Adaptive Business Intelligence Research Articles

Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review.

In order to comprehensively understand the characteristics of Adaptive Business Intelligence (ABI) in Healthcare, this study is structured to provide insights into the common features and evolving patterns within this domain. Applying the Sheridan's Classification as a framework, we aim to assess the degree of autonomy exhibited by various ABI components. Together, these objectives will contribute to a deeper understanding of ABI implementation and its implications within the Healthcare context. A comprehensive search of academic databases was conducted to identify relevant studies, selecting AIS e-library (AISel), Decision Support Systems Journal (DSSJ), Nature, The Lancet Digital Health (TLDH), PubMed, Expert Systems with Application (ESWA) and npj Digital Medicine as information sources. Studies from 2006 to 2022 were included based on predefined eligibility criteria. PRISMA statements were used to report this study. The outcomes showed that ABI systems present distinct levels of development, autonomy and practical deployment. The high levels of autonomy were essentially associated with predictive components. However, the possibility of completely autonomous decisions by these systems is totally excluded. Lower levels of autonomy are also observed, particularly in connection with prescriptive components, granting users responsibility in the generation of decisions. The study presented emphasizes the vital connection between desired outcomes and the inherent autonomy of these solutions, highlighting the critical need for additional research on the consequences of ABI systems and their constituent elements. Organizations should deploy these systems in a way consistent with their objectives and values, while also being mindful of potential adverse effects. Providing valuable insights for researchers, practitioners, and policymakers aiming to comprehend the diverse levels of ABI systems implementation, it contributes to well-informed decision-making in this dynamic field.

Interoperability Architecture proposal for Adaptive Business Intelligence Systems in Healthcare Environments

The integration of systems for adaptive business intelligence (ABI) in the healthcare industry has the potential to revolutionize and reform the way organizations approach data analysis and decision-making. By providing real-time, actionable insights and enabling organizations to continuously adapt and evolve, ABI has the potential to drive better outcomes, reduce costs and improve the overall quality of patient care.This article proposes an interoperability architecture that allows the seamless use and integration of HL7 messages and the information they contain as an interface with these systems. The proposed architecture follows a microservices paradigm with an emphasis on its modularity and distribution capacity and is part of an effort to achieve interoperability and ease of integration with ABI systems, while never neglecting key characteristics such as speed, security and the best practices in software development and architecture.

Efficient use of data and knowledge transfers in information systems

Business intelligence under data treatment is consolidated in one of the differentiating factors between organizations. Business intelligence (BI) solutions are not only applicable for commercial purposes, they are also used to measure the performance and fulfillment of goals in organizations. The objective of this research is to apply an adaptive BI model in order to diagnose the real state of the organization and formulate from it improvement plans capable of raising the levels of performance and profits of the same. The application of the adaptive model resulted in the establishment of coordination between Information Technology solutions and the organizational culture of organizations. It was also established that private organizations have achieved a better position of maturity due to their horizontal operating policies. The exploratory method has the perspective of pointing out important aspects of the problem and of finding the fundamental stages in an investigation, the data collection technique is the survey, to be able to interview and know the opinions of the people who work with technology and to be able to establish the most important data.

Adaptive Business Intelligence platform and its contribution as a support in the evolution of Hospital 4.0

Abstract For many years there has been debate about what healthcare systems will look like in the future. Covid-19 has caused all Healthcare organizations to quickly adopt new solutions and evolution in this sector is a certainty. This research looks at the role that an Adaptive Business Intelligence (ABI) system can play in the evolution to a Hospital 4.0 and how it needs to evolve to achieve full integration between hospital services and the technological solutions. Thus, the first version of this system is explained and that will serve as a basis for the development of a more robust platform, with a view to a more effective environment, both for the professionals and for the main beneficiary of this type of service, the patient.

Adaptive Business Intelligence: A New Architectural Approach

Healthcare systems face enormous challenges, fundamentally due to the amount of data generated daily in a hospital environment, which forces entities to reflect on how to organize and use the same data. Currently, the number of studies at this level is growing, focusing on the innovation to be implemented, so that this same sector can adopt new methodologies, architectures and technologies that allow a more efficient support of existing hospital processes, as well as the results to be provided to all professionals involved in this area. In this research, an Adaptive Business Intelligence architecture is proposed, whose contribution was supported by the realization of an adequate conceptual and technological framework describing its development at different levels. Thus, a possible modernization of several working methods is initiated, with the introduction of an architecture capable of contributing to several factors, both at clinical and administrative levels, meeting the needs of a hospital system, regarding the design, development, implementation and demonstration of results.

Inteligencia empresarial adaptativa y su aplicación predictiva sobre patrones taxonómicos de deserción

<p>Las variables que surgen de las bases de datos de una Universidad Nacional de Argentina, favorecen el análisis y aplicación de una metodología de Inteligencia de Negocio Adaptiva, mediante la cual, aplicando los filtros adecuados de manera metodológica, como se expone en el presente trabajo, permitirán encontrar los alumnos en riesgo de desertar y realizar un trabajo preventivo sobre ellos. Existen datos socio-psicométricos, económicos y culturales, además de elementos físicos que han de ser tomados en consideración relacionándolos con la terminalidad de estudios de los sujetos evaluados. Surge a partir de allí, un modelo predictivo de aprendizaje que clasifica las causalidades de deserción. Finalmente, se puede explotar este conocimiento para desarrollar la aplicación en cualquier lenguaje de programación, de manera que aproveche tal conocimiento, con uso en nuevos sujetos y sus datos. En virtud de ello, se exponen las principales ventajas obtenidas de las capacidades predictivas sobre los datos del caso.</p>

Study of adaptive business intelligence techniques for optimization of organizational decision making (In select automobile manufacturing companies in Maharashtra)

Study of adaptive business intelligence techniques for optimization of organizational decision making (In select automobile manufacturing companies in Maharashtra)

KPI-based decision impact evaluation system for adaptive business intelligence

KPI-based decision impact evaluation system for adaptive business intelligence

Web 2.0 environmental scanning and adaptive decision support for business mergers and acquisitions

Globalization has triggered a rapid increase in cross-border mergers and acquisitions (M&As). However, research shows that only 17 percent of cross-border M&As create shareholder value. One of the main reasons for this poor track record is top management's lack of attention to nonfinancial aspects (e.g., sociocultural aspects) of M&As. With the rapid growth of Web 2.0 applications, online environmental scanning provides top executives with unprecedented opportunities to tap into collective web intelligence to develop better insights about the sociocultural and political--economic factors that cross-border M&As face. Grounded in Porter's five forces model, one major contribution of our research is the design of a novel due diligence scorecard model that leverages collective web intelligence to enhance M&A decision making. Another important contribution of our work is the design and development of an adaptive business intelligence (BI) 2.0 system underpinned by an evolutionary learning approach, domain-specific sentiment analysis, and business relation mining to operationalize the aforementioned scorecard model for adaptive M&A decision support. With Chinese companies' cross-border M&As as the business context, our experimental results confirm that the proposed adaptive BI 2.0 system can significantly aid decision makers under different M&A scenarios. The managerial implication of our findings is that firms can apply the proposed BI 2.0 technology to enhance their strategic decision making, particularly when making cross-border investments in targeted markets for which private information may not be readily available.

Machine intelligence, adaptive business intelligence, and natural intelligence [Research Frontier

One of the key observations of the author was that machine intelligence might be defined as the capability of a system to adapt its behavior to meet desired goals in a range of environments. Interestingly, the three components of prediction, adaptation, and optimization constitute the core modules of adaptive business intelligence systems. Clearly, the future of the business intelligence industry lies in systems that can make decisions, rather than tools that produce detailed reports.

Adaptive Business Intelligence

Since the computer age dawned on mankind, one of the most important areas in information technology has been that of “decision support.” Today, this area is more important than ever. Working in dynamic and ever-changing environments, modern-day managers are responsible for an assortment of far reaching decisions: Should the company increase or decrease its workforce? Enter new markets? Develop new products? Invest in research and development? The list goes on. But despite the inherent complexity of these issues and the ever-increasing load of information that business managers must deal with, all these decisions boil down to two fundamental questions: What is likely to happen in the future? What is the best decision right now? Whether we realize it or not, these two questions pervade our everyday lives — both on a personal and professional level. When driving to work, for instance, we have to make a traffic prediction before we can choose the quickest driving route. At work, we need to predict the demand for our product before we can decide how much to produce. And before investing in a foreign market, we need to predict future exchange rates and economic variables. It seems that regardless of the decision being made or its complexity, we first need to make a prediction of what is likely to happen in the future, and then make the best decision based on that prediction. This fundamental process underpins the basic premise of Adaptive Business Intelligence.

Adaptive business intelligence based on evolution strategies: some application examples of self-adaptive software

Self-adaptive software is one of the key discoveries in the field of evolutionary computation, originally invented in the framework of so-called Evolution Strategies in Germany. Self-adaptivity enables the algorithm to dynamically adapt to the problem characteristics and even to cope with changing environmental conditions – as they occur in unforeseeable ways in many real-world business applications. In evolution strategies, self-adaptability is generated by means of an evolutionary search process that operates on the solutions generated by the method as well as on the evolution strategy’s parameters, i.e., the algorithm itself. By focusing on a basic algorithmic variant of evolution strategies, the fundamental idea of self-adaptation is outlined in this paper. Applications of evolution strategies for NuTech’s clients include the whole range of business tasks, including R&D, technical design, control, production, quality control, logistics, and management decision support. While such examples can of course not be disclosed, we illustrate the capabilities of evolution strategies by giving some simpler application examples to problems occurring in traffic control and engineering.