on the web is available in different formats and through different access interfaces. Therefore, indexing or semantic processing of the data through websites could be cumbersome. Web Scraping/Data extracting is the technique that aims to address this issue. Web scraping is used to transform unstructured data on the web into structured data that can be stored and analyzed in a central local database or spreadsheet. There are various web scraping techniques including Traditional copy-and-paste, Text capturing and regular expression matching, HTTP programming, HTML parsing, DOM parsing, Vertical aggregation platforms, Semantic annotation recognition, and Computer vision webpage analyzers. Traditional copy and paste is the basic and tiresome web scraping technique where people need to scrap lots of datasets. Web scraping software is the easiest scraping technique since all the other techniques except traditional copy and pastes require some form of technical expertise. Even though there are many webs scraping software available today, most of them are designed to serve one specific purpose. Businesses cannot decide using the data. This research focused on building web scraping software using Python and NLP. Convert the unstructured data to structured data using NLP. We can also train the NLP NER model. The study's findings provide a way to effectively gauge business impact

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Hayman, I. "Review: Business Intelligence: The IBM Solution." Computer Bulletin 42, no. 1 (January 1, 2000): 30–31. http://dx.doi.org/10.1093/combul/42.1.30-c.

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Borissova, Daniela, Ivan Mustakerov, and Dilian Korsemov. "Business Intelligence System via Group Decision Making." Cybernetics and Information Technologies 16, no. 3 (September 1, 2016): 219–29. http://dx.doi.org/10.1515/cait-2016-0045.

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Abstract In the paper a business intelligence tool based on group decision making is proposed. The group decision making uses a combinatorial optimization modeling technique. It takes into account weighted coefficients for evaluation criteria assigned by decision makers together with their scores for the alternatives in respect of these criteria. The proposed optimization model for group decision making considers also the knowledge level of the group members involved as decision makers. This optimization model is implemented in three-layer architecture of Web application for business intelligence by group decision making. Developed Web application is numerically tested for a representative problem for software choice considering six decision makers, three alternatives and 19 evaluation criteria. The obtained results show the practical applicability and effectiveness of the proposed approach.

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Ul Haq, Muh Zia, and Suharjito Suharjito. "Usability Analysis of Business Intelligence Tool Based Table Virtualization." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (February 1, 2018): 431. http://dx.doi.org/10.11591/ijeecs.v9.i2.pp431-437.

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<p>The use of Business Intelligence for Small-Mid Enterprise (SME) no longer impeded by the size of the resources required. The study in this paper provides an overview of how the use of proposed software can be used as an effective and efficient alternative solution in building BI Tools combined with table virtualization utilization in SQL. To know the level of usability and suitability of the developed BI Tool, the researcher conducted evaluation and analyzation using 2 (two) tools (Heuristics Evaluation and UMUX-Lite). The result found the comparison between the output (Business Intelligence Tool) produced by PHP Report Maker has a better level of usability and suitability than its competitors. Although both are in a decent median, the output needs preferences and customizations to achieve maximum functionality like well-known other development software. </p>

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Khozium, Mohamed O., and Norah S. Farooqi. "Cooperative Business Intelligence Model Using a Multiagent Platform." Scientific Programming 2020 (November 11, 2020): 1–12. http://dx.doi.org/10.1155/2020/8898719.

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Modern companies wish to utilize business intelligence (BI) to track and analyze their courses of action. Many BI applications serve this purpose at many levels, starting from documenting and charting and ending with analytics and decision support systems, which are considered a sufficient complement to consultancy and management resources. However, the contemporaneous BI software is missing two functionalities. First, although nearly all applications of the same genre use almost identical concepts, there is no unified application programming interface (API) to enable interaction. The second problem is a consequence of the first issue. Without a unified API, BI applications cannot be integrated, eliminating any possibility of establishing universal platforms for BI distributed services. Lacking these two functionalities makes developers reinvent the wheel with each new implementation. To solve these problems, we propose a platform running a multiagent business intelligence system. This system empowers the available BI resources to serve a larger segment of the BI end-user applications cooperatively. To build this system, we propose a unified model that enables distributive agent-based tasking and cooperative interaction. This allows researchers to cooperate in spreading the multiagent platform’s functionality and helps them proceed toward more detailed analysis considering agents’ construction. Moreover, it will enable BI service providers to cooperatively implement new applications and develop better solutions while maintaining a functional end-user program.

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Power, Daniel J. "Mobile decision support and business intelligence: an overview☆." Journal of Decision Systems 22, no. 1 (January 2013): 4–9. http://dx.doi.org/10.1080/12460125.2012.760267.

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Gallinucci, Enrico, Matteo Golfarelli, and Stefano Rizzi. "Advanced topic modeling for social business intelligence." Information Systems 53 (October 2015): 87–106. http://dx.doi.org/10.1016/j.is.2015.04.005.

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Drushku, Krista, Julien Aligon, Nicolas Labroche, Patrick Marcel, and Verónika Peralta. "Interest-based recommendations for business intelligence users." Information Systems 86 (December 2019): 79–93. http://dx.doi.org/10.1016/j.is.2018.08.004.

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Gao, Zhuolin, and Qizheng Qian. "The Risk and Benefits of Applying Artificial Intelligence in Business Discussions." BCP Business & Management 30 (October 24, 2022): 808–12. http://dx.doi.org/10.54691/bcpbm.v30i.2569.

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Artificial intelligence (AI) is the development and implementation of algorithms to create a dynamic computing environment replicating human intelligence's fundamental processes. To make a computer think and act like a human requires three essential components: a computing system, data and data management, and powerful artificial intelligence algorithms [1]. With the development of AI technology, more and more companies are turning to AI, for example, to assist them in negotiations. Artificial intelligence is a powerful tool for the mathematical analysis of negotiation situations [2]. Negotiation is a process of social interaction and communication about the distribution and redistribution of power. Traditionally, negotiation assistance has been based on normative and prescriptive research, with analysts and experts as users. Electronic negotiation systems provide services to negotiators, addressing their needs rather than guiding their operations to comply with logic and optimization, as is common in software engineering [3]. Therefore, it was found that artificial intelligence is, to some extent, helpful in negotiation. This study will use some examples, like Pactum, to examine the advantages and disadvantages of AI in business negotiations, and in the end, it will suggest some solutions for the negative impacts.

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Ponomarenko and Teleus. "Business analysis as an effective data processing tool." Problems of Innovation and Investment Development, no. 23 (April 10, 2020): 64–70. http://dx.doi.org/10.33813/2224-1213.23.2020.7.

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The subject of the research is the approach to the possibility of using business intelligence for integrated data processing and analysis in order to optimize the company’s activities. The purpose of writing this article is to study the concept of the BI-systems peculiarities use as one of the advanced approaches to the pro- cessing and analysis of large data sets that are continuously accumulated from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the pos- sibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty consists the features of using the business analytics model in modern conditions to optimize the activities of companies through the use of complex information, which in many cases is unstructured, are identified. The main directions of working with big data are disclosed, starting from the stage of collection and storage in specialized repositories, and ending with a comprehensive analysis of information. The main advantages of using dashboards in the process of demonstrating research results are given. A comprehensive analysis of software products in the business intelligence market has been carried out. Conclusions. The use of business intelligence allows companies to optimize their activities by making effective management decisions. The availability of a large number of BI tools al- lows company to adapt the analysis system in accordance with available data and existing needs of the company. Software solutions make it possible to build dash- boards with the settings of the selected system of indicators.

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Valentin, Grecu, and Nate Silviu. "Managing Sustainability with Eco-Business Intelligence Instruments." Management of Sustainable Development 6, no. 1 (August 22, 2014): 25–30. http://dx.doi.org/10.2478/msd-2014-0003.

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Abstract In response to increasing concerns of society about environmental degradation and increasing demands for a transition to a more sustainable society, the business companies are increasingly active in aligning their processes and services with a sustainability agenda. Production, distribution and supply of goods and services require material and energy consumption, having an impact on natural resources both quantitatively and qualitatively, generating waste, pollution and disrupting ecosystems. Ecobusiness intelligence is the capacity of people, processes and applications/tools to organize business information, to facilitate consistent access to them and analyse them in order to improve management decisions, for better performance management of the organizations that are increasingly pressed to synchronize their processes and services with a sustainable development agenda, through the development, testing and implementation of decision support software. This paper advances the idea that BI methods and tools have an important but as yet not well studied role to play in helping organizations implement and monitor sustainable and socially responsible business practices.

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Wark, Jason David. "Power Up: Combining Behavior Monitoring Software with Business Intelligence Tools to Enhance Proactive Animal Welfare Reporting." Animals 12, no. 13 (June 22, 2022): 1606. http://dx.doi.org/10.3390/ani12131606.

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Animal welfare is a dynamic process, and its evaluation must be similarly dynamic. The development of ongoing behavior monitoring programs in zoos and aquariums is a valuable tool for identifying meaningful changes in behavior and allows proactive animal management. However, analyzing observational behavior data in an ongoing manner introduces unique challenges compared with traditional hypothesis-driven studies of behavior over fixed time periods. Here, I introduce business intelligence software as a potential solution. Business intelligence software combines the ability to integrate multiple data streams with advanced analytics and robust data visualizations. As an example, I provide an overview of the Microsoft Power BI platform, a leading option in business intelligence software that is freely available. With Power BI, users can apply data cleaning and shaping in a stepwise fashion, then build dashboards using a library of visualizations through a drag-and-drop interface. I share two examples of data dashboards built with Power BI using data from the ZooMonitor behavior recording app: a quarterly behavior summary and an enrichment evaluation summary. I hope this introduction to business intelligence software and Microsoft Power BI empowers researchers and managers working in zoos and aquariums with new tools to enhance their evidence-based decision-making processes.

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Journal articles on the topic 'Business intelligence Software'

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