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.FCT – Fundação para a Ciência e Tecnologi

Machine intelligence, adaptive business intelligence, and natural intelligence

Copyright © 2008 IEEEOne 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.Zbigniew Michalewicz and Matthew Michalewic

Adaptive business intelligence in healthcare - A platform for optimising surgeries

Adaptive Business Intelligence (ABI) combines predictive with prospective analytics in order to give support to the decision making process. Surgery scheduling in hospital operating rooms is a high complex task due to huge volume of surgeries and the variety of combinations and constraints. This type of activity is critical and is often associated to constant delays and significant rescheduling. The main task of this work is to provide an ABI based platform capable of estimating the time of the surgeries and then optimising the scheduling (minimizing the waste of resources). Combining operational data with analytical tools this platform is able to present complex and competitive information to streamline surgery scheduling. A case study was explored using data from a portuguese hospital. The best achieved relative absolute error attained was 6.22%. The paper also shows that the approach can be used in more general applications.This work has been supported by FCT –Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/201

Análisis taxonómico predictivo aplicado a la detección temprana de alumnos universitarios en riesgo de deserción

La deserción universitaria es un problema que aqueja tanto a la educación pública como privada en la Argentina. En este trabajo se presenta una aplicación autoadaptativa predictiva para la detección temprana de alumnos universitarios que se encuentran en riesgo de abandonar sus estudios. La aplicación fue construida a partir del modelo obtenido con una herramienta de extracción de conocimiento, sobre datos de alumnos universitarios, aplicando las fases de una metodología de Adaptive Business Intelligence. Para esto se han tomado en consideración los datos socio-económico-culturales de los alumnos ingresantes así como la finalización de sus estudios, todos obtenidos del Sistema de Gestión Universitaria (SIU). Con estos datos, y aplicando una metodología de Adaptive Business Intelligence se ha generado un modelo de aprendizaje que permite la clasificación de alumnos universitarios como posibles candidatos a la deserción de su estudios.Sociedad Argentina de Informática e Investigación Operativ

Análisis taxonómico predictivo aplicado a la detección temprana de alumnos universitarios en riesgo de deserción

La deserción universitaria es un problema que aqueja tanto a la educación pública como privada en la Argentina. En este trabajo se presenta una aplicación autoadaptativa predictiva para la detección temprana de alumnos universitarios que se encuentran en riesgo de abandonar sus estudios. La aplicación fue construida a partir del modelo obtenido con una herramienta de extracción de conocimiento, sobre datos de alumnos universitarios, aplicando las fases de una metodología de Adaptive Business Intelligence. Para esto se han tomado en consideración los datos socio-económico-culturales de los alumnos ingresantes así como la finalización de sus estudios, todos obtenidos del Sistema de Gestión Universitaria (SIU). Con estos datos, y aplicando una metodología de Adaptive Business Intelligence se ha generado un modelo de aprendizaje que permite la clasificación de alumnos universitarios como posibles candidatos a la deserción de su estudios.Sociedad Argentina de Informática e Investigación Operativ

Análisis taxonómico predictivo aplicado a la detección temprana de alumnos universitarios en riesgo de deserción

La deserción universitaria es un problema que aqueja tanto a la educación pública como privada en la Argentina. En este trabajo se presenta una aplicación autoadaptativa predictiva para la detección temprana de alumnos universitarios que se encuentran en riesgo de abandonar sus estudios. La aplicación fue construida a partir del modelo obtenido con una herramienta de extracción de conocimiento, sobre datos de alumnos universitarios, aplicando las fases de una metodología de Adaptive Business Intelligence. Para esto se han tomado en consideración los datos socio-económico-culturales de los alumnos ingresantes así como la finalización de sus estudios, todos obtenidos del Sistema de Gestión Universitaria (SIU). Con estos datos, y aplicando una metodología de Adaptive Business Intelligence se ha generado un modelo de aprendizaje que permite la clasificación de alumnos universitarios como posibles candidatos a la deserción de su estudios.Sociedad Argentina de Informática e Investigación Operativ

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

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.FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202

The necessities for building a model to evaluate Business Intelligence projects- Literature Review

In recent years Business Intelligence (BI) systems have consistently been rated as one of the highest priorities of Information Systems (IS) and business leaders. BI allows firms to apply information for supporting their processes and decisions by combining its capabilities in both of organizational and technical issues. Many of companies are being spent a significant portion of its IT budgets on business intelligence and related technology. Evaluation of BI readiness is vital because it serves two important goals. First, it shows gaps areas where company is not ready to proceed with its BI efforts. By identifying BI readiness gaps, we can avoid wasting time and resources. Second, the evaluation guides us what we need to close the gaps and implement BI with a high probability of success. This paper proposes to present an overview of BI and necessities for evaluation of readiness. Key words: Business intelligence, Evaluation, Success, ReadinessComment: International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 201

Architecture proposal for deploying and integrating intelligent models in ABI

The integration of Adaptive Business Intelligence systems in healthcare has garnered significant attention due to their potential to manage the ever-growing volume of healthcare data and enhance the quality of care provided to society. ABI systems also play a crucial role in supporting hospital administrators in making strategic decisions. To facilitate the transparency and interoperability of these solutions, the scientific community has embarked on various studies to develop technologic architectures capable of meeting the complex requirements of healthcare settings. One of the key challenges in adopting this technology is the creation and integration of prediction and optimization models in an automated and semi-autonomous manner. This article presents a novel and robust microservices architecture designed to streamline the deployment of intelligent models and seamlessly integrate them within the ABI system. This paper begins by introducing the problem of deploying and integrating intelligent models into ABI systems, providing essential context on ABI systems within the healthcare domain. Subsequently, it details the proposed architecture, outlining its technical approaches and highlighting the advantages it brings to the healthcare ecosystem. Finally, the paper concludes by summarizing the contributions and future directions for research in this critical area, emphasizing the potential impact of this architecture on improving healthcare intelligence systems.This research was funded by Fundação para a Ciência e Tecnologia, within the Project Scope: UIDB/00319/202

Understanding students' mobility habits towards the implementation of an adaptive ubiquitous platform

Adapting technological environments to users is a concern since Mark Weiser launched the concept of ubiquitous computing and, in order to do that, is necessary to understand users’ characteristics. In this context, the purpose of this paper is to present a study about students’ mobility habits within a university campus, having the intention of getting insights towards the best place to set an interactive public display and of predicting the main characteristics of the audience that will be present on that spot in forthcoming periods. Thus, the envisioned results of this work will allow the adaptation of the contents exhibited on the device to the audience. To perform the study, a set of logs of accesses to the university’s Wi-Fi was used, data mining techniques were implemented and forecasting models were built, using the line of work suggested by the CRISP-DM methodology. As result, students profile were built based on past wireless accesses and on their scholar schedules, and three time series models were used (Holt-Winters, Seasonal Naive and Simple Exponential Smoothing) to predict the presence of students on the envisioned spot in future periods