Relevant bibliographies by topics / Système Business Intelligence

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Système Business Intelligence'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Système Business Intelligence"

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Mau, Jens. "Brachliegendes Potenzial." kma - Klinik Management aktuell 13, no. 11 (2008): 94. http://dx.doi.org/10.1055/s-0036-1574954.

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Dank Business-Intelligence-Systemen bekommen Entscheider im Krankenhaus gezielte Analysen und Statistiken. Doch die Systeme sind teuer und Extras kosten zusätzlich. Eine Lösung auf Microsoft-Basis ziehen viele nicht in Erwägung – aber es gibt sie. Im Krankenhaus existiert ein schwer zu beherrschender Datenzoo.
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Mollekopf, Heiner. "Nutzen von Business Intelligence-Systemen." ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 104, no. 5 (2009): 411–14. http://dx.doi.org/10.3139/104.110077.

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Chamoni, Peter, and Peter Gluchowski. "Integrationstrends bei Business-Intelligence-Systemen." Wirtschaftsinformatik 46, no. 2 (2004): 119–28. http://dx.doi.org/10.1007/bf03250931.

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Tunowski, Remigiusz. "Organization Effectiveness and Business Intelligence Systems. Literature Review." Management and Business Administration. Central Europe 23, no. 4 (2015): 55–73. http://dx.doi.org/10.7206/mba.ce.2084-3356.157.

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Schwalm, Stephan, and Carsten Bange. "Einsatzpotenziale von XML in Business-Intelligence-Systemen." Wirtschaftsinformatik 46, no. 1 (2004): 5–14. http://dx.doi.org/10.1007/bf03250991.

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Humm, Bernhard, and Frank Wietek. "Architektur von Data Warehouses und Business Intelligence Systemen." Informatik-Spektrum 28, no. 1 (2005): 3–14. http://dx.doi.org/10.1007/s00287-004-0450-5.

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Anurag Reddy Basani, Maria, and Anudeep Kandi. "Data-Driven Decision Making: Advanced Database Systems for Business Intelligence." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 844–50. http://dx.doi.org/10.21275/sr241114034006.

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Grünwald, Markus, and Dirk Taubner. "Business Intelligence." Informatik-Spektrum 32, no. 5 (2009): 398–403. http://dx.doi.org/10.1007/s00287-009-0374-1.

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Herring, Jan P. "Business Intelligence." Journal of Business Strategy 14, no. 3 (1993): 10–12. http://dx.doi.org/10.1108/eb039552.

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Schön, Dietmar, and Bernd Springenberg. "Planung und Steuerung im Controlling-Cockpit der TPPG, ein mittelständisches Handelsunternehmen." Controlling 31, no. 4 (2019): 29–37. http://dx.doi.org/10.15358/0935-0381-2019-4-29.

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Mittelständische Unternehmen benötigen zur erfolgreichen Unternehmenssteuerung leistungsfähige IT-gestützte Controlling-Systeme. Excel und andere suboptimale Systeme müssen konsequent durch Business-Intelligence-Technologie abgelöst werden. Hierzu zählt ein solides Datawarehouse und ein leistungsfähiges Frontend. Dieser Beitrag zeigt am Beispiel der Ter Plastic Polymer Group auf, welche Faktoren zu einer erfolgreichen Umsetzung eines BI-gestützten Controlling-Cockpits notwendig sind.
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Dissertations / Theses on the topic "Système Business Intelligence"

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Kuchmann-Beauger, Nicolas. "Système de questions/réponses dans un contexte de business intelligence." Phd thesis, Ecole Centrale Paris, 2013. http://tel.archives-ouvertes.fr/tel-00944140.

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Le volume et la complexité des données générées par les systèmes d'information croissent de façon singulière dans les entrepôts de données. Le domaine de l'informatique décisionnelle (aussi appelé BI) a pour objectif d'apporter des méthodes et des outils pour assister les utilisateurs dans leur tâche de recherche d'information. En effet, les sources de données ne sont en général pas centralisées, et il est souvent nécessaire d'interagir avec diverses applications. Accéder à l'information est alors une tâche ardue, alors que les employés d'une entreprise cherchent généralement à réduire leur charge de travail. Pour faire face à ce constat, le domaine " Enterprise Search " s'est développé récemment, et prend en compte les différentes sources de données appartenant aussi bien au réseau privé d'entreprise qu'au domaine public (telles que les pages Internet). Pourtant, les utilisateurs de moteurs de recherche actuels souffrent toujours de du volume trop important d'information à disposition. Nous pensons que de tels systèmes pourraient tirer parti des méthodes du traitement naturel des langues associées à celles des systèmes de questions/réponses. En effet, les interfaces en langue naturelle permettent aux utilisateurs de rechercher de l'information en utilisant leurs propres termes, et d'obtenir des réponses concises et non une liste de documents dans laquelle l'éventuelle bonne réponse doit être identifiée. De cette façon, les utilisateurs n'ont pas besoin d'employer une terminologie figée, ni de formuler des requêtes selon une syntaxe très précise, et peuvent de plus accéder plus rapidement à l'information désirée. Un challenge lors de la construction d'un tel système consiste à interagir avec les différentes applications, et donc avec les langages utilisés par ces applications d'une part, et d'être en mesure de s'adapter facilement à de nouveaux domaines d'application d'autre part. Notre rapport détaille un système de questions/réponses configurable pour des cas d'utilisation d'entreprise, et le décrit dans son intégralité. Dans les systèmes traditionnels de l'informatique décisionnelle, les préférences utilisateurs ne sont généralement pas prises en compte, ni d'ailleurs leurs situations ou leur contexte. Les systèmes état-de-l'art du domaine tels que Soda ou Safe ne génèrent pas de résultats calculés à partir de l'analyse de la situation des utilisateurs. Ce rapport introduit une approche plus personnalisée, qui convient mieux aux utilisateurs finaux. Notre expérimentation principale se traduit par une interface de type search qui affiche les résultats dans un dashboard sous la forme de graphes, de tables de faits ou encore de miniatures de pages Internet. En fonction des requêtes initiales des utilisateurs, des recommandations de requêtes sont aussi affichées en sus, et ce dans le but de réduire le temps de réponse global du système. En ce sens, ces recommandations sont comparables à des prédictions. Notre travail se traduit par les contributions suivantes : tout d'abord, une architecture implémentée via des algorithmes parallélisés et qui prend en compte la diversité des sources de données, à savoir des données structurées ou non structurées dans le cadre d'un framework de questions/réponses qui peut être facilement configuré dans des environnements différents. De plus, une approche de traduction basée sur la résolution de contrainte, qui remplace le traditionnel langage-pivot par un modèle conceptuel et qui conduit à des requêtes multidimensionnelles mieux personnalisées. En outre, en ensemble de patrons linguistiques utilisés pour traduire des questions BI en des requêtes pour bases de données, qui peuvent être facilement adaptés dans le cas de configurations différentes.
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Harriet, Loïc. "L'intelligence économique à la lumière des concepts managériaux : l'étude de cas d'une entreprise du secteur énergétique." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0193/document.

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L’intelligence économique se présente comme une exception conceptuelle francophone issue à la fois de traductions de différents termes anglo-saxons mais également d’une agrégation de fonctions organisationnelles liées à l’information. Ces fondements hétéroclites se conjuguent à une effervescence pratique, l’intelligence économique ne cessant de se développer sous diverses formes dans les organisations. Cette thèse vise à proposer un fondement théorique nouveau à ces expériences en s’appuyant sur les concepts managériaux au travers de l’étude du cas de Gaz de Bordeaux, une entreprise du secteur énergétique. S’inscrivant dans une volonté exploratoire fondée sur une méthodologie qualitative, l’objectif est de proposer une définition s’appuyant sur les théories gestionnaires de l’asymétrie d’information, du système et de la valeur<br>"L'intelligence économique" is presented as a French conceptual exception after at a time of translations various English terms but also an aggregation of organizational functions related to information. These heteroclite bases are combined to an effervescent practice, “l’intelligence économique” never ceasing to develop in various forms in organizations. This thesis aims to propose a new theoretical basis for these experiments based on the managerial concepts through a case study of Gaz de Bordeaux, an energy firm. As part of an exploratory will based on a qualitative, the objective is to propose a definition based on the Management Science theories of asymmetric information, system and value
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Crossland, Maria. "How business intelligence is adding business value." Master's thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/10287.

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Includes bibliographical references (leaves 67-72).<br>Business Intelligence (BI) continues to top the list of CIO priorities, investment in BI technologies continues to grow and organizations are becoming increasingly reliant on BI to help reduce costs and grow revenues. However, structured measurement and monitoring of the business value that can be attributed to BI investment remain elusive. This study used a multiple case study approach to examine how BI is adding value to organizations, what processes and methods are being followed for the evaluation of the business value that BI delivers as well as what approaches are being used to maximize the potential value that the organization's investment on BI could deliver.
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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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Kuchmann-Beauger, Nicolas. "Question Answering System in a Business Intelligence Context." Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2013. http://www.theses.fr/2013ECAP0021/document.

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Le volume et la complexité des données générées par les systèmes d’information croissent de façon singulière dans les entrepôts de données. Le domaine de l’informatique décisionnelle (aussi appelé BI) a pour objectif d’apporter des méthodes et des outils pour assister les utilisateurs dans leur tâche de recherche d’information. En effet, les sources de données ne sont en général pas centralisées, et il est souvent nécessaire d’interagir avec diverses applications. Accéder à l’information est alors une tâche ardue, alors que les employés d’une entreprise cherchent généralement à réduire leur charge de travail. Pour faire face à ce constat, le domaine « Enterprise Search » s’est développé récemment, et prend en compte les différentes sources de données appartenant aussi bien au réseau privé d’entreprise qu’au domaine public (telles que les pages Internet). Pourtant, les utilisateurs de moteurs de recherche actuels souffrent toujours de du volume trop important d’information à disposition. Nous pensons que de tels systèmes pourraient tirer parti des méthodes du traitement naturel des langues associées à celles des systèmes de questions/réponses. En effet, les interfaces en langue naturelle permettent aux utilisateurs de rechercher de l’information en utilisant leurs propres termes, et d’obtenir des réponses concises et non une liste de documents dans laquelle l’éventuelle bonne réponse doit être identifiée. De cette façon, les utilisateurs n’ont pas besoin d’employer une terminologie figée, ni de formuler des requêtes selon une syntaxe très précise, et peuvent de plus accéder plus rapidement à l’information désirée. Un challenge lors de la construction d’un tel système consiste à interagir avec les différentes applications, et donc avec les langages utilisés par ces applications d’une part, et d’être en mesure de s’adapter facilement à de nouveaux domaines d’application d’autre part. Notre rapport détaille un système de questions/réponses configurable pour des cas d’utilisation d’entreprise, et le décrit dans son intégralité. Dans les systèmes traditionnels de l’informatique décisionnelle, les préférences utilisateurs ne sont généralement pas prises en compte, ni d’ailleurs leurs situations ou leur contexte. Les systèmes état-de-l’art du domaine tels que Soda ou Safe ne génèrent pas de résultats calculés à partir de l’analyse de la situation des utilisateurs. Ce rapport introduit une approche plus personnalisée, qui convient mieux aux utilisateurs finaux. Notre expérimentation principale se traduit par une interface de type search qui affiche les résultats dans un dashboard sous la forme de graphes, de tables de faits ou encore de miniatures de pages Internet. En fonction des requêtes initiales des utilisateurs, des recommandations de requêtes sont aussi affichées en sus, et ce dans le but de réduire le temps de réponse global du système. En ce sens, ces recommandations sont comparables à des prédictions. Notre travail se traduit par les contributions suivantes : tout d’abord, une architecture implémentée via des algorithmes parallélisés et qui prend en compte la diversité des sources de données, à savoir des données structurées ou non structurées dans le cadre d’un framework de questions/réponses qui peut être facilement configuré dans des environnements différents. De plus, une approche de traduction basée sur la résolution de contrainte, qui remplace le traditionnel langage-pivot par un modèle conceptuel et qui conduit à des requêtes multidimensionnelles mieux personnalisées. En outre, en ensemble de patrons linguistiques utilisés pour traduire des questions BI en des requêtes pour bases de données, qui peuvent être facilement adaptés dans le cas de configurations différentes<br>The amount and complexity of data generated by information systems keep increasing in Warehouses. The domain of Business Intelligence (BI) aims at providing methods and tools to better help users in retrieving those data. Data sources are distributed over distinct locations and are usually accessible through various applications. Looking for new information could be a tedious task, because business users try to reduce their work overload. To tackle this problem, Enterprise Search is a field that has emerged in the last few years, and that takes into consideration the different corporate data sources as well as sources available to the public (e.g. World Wide Web pages). However, corporate retrieval systems nowadays still suffer from information overload. We believe that such systems would benefit from Natural Language (NL) approaches combined with Q&amp;A techniques. Indeed, NL interfaces allow users to search new information in their own terms, and thus obtain precise answers instead of turning to a plethora of documents. In this way, users do not have to employ exact keywords or appropriate syntax, and can have faster access to new information. Major challenges for designing such a system are to interface different applications and their underlying query languages on the one hand, and to support users’ vocabulary and to be easily configured for new application domains on the other hand. This thesis outlines an end-to-end Q&amp;A framework for corporate use-cases that can be configured in different settings. In traditional BI systems, user-preferences are usually not taken into account, nor are their specific contextual situations. State-of-the art systems in this field, Soda and Safe do not compute search results on the basis of users’ situation. This thesis introduces a more personalized approach, which better speaks to end-users’ situations. Our main experimentation, in this case, works as a search interface, which displays search results on a dashboard that usually takes the form of charts, fact tables, and thumbnails of unstructured documents. Depending on users’ initial queries, recommendations for alternatives are also displayed, so as to reduce response time of the overall system. This process is often seen as a kind of prediction model. Our work contributes to the following: first, an architecture, implemented with parallel algorithms, that leverages different data sources, namely structured and unstructured document repositories through an extensible Q&amp;A framework, and this framework can be easily configured for distinct corporate settings; secondly, a constraint-matching-based translation approach, which replaces a pivot language with a conceptual model and leads to more personalized multidimensional queries; thirdly, a set of NL patterns for translating BI questions in structured queries that can be easily configured in specific settings. In addition, we have implemented an iPhone/iPad™ application and an HTML front-end that demonstrate the feasibility of the various approaches developed through a series of evaluation metrics for the core component and scenario of the Q&amp;A framework. To this end, we elaborate on a range of gold-standard queries that can be used as a basis for evaluating retrieval systems in this area, and show that our system behave similarly as the well-known WolframAlpha™ system, depending on the evaluation settings
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Kashora, Kudzai. "Leveraging mobile business intelligence to create strategic business value." Master's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/13218.

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Includes bibliographical references.<br>Currently, there is a growing need for mobile Business Intelligence (BI) in the business world as the nature of work is changing and employees are more mobile than ever before. Mobile BI promises portability and pervasive access to BI, making it a topic high on many directors’ agendas; however the tangible and intangible benefits of mobile BI are still not well understood. Coupled with this, BI practitioners’ are sceptical about the real business value of delivering BI reports to mobile devices and how this undertaking can bring about organizational changes in the long run. As the field of mobile BI is still in its infancy, there is a lack of research which addresses the business value of mobile BI. The existing studies in this research area have been focused on adoption and implementation strategies. This study therefore attempts to address the gap by investigating how mobile BI can be utilised to enhance organizational performance and also contribute towards strategic business value. In light of this, an extensive literature review was conducted which revealed that mobile BI usage can result in benefits, such as improved employee performance management, organizational agility and customer satisfaction. A conceptual model was developed based on the literature and this model acted as the framework for investigating the research problem.
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Škapa, Martin. "Návrh a implementace Business Intelligence systému." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2009. http://www.nusl.cz/ntk/nusl-222228.

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Aim of master’s thesis is design of Business Intelligence solution based on non commercial technology, consideration of installation costs, estimation of economic benefits and designing of final solution of currently unsatisfactory situation in company Fortemix s.r.o.
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Robles, Sebastian. "Business intelligence in Chile, recommendations to develop local applications." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/70831.

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Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, June 2011.<br>"February 2010." Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 60).<br>The volume of information generated from enterprise applications is growing exponentially, and the cost of storage is decreasing rapidly. In addition, cloud-based applications, mobile devices and social networks are becoming relevant sources of unstructured data that provide essential information for strategic decisions making. Therefore, with time, enterprise databases will become more valuable for business but also much harder to integrate, process and analyze. Business Intelligence software was instrumental in helping organizations to analyze information and provide reports to support business decision-making. Accordingly, BI applications evolved as enterprise information grew, hardware-processing capacities developed, and storage cost is being reduced significantly. In this paper, we will analyze the current BI world market and compare it with the Chilean market, in order to come up with business plan recommendations for local developers and systems integrators interested in capitalizing the opportunities generated by the global BI software market consolidation.<br>by Sebastian Robles.<br>S.M.in Engineering and Management
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Chamoun, Christoffer. "Self-Service Business Intelligence : Kritiska framgångsfaktorer för att tillämpa Self-Service Business Intelligence." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15645.

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Self-service Business Intelligence är idag ett relativt nytt koncept och det blir vanligare idag att verksamheter börjar röra sig mot denna nya trend inom Business Intelligence för att att göra sina användare mer flexibla i sitt beslutsfattande. Anpassningen idag till Self- service BI är idag är låg och har sjunkit de senare trots stora investeringar. Konceptet med Self-service BI är kan vara svårt för användarna att förstå och det finns ett antal faktorer som kan bidra till att öka anpassningen och uppnå framgångar med att tillämpa SSBI. Denna studien syftar till att ta reda på: ”Vad finns det för kritiska framgångsfaktorer för att tillämpa Self-Service BI? ”. För att besvara frågan användes en kvalitativ metod och insamlingen för data utfördes med hjälp av semistrukturerade intervjuer. Intervjuerna utfördes på 6 företag med 6 olika respondenter som har erfarenhet inom SSBI och BI. Respondenterna bidrog med empirisk underlag för att kunna besvara studiens frågeställning, men även till litteraturen med information som litteraturen tidigare inte nämner. Resultaten har visat att svaren från respondenterna och litteraturen går i linje med varandra när det gäller kritiska framgångsfaktorer. Framgångsfaktorerna som diskuteras och tas upp av respondenterna och litteraturen var: Rätt verktyg för rätt användare &amp; anpassningsbara användargränssnitt, utbildning, data governance &amp; data management, kartlägga användare och tillgänglighet av data för att framgångsrikt tillämpa SSBI. Nya faktorer som framkom under de semistrukturerade intervjuerna med respondenterna var: Change management, kommunikation och experimentering &amp; testning.
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Khalaf, Patrik. "Mobile business intelligence : För en lyckad mobile business intelligence lösning." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15752.

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Allt fler organisationer börjar se nyttan med Business Intelligence och varför det bör implementeras i sin verksamhet. Genom att använda sig av beslutsstödsystem kan organisationer samla ihop och bearbeta affärs-data och på så sätt få ut mer av sin verksamhet. Idag räcker det inte längre att ha tillgång till endast företagsinformationen inom verksamheten utan det behövs också kunna utnyttja realtids datan genom de mobila enheterna, detta genom Mobile Business Intelligence. Det handlar om en mobil variant av den traditionella business Intelligence (BI).  Med tanke på den mobila användningen ökade tillgängligheten samt prestandan i mobila enheter, så finns det stora möjligheter för Mobile BI ute i verksamheterna. Mobile BI som vilket nytt system som helst kommer även en viss problematik, kritiska framgångsfaktorer har analyserats och utvärderats. Inom dimensionen organisationsstöd identifierades två framgångsfaktorer som kunde styrkas vid namn managementsupport och skickligheter &amp; kunskaper. Utöver dessa kunde även ytterligare två framgångsfaktorer styrkas som kritiska, nämligen datasäkerhet och användbarheten genom den begränsade skärm-ytan. Med denna studie ska det underlätta och göra det enklare för verksamheter att implementera Mobile BI i sin verksamhet. Studien tar upp viktiga framgångsfaktorer vid en implementation av Mobile BI och vad organisationer bör ha i åtanke.<br>More and more organizations are beginning to see the benefits of Business Intelligence and why it should be implemented into their business. By using decision support systems, organizations can merge and process business data and thus get more out of their business. Today, access to business information within the business is no longer sufficient, but it also needs to be able to use real-time data through the mobile devices. This through Mobile Business Intelligence, which is about a mobile variant of the traditional business intelligence (BI). Given the current mobile usage and how it has increased accessibility and mobile device performance, there are great opportunities for Mobile BI in the business. Mobile BI as any new system will also come with a certain problematic and critical success factors have been analyzed and evaluated. Within the organizational support dimension, two success factors were identified that was confirmed critical by name management support and knowledge and skills. In addition to these, another two success factors could be proved critical, named data security and usability through the limited screen area. With this study, it will facilitate and make it easier for businesses to implement Mobile BI in their operations. The study addresses key success factors in implementing Mobile BI and what organizations should keep in mind.
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Books on the topic "Système Business Intelligence"

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Burmester, Lars. Adaptive Business-Intelligence-Systeme. Vieweg+Teubner, 2011. http://dx.doi.org/10.1007/978-3-8348-8118-2.

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T, Leondes Cornelius, ed. Intelligent knowledge-based systems: Business and technology in the new millennium. Kluwer Academic, 2005.

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1949-, King David, ed. Expert systems: Artificial intelligence in business. J. Wiley, 1985.

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Turban, Efraim. Decision support and business intelligence systems. 9th ed. Pearson/Prentice Hall, 2011.

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Howson, Cindi. Successful Business Intelligence. McGraw-Hill, 2008.

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Sauter, Vicki L. Decision Support Systems for Business Intelligence. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470634431.

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Kautish, Sandeep, Prasenjit Chatterjee, Dragan Pamucar, N. Pradeep, and Deepmala Singh, eds. Computational Intelligence for Modern Business Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-5354-7.

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Ivanovic, Mirjana, Marite Kirikova, and Laila Niedrite, eds. Digital Business and Intelligent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09850-5.

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Lupeikienė, Audronė, Jolita Ralyté, and Gintautas Dzemyda, eds. Digital Business and Intelligent Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63543-4.

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Odincov, Boris. Models and intelligent systems. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1060845.

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The monograph consists of three chapters, the first of which outlines the theoretical foundations of intelligent information systems. Special attention is paid to the disclosure of the term "model" as the intended meaning depends on the understanding of the material. Introduces and examines the new concepts such as the associative and intuitive knowledge while in the creation of intellectual information systems are not used. &#x0D; The second Chapter contains the analysis of problems of development of artificial intelligence (AI), developed in two directions: classical and statistical. Discusses difficulties in the development of the classical approach, associated with identifying the meaning of words, phrases, text, and formulating thoughts. The analysis of problems arising in the play of imagination and insight, machine understanding of natural language texts, play, verbalization and reflection. &#x0D; The third Chapter contains examples of the development of intelligent information systems and technologies in practice of management of economic objects. Theoretical bases of construction of information robots designed to support the task hierarchy of the knowledge base and generating control regulations. The technology of their creation and application in the management of the business efficiency of enterprise business processes and its investment activities. &#x0D; Focused on researchers and developers, AI and intelligent information systems, as well as graduate students and faculty in related academic disciplines.
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Book chapters on the topic "Système Business Intelligence"

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Burmester, Lars. "Business Intelligence." In Adaptive Business-Intelligence-Systeme. Vieweg+Teubner, 2011. http://dx.doi.org/10.1007/978-3-8348-8118-2_5.

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Rizzi, Stefano. "Business Intelligence." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_881-2.

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Rizzi, Stefano. "Business Intelligence." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_881.

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Rizzi, Stefano. "Business Intelligence." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_881.

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Caserio, Carlo, and Sara Trucco. "Business Intelligence Systems." In Contributions to Management Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77679-8_3.

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Kale, Vivek. "Business Intelligence Systems." In Enterprise Performance Intelligence and Decision Patterns. Auerbach Publications, 2017. http://dx.doi.org/10.4324/9781351228428-10.

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Negash, Solomon, and Paul Gray. "Business Intelligence." In Handbook on Decision Support Systems 2. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-48716-6_9.

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Gronwald, Klaus-Dieter. "BI: Business Intelligence." In Integrated Business Information Systems. Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-53291-1_13.

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Gronwald, Klaus-Dieter. "BI: Business Intelligence." In Integrated Business Information Systems. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-59811-5_5.

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Kazeli, Hariklea. "Cloud Business Intelligence." In Business Information Systems Workshops. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11460-6_26.

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Conference papers on the topic "Système Business Intelligence"

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N, Kirubakaran, Preethi S. T, Parijatham R, and Pramithi R. "Smart Business with Intelligent Business Cards (SBIBC)." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894227.

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Jaganathan, Humashankar Vellathur, Prakash Murugesan, Sairam Madasu, and Imran Ur Rehman. "Artificial Intelligence Embedded Customer Relationship Management for Business-to-Business Firms using Improved Long Short-Term Memory." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721741.

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Wu, Jui-Yu. "Computational Intelligence-Based Intelligent Business Intelligence System: Concept and Framework." In 2010 Second International Conference on Computer and Network Technology. IEEE, 2010. http://dx.doi.org/10.1109/iccnt.2010.23.

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Houhala, Keijo, and Vesa Salminen. "Innovation Automation by AI as an Engine for Value Creation." In 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004948.

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Artificial intelligence and innovation have shaped the business landscape and brought new opportunities and challenges. Artificial intelligence, AI, provides the computing power needed to analyze and predict a huge amount of data, enabling the identification and creation of new ideas. At the same time, AI can be used to improve business efficiency through automation and intelligent systems, freeing up human capital for more strategic tasks. In this article, we will focus on examining the potential of AI from the perspective of innovation, operational efficiency, and utilization of human capital.Intersections of Artificial Intelligence and InnovationThis article delves into the role of artificial intelligence in innovations and its connection to streamlining operations and optimizing the utilization of human capital. We journey towards the possibilities in the space of knowledge that opens new doors for creating innovations.Artificial Intelligence and the Innovation ProcessWe explore the role of artificial intelligence in the innovation process, examining its potential to generate and identify new ideas and enhance innovation activities. This article answers for research questions: •How to create continuous value by utilizing artificial intelligence as an engine for innovation automation•How to intersect human capital with artificial intelligence in innovation automationResearch ApproachThe collection of data and research approach has been partially constructive, conceptual and analytical, because it introduces a pathway to innovation automation by utilizing AI as an engine for new value creation. It introduces experiences and results of several development activities and thesis works on the Heinola City environment. The Role of Artificial Intelligence in Business OptimizationWe scrutinize how artificial intelligence enhances business efficiency through automation and intelligent systems. Liberating human capital for more strategic tasks is a central theme in this section.Synergy Between Human Capital and Artificial IntelligenceWe emphasize the possibilities and efficient role allocation for collaboration between human capital and artificial intelligence. Clear responsibilities among different stakeholders add value.A Perspective from the Public SectorWe consider the unique perspective of the public sector and contemplate the challenges and opportunities of using artificial intelligence in this sector. Key considerations include cybersecurity and ethical issues.Requirements for Artificial Intelligence and Future ProspectsIn the final section, we ponder the technical and organizational requirements for artificial intelligence and its future potential in innovations. Well-planned and appropriately assigned artificial intelligence can be the key to sustainable and efficient value creation.ConclusionsWe summarize the main messages of the article and present conclusions on the shared future of artificial intelligence and innovation. Artificial intelligence can act as a significant engine for value creation, provided its use is strategically integrated, and human capital remains at the center.
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Mach, Maria Antonina, and M. Salem Abdel-Badeeh. "Intelligent techniques for business intelligence in healthcare." In 2010 10th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2010. http://dx.doi.org/10.1109/isda.2010.5687209.

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Dasgupta, Supratim, and Vamsi Krishna Vankayala. "Developing Real Time Business Intelligence Systems the Agile Way." In 2007 1st Annual IEEE Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/systems.2007.374652.

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Singh, Nehul, and Satyendra Singh Chouhan. "Role of Artificial Intelligence for Development of Intelligent Business Systems." In 2021 IEEE International Symposium on Smart Electronic Systems (iSES). IEEE, 2021. http://dx.doi.org/10.1109/ises52644.2021.00092.

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Birznieks, Janis, and Lasma Licite-Kurbe. "Analysis of the Introduction of Business Intelligence and Data Warehousing into Businesses in Latvia." In 22nd International Scientific Conference. “Economic Science for Rural Development 2021”. Latvia University of Life Sciences and Technologies. Faculty of Economics and Social Development, 2021. http://dx.doi.org/10.22616/esrd.2021.55.028.

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The market of global business intelligence technologies reached EUR 18.3 billion in 2017 and is expected to reach EUR 22.8 billion in the near future, as such technologies provide companies with a number of benefits: new information for business decision-making, real-time financial reporting and manual work automation. Nevertheless, many companies around the world do not achieve the desired results of applying business intelligence and data warehousing technologies. The research aims to develop scenarios for applying business intelligence and data warehousing tools in entrepreneurship in Latvia based on an examination of the tools. The research has found that the companies examined in the case study have introduced business intelligence along with data warehousing; however, there are differences in applying the business intelligence and the level of its advancement. Overall, a business intelligence system makes core and support operations and processes faster, as well as reduces costs and requires less human resources. However, problems were identified concerning a lack of motivation in employees to learn new technologies. The scenario analysis concluded that large companies should perform as many administrative and technological processes related to the mentioned technologies as possible themselves rather than outsource them, which allows them to save funds on the development of such technologies and improve the company’s data culture.
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Krneta, Dragoljub, Dragica Radosav, and Biljana Radulovic. "Realization business intelligence in commerce using Microsoft Business Intelligence." In 2008 6th International Symposium on Intelligent Systems and Informatics (SISY 2008). IEEE, 2008. http://dx.doi.org/10.1109/sisy.2008.4664943.

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Helfenstein, Sacha, Olena Kaikova, Oleksiy Khriyenko, and Vagan Terziyan. "Emotional Business Intelligence." In 2014 7th International Conference on Human System Interactions (HSI). IEEE, 2014. http://dx.doi.org/10.1109/hsi.2014.6860441.

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Reports on the topic "Système Business Intelligence"

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Pasupuleti, Murali Krishna. Empathetic AI in Action: Transforming Customer Service with Emotional Intelligence. National Education Services, 2025. https://doi.org/10.62311/nesx/rr725.

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Abstract: This article explores the transformative impact of Emotionally Intelligent AI on customer service, focusing on how AI systems are designed to understand and respond to human emotions with empathy and precision. It delves into the core technologies, such as sentiment analysis, emotion recognition models, and reinforcement learning, that enable AI to provide emotionally aware interactions. Practical applications are discussed, including AI-powered customer support, personalized experiences, and crisis management solutions. The Article also covers the psychological foundations of AI-driven empathy, ethical and privacy considerations, and future trends in affective computing and integration with technologies like AR/VR and IoT. The potential business advantages of adopting Emotionally Intelligent AI for enhanced customer satisfaction and long-term relationship management are highlighted, emphasizing the balance between technology and the human touch. Keywords: Emotionally Intelligent AI, customer service, empathy, sentiment analysis, emotion recognition, reinforcement learning, affective computing, personalized interactions, ethical AI, data privacy, AR/VR, IoT, human-AI interaction, future trends, business impact.
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Vuyyuru, Tejaswini. Using Predictive Maintenance techniques and Business Intelligence to develop smarter factory systems for the digital age. Iowa State University, 2018. http://dx.doi.org/10.31274/cc-20240624-1566.

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Danylchuk, Hanna B., and Serhiy O. Semerikov. Advances in machine learning for the innovation economy: in the shadow of war. Криворізький державний педагогічний університет, 2023. http://dx.doi.org/10.31812/123456789/7732.

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This preface introduces the selected and revised papers presented at the 10th International Conference on Monitoring, Modeling &amp; Management of Emergent Economy (M3E2 2022), held online in Ukraine, on November 17-18, 2022. The conference aimed to bring together researchers, practitioners, and students from various fields to exchange ideas, share experiences, and discuss challenges and opportunities in applying computational intelligence and data science for the innovation economy. The innovation economy is a term that describes the emerging paradigm of economic development that is driven by knowledge, creativity, and innovation. It requires new approaches and methods for solving complex problems, discovering new opportunities, and creating value in various domains of science, business,and society. Computational intelligence and data science are two key disciplines that can provide such approaches and methods by exploiting the power of data, algorithms, models, and systems to enable intelligent decision making, learning, adaptation, optimization, and discovery. The papers in this proceedings cover a wide range of topics related to computational intelligence and data science for the innovation economy. They include theoretical foundations, novel techniques, and innovative applications. The papers were selected and revised based on the feedback from the program committe members and reviewers who ensured their high quality. We would like to thank all the authors who submitted their papers to M3E2 2022. We also appreciate the keynote speakers who shared their insights and visions on the current trends and future directions of computational intelligence and data science for the innovation economy. We acknowledge the support of our sponsors, partners, and organizers who made this conference possible despite the challenging circumstances caused by the ongoing war in Ukraine. Finally, we thank all the participants who attended the conference online and contributed to its success.
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Canto, Patricia, ed. The role of vocational training knowledge intensive business services. (Main conclusions). Universidad de Deusto, 2020. http://dx.doi.org/10.18543/vyqr9353.

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In the global economic model, the service sector continues to gain ground on the manufacturing sector and trends such as the integration of new technologies into production processes are advancing inexorably. Advanced economies are pushed to specialise, supported by their regional innovation systems, and cities are emerging as key and strategic centres of activity. In this context, Knowledge Intensive Business Services (KIBS) are presented as critical due to their capacity to promote innovation within the regional productive fabric and smart specialisation strategies, the promotion of advanced manufacturing, the generation of quality employment and the stimulation of economic growth, especially in urban environments. This is why many cities, prioritizing KIBS to stimulate their economy, need to create and retain talent for this sort of industry. Likewise, vocational and education training (VET) systems, such as the Basque VET system, have so far developed their greatest strengths in the field of manufacturing knowledge. Due to this, VET seems to be obliged to adapt to this new scenario, in which KIBS and cities stand out, in order to continue to maintain their level of excellence. KIBS have been extensively examined, but until now no one had posed the following questions: What is the role of vocational training in KIBS? To what extent are VET profiles (and will VET profiles be) relevant in KIBS? This study will show an emerging trend in the labour market. This is the growing relevance of technology profiles with VET background in KIBS, especially in technology-based KIBS. VET technology profiles can be consolidated as one of the main implementing agents of the digital transformation (cybersecurity, blockchain, cloud computing, UX design, artificial intelligence, scientific computing...). To this end, hybridisation with other fields of knowledge but also with studies of other kinds such as university studies may be essential.
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Rihm, Alfredo, Carolina Piamonte, Eduardo Antonio Restrepo Lagos, Magda Correal, and Paula Gabriela Guerra Morán. Digital Transformation of Solid Waste Management: Waste Collection Innovation, Business Intelligence, and Digital Technologies to Transition Waste Management Towards Circularity in Latin America and the Caribbean. Edited by Claudia M. Pasquetti. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013169.

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If you are interested in technology and innovation, and have been wondering what are the new trends in technological and digital innovation in the solid waste sector in Latin America and the Caribbean, this publication is for you! The transition to the circular economy, climate action, the fourth industrial revolution bring new challenges to operators in the sector. Key challenges highlighted include the need for robust data and the digitization of waste management systems to meet the objectives of the circular economy. The text details the efforts of organizations such as the IDB to develop data generation and analysis tools through digital innovations. It also explores the role of smart waste technologies (SWT), such as Artificial Intelligence (AI), Internet of Things (IoT) and data analytics, in transforming integrated solid waste management (ISWM), improving operational efficiency and supporting sustainable practices. The publication delves into various technological tools used in ISMS, including business intelligence (BI), enterprise resource planning (ERP) and fleet management software. Case studies from countries such as Argentina, Colombia and Ecuador illustrate the successful application of these tools, highlighting their benefits in improving decision making, operational efficiency and overall service quality. The text concludes with recommendations for implementing smart waste technologies in the LAC region to foster digital transformation and support a circular economy model effectively.
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Li, Richard. DeepSeek: Revolutionising the AI industry. LegalOne Global Limited, 2025. https://doi.org/10.62436/a-1740063218021.

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Recently, DeepSeek, a Chinese artificial intelligence (AI) startup, has made headlines with its groundbreaking advancements in AI technology. Founded in 2023 by entrepreneur Liang Wenfeng, DeepSeek has quickly risen to prominence, challenging established AI giants like OpenAI and Google. DeepSeek, headquartered in Hangzhou, China, is known for its cost-efficient AI models and open-source approach, making advanced AI technology accessible to a wider range of businesses and developers. Open-source AI refers to systems whose source code, datasets, and model parameters are freely available for anyone to use, study, modify, and share, promoting collaboration, transparency, and innovation within the AI community. This article delves into the impact Deepseek has on businesses and the industry, and what the future holds for this Chinese company.
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Marsden, Eric, and Véronique Steyer. Artificial intelligence and safety management: an overview of key challenges. Foundation for an Industrial Safety Culture, 2025. https://doi.org/10.57071/iae290.

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Artificial intelligence based on deep learning, along with big data analysis, has in recent years been the subject of rapid scientific and technological advances. These technologies are increasingly being integrated into various work environments with the aim of enhancing performance and productivity. This dimension of the digital transformation of businesses and regulatory authorities presents both significant opportunities and potential risks for industrial safety management practices. While there are numerous expected benefits, such as the ability to process large volumes of reliability data or unstructured natural language incident reports, the structural opacity of large neural networks, their non-deterministic nature, and their capacity to learn from new data mean that traditional safety assurance techniques used for conventional software are not applicable. Additionally, the expansion of the scope of automatable tasks and the gradual move towards work collectives that are composed of human operators who collaborate with various intelligent machines and agents introduce new variables that must be considered alongside and integrated with the organizational and human factors of safety. What are the main challenges posed by these new technologies in terms of skills management, worker well-being, privacy protection, and the pursuit of performance that aligns with societal expectations? What changes are required in how we conceptualize the safety of high-stakes activities, how we demonstrate and verify the absence of unacceptable risks, and anticipate potential deviations? This document provides a concise overview of the most recent available information, contextualized by decades of research on automation in high-hazard systems. It focuses specifically on the projected impacts for high-hazard industries and infrastructures over the next ten years.
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Nowinska, Agnieszka Urszula, and Gisele Msann. AI disruption in chartering in Danish Shipping. Aalborg University Open Publishing, 2025. https://doi.org/10.54337/aau.bk2_2025.

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Our research highlights the current state and trends of artificial intelligence (AI) adoption in Denmark’s chartering, particularly in the dry bulk and tanker segments. Companies in the dry bulk sector are leading AI adoption, with the tanker segment closely following and adoption rates in our sample appear higher than national averages reported by consultancies. Most firms are in either the experimental phase or transitioning toward more integrated AI systems, often opting for hybrid models that allow them to maintain internal control over key processes. Factors such as company size and maturity also influence the pace and approach to AI adoption.AI is seen as a tool to enhance rather than replace jobs in the early stages of shipping operations, especially in pre-fixture activities. However, there is greater potential for automation and job substitution in the post-fixture phase, particularly in tasks such as contract (CP) management. On the supply side, the market for maritime AI and software solutions is highly competitive and fragmented, with many providers offering diverse products. Recent consolidation trends reflect different strategies: some companies, like are specializing in core offerings, while others, like are diversifying into both SaaS and pure software models. These consolidations are not only intensifying competition but also fostering partnerships between rivals—a dynamic known as coopetition. Interestingly, some shipping firms are entering the software market themselves, signaling innovation in business models. Machine learning (ML) technologies are primarily used in pre-fixture tools (like email management and tracking), while generative AI is increasingly applied in post-fixture functions, particularly contract management.
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Fang, Mei Lan, Judith Sixsmith, Jacqui Morris, et al. AgeTech, Ethics and Equity: Towards a Cultural Shift in AgeTech Ethical Responsibility. University of Dundee, 2023. http://dx.doi.org/10.20933/100001292.

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Population ageing is a global phenomenon which presents major challenges for the provision of care at home and in the community (ONS, 2018). Challenges include the human and economic costs associated with increasing numbers of older people with poor physical and mental health, loneliness, and isolation challenges (Mihalopoulos et al., 2020). The global ageing population has led to a growth in the development of technology designed to improve the health, well-being, independence, and quality of life of older people across various settings (Fang, 2022). This emerging field, known as “AgeTech,” refers to “the use of advanced technologies such as information and communications technologies (ICT’s), technologies related to e-health, robotics, mobile technologies, artificial intelligence (AI), ambient systems, and pervasive computing to drive technology-based innovation to benefit older adults” (Sixsmith, et al., 2020 p1; see also Pruchno, 2019; Sixsmith, Sixsmith, Fang, and Horst, 2020). AgeTech has the potential to contribute in positive ways to the everyday life and care of older people by improving access to services and social supports, increasing safety and community inclusion; increasing independence and health, as well as reducing the impact of disability and cognitive decline for older people (Sixsmith et al, 2020). At a societal level, AgeTech can provide opportunities for entrepreneurs and businesses (where funding and appropriate models exist) (Akpan, Udoh and Adebisi, 2022), reduce the human and financial cost of care (Mihalopoulos et al., 2020), and support ageing well in the right place (Golant, 2015).
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Khan, Samir. AI in Wonderland: Engineering in the Age of Overpromised Technology. SAE International, 2025. https://doi.org/10.4271/epr2025002.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;This report explores the move from traditional industry practices to emerging technologies, specifically the integration of artificial intelligence (AI) solutions in engineering service sectors. It highlights the increasing problem of “technology washing,” when organizations overstate (sometimes deceivingly) their technology abilities and ethics, posing challenges to accountability, transparency, and trust in various fields. The rise of AI-based solutions in sectors like autonomous mobility, manufacturing, and aerospace has exposed a contrast between ambitious future aspirations and current technological barriers. With this, the role of human knowledge in guaranteeing ethical, efficient, and clear technology incorporation becomes essential.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Starting with an examination of today’s technological scene, this report tackles topics such as the buzz around autonomous systems and the difficulties of standardizing fresh innovations. It also points out the problem of organizations exaggerating the capabilities of AI, stressing the importance of human monitoring to manage operational risks and uphold public trust. Practical scenarios in autonomous mobility, aerospace, and manufacturing highlight a significant discrepancy between industry targets and technological feasibilities, stressing the indispensable contribution of human intervention in ensuring successful implementation.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;These examples are analyzed to give insights into current technology successes and limitations and to propose a balanced path for the future. Ultimately, there may be a future where groundbreaking technological advancements remain in harmony with human values. This report challenges established narratives and outlines a path for ethical technological advancement that is transparent and in line with societal values, examining questions like the following:&lt;ul class="list disc"&gt;&lt;li class="list-item"&gt;&lt;div class="htmlview paragraph"&gt;&lt;b&gt;What are some popular misunderstandings and exaggerated claims regarding AI capabilities today?&lt;/b&gt; Examine the divide between how the public sees things and what’s actually true in the context of deceptive AI practices and inflated statements.&lt;/div&gt;&lt;/li&gt;&lt;li class="list-item"&gt;&lt;div class="htmlview paragraph"&gt;&lt;b&gt;How do organizations maintain a balance between rapid technological adoption and human oversight?&lt;/b&gt; Explore ways to maintain human knowledge in decision-making processes despite technological advances.&lt;/div&gt;&lt;/li&gt;&lt;li class="list-item"&gt;&lt;div class="htmlview paragraph"&gt;&lt;b&gt;What are stakeholders’ views on the reliability and safety of autonomous technologies?&lt;/b&gt; Investigate the certainty levels in crucial systems that have implications for public safety and business continuity.&lt;/div&gt;&lt;/li&gt;&lt;li class="list-item"&gt;&lt;div class="htmlview paragraph"&gt;&lt;b&gt;What are the risks associated with overusing AI for critical functions?&lt;/b&gt; Highlight the potential pitfalls of excessive reliance on AI without proper backup systems or redundancy plans.&lt;/div&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;NOTE: SAE Edge Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE Edge Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. These reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.&lt;/div&gt;&lt;/div&gt;
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