Data Science: The state of the art

As organizações têm cada vez mais acesso a um maior volume de dados. A contribuir para este fenómeno está o desenvolvimento tecnológico e o conceito de internet das coisas, que permite cada vez mais interligar mecanismos e dispositivos, e consequentemente, diversificar as fontes de informação. Esta evolução tecnológica permite que os dados sejam retirados das mais diversas formas e plataformas, quer qualitativa quer quantitativamente. Este fenómeno que designamos por Big Data, está a tornar disruptivas muitas empresas, alterando desta forma modelos de negócio, inovando o marketing, produtos e serviços e tornando ainda algumas organizações mais eficientes. Sabe-se também que as capacidades analíticas das empresas têm de dar resposta a este crescimento de dados através de modelos mais avançados, orientados para tomadas de decisões mais acertadas, como a análise preditiva e prescritiva, e recorrendo a técnicas de Data Mining ou Machine Learning por forma a otimizar a gestão dos recursos e contribuindo para a eficácia e eficiência das organizações. Esta condição obriga as empresas a aumentar a sua capacidade de adaptação e decisão, para que os dados e a sua compreensão se tornem fontes de vantagem competitivaOrganizations increasingly have access to a growing volume of data. Contributing to this is the technological development and the concept of the internet of things, which allows increasingly interconnecting mechanisms and devices, and consequently diversify the sources of information. This technological evolution allows the data to be withdrawn in the most diverse forms and platforms, both qualitatively and quantitatively. This phenomenon, which we call Big Data, is disrupting many companies. Changing this way, business models, innovating the marketing, products and services, still making some organizations more efficient. It is also known that the analytical capabilities of companies have to respond to this increase in data through more advanced models oriented towards better decision making, such as predictive and prescriptive analysis and using Data Mining or Machine Learning techniques to optimize the management of resources and contributing to efficiency and effectiveness. This condition forces companies to increase their ability to adapt and make decisions, so that data and their understanding become sources of competitive advantage

Using Case-Based Reasoning for Simulation Modeling in Healthcare

The healthcare system is always defined as a complex system. At its core, it is a system composed of people and processes and requires performance of different tasks and duties. This complexity means that the healthcare system has many stakeholders with different interests, resulting in the emergence of many problems such as increasing healthcare costs, limited resources and low utilization, limited facilities and workforce, and poor quality of services. The use of simulation techniques to aid in solving healthcare problems is not new, but it has increased in recent years. This application faces many challenges, including a lack of real data, complicated healthcare decision making processes, low stakeholder involvement, and the working environment in the healthcare field. The objective of this research is to study the utilization of case-based reasoning in simulation modeling in the healthcare sector. This utilization would increase the involvement of stakeholders in the analysis process of the simulation modeling. This involvement would help in reducing the time needed to build the simulation model and facilitate the implementation of results and recommendations. The use of case-based reasoning will minimize the required efforts by automating the process of finding solutions. This automation uses the knowledge in the previously solved problems to develop new solutions. Thus, people could utilize the simulation modeling with little knowledge about simulation and the working environment in the healthcare field. In this study, a number of simulation cases from the healthcare field have been collected to develop the case-base. After that, an indexing system was created to store these cases in the case-base. This system defined a set of attributes for each simulation case. After that, two retrieval approaches were used as retrieval engines. These approaches are K nearest neighbors and induction tree. The validation procedure started by selecting a case study from the healthcare literature and implementing the proposed method in this study. Finally, healthcare experts were consulted to validate the results of this study