Intelligent systems based in hospital database malfunction scenarios

Databases are indispensable for everyday tasks in organizations, particularly in healthcare units. Databases archive important and confidential information about patient's clinical status. Therefore, they must always be available, reliable and at high performance level. In healthcare units, fault tolerant systems ensure the availability, reliability and disaster recovery of data. However, these mechanisms do not allow taking preventive actions in order to avoid fault occurrence. In this context, it emerges the necessity of developing a fault prevention system. It can predict database malfunction in advance and provides early decision taken to solve problems. The objectives of this paper are: monitoring database performance and adapt a forecasting model used in medicine (MEWS) to the database context. Based on mathematical tools it was created a scale that assesses the severity of abnormal situations. In this way, it is possible to define the scenarios where database symptoms must trigger alerts and assistance request.