Modelo de fusão dirigido por humanos e ciente de qualidade de informação

Abstract

Situational Awareness (SAW) is a cognitive process widely spread in areas that require critical decision-making and refers to the level of consciousness that an individual or team has about a situation. In the emergency management domain, the situational information inferred by decision support systems affects the SAW of human operators, which is also influenced by the dynamicity and critical nature of the events. Failures in SAW, typically caused by high levels of stress, information overload and the inherent need to perform multiple tasks, can induce human operators to errors in decision-making, resulting in risks to life, assets or to the environment. Data fusion processes present opportunities to improve human operators’ SAW and enrich their knowledge on situations. However, problems related to the quality of information can lead to uncertainties, especially when human operators are also sources of information, requiring the restructuring of the fusion process. The state of the art of data and information fusion models presents approaches with limited participation of human operators, typically reactive, besides solutions that are restricted in mechanisms to manage the quality of information throughout the fusion process. Thus, the present work presents a new information fusion model, called Quantify (Quality-aware Human-driven Information Fusion Model), whose major differentials are the greater involvement of human operators and the use of the information quality management throughout the fusion process. In order to support the Quantify model, an innovative methodology was developed for the assessment and representation of data and information quality, called IQESA (Information Quality Assessment Methodology in the Context of Emergency Situation Awareness) specialized in the context of emergency situational awareness and which also involves the human operator. In order to validate the model and the methodology, a service-oriented architecture and two emergency situation assessment systems were developed, one guided by the Quantify model and another driven by the state-of-the-art model (User-Fusion). In a case study, robbery events reported to the emergency response service of the S˜ao Paulo State Military Police (Pol´ıcia Militar do Estado de S˜ao Paulo - PMESP) were submitted to the systems and then evaluated by the PMESP operators, revealing higher rates of SAW by the application of the Quantify model. These positive results confirm the need of this new model and methodology, besides revealing an opportunity to enrich the current emergency response system used by PMESP.Situational Awareness (SAW) is a cognitive process widely spread in areas that require critical decision-making and refers to the level of consciousness that an individual or team has about a situation. In the emergency management domain, the situational information inferred by decision support systems affects the SAW of human operators, which is also influenced by the dynamicity and critical nature of the events. Failures in SAW, typically caused by high levels of stress, information overload and the inherent need to perform multiple tasks, can induce human operators to errors in decision-making, resulting in risks to life, assets or to the environment. Data fusion processes present opportunities to improve human operators’ SAW and enrich their knowledge on situations. However, problems related to the quality of information can lead to uncertainties, especially when human operators are also sources of information, requiring the restructuring of the fusion process. The state of the art of data and information fusion models presents approaches with limited participation of human operators, typically reactive, besides solutions that are restricted in mechanisms to manage the quality of information throughout the fusion process. Thus, the present work presents a new information fusion model, called Quantify (Quality-aware Human-driven Information Fusion Model), whose major differentials are the greater involvement of human operators and the use of the information quality management throughout the fusion process. In order to support the Quantify model, an innovative methodology was developed for the assessment and representation of data and information quality, called IQESA (Information Quality Assessment Methodology in the Context of Emergency Situation Awareness) specialized in the context of emergency situational awareness and which also involves the human operator. In order to validate the model and the methodology, a service-oriented architecture and two emergency situation assessment systems were developed, one guided by the Quantify model and another driven by the state-of-the-art model (User-Fusion). In a case study, robbery events reported to the emergency response service of the S˜ao Paulo State Military Police (Pol´ıcia Militar do Estado de S˜ao Paulo - PMESP) were submitted to the systems and then evaluated by the PMESP operators, revealing higher rates of SAW by the application of the Quantify model. These positive results confirm the need of this new model and methodology, besides revealing an opportunity to enrich the current emergency response system used by PMESP.