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Previous issue date: 2016-01-26 / Não recebi financiamento / 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. / Consciência Situacional (Situational Awareness - SAW) é um processo cognitivo amplamente difundido em áreas que demandam a tomada de decisão critica e se refere ao nível de consciência que um indivíduo ou equipe detém sobre uma situação. No domínio de gerenciamento de emergências, a informação situacional inferida por sistemas de apoio à decisão afeta a SAW de operadores humanos, a qual é também influenciada pela dinamicidade e natureza crítica dos eventos. Falhas de SAW, tipicamente provocadas pelo alto nível de stress, sobrecarga de
informação e pela inerente necessidade de realização de múltiplas tarefas, podem induzir operadores humanos a erros no processo decisório e acarretar riscos `a vida, ao patrimônio ou ao meio ambiente. Processos de fusão de dados apresentam oportunidades para aprimorar a SAW de operadores humanos e enriquecer o seu conhecimento sobre situações. Entretanto, problemas
referentes `a qualidade da informação podem gerar incertezas, principalmente quando operadores humanos são também fontes de informação, demandando assim a reestruturação do processo de fusão. O estado da arte em modelos de fusão de dados e informações apresenta abordagens com limitada participação de humanos, tipicamente reativa, além das soluções serem restritas em mecanismos para gerir a qualidade da informação. Assim, este trabalho
apresenta um novo modelo de fusão de informações, denominado Quantify (Quality-Aware Human-Driven Information Fusion Model), cujos principais diferenciais são a intensificação da participação humana e o emprego continuo da gestão da qualidade da informação ao longo do processo de fusão. Em suporte ao modelo Quantify, foi desenvolvida uma metodologia inovadora para a avaliação e representação da qualidade de dados e informações, denominada
IQESA (Information Quality Assessment Methodology in the Context of Emergency Situation Awareness), especializada no contexto de consciência situacional de emergências e que também envolve o operador humano. Para validar o modelo e a metodologia, uma arquitetura orientada a serviços e dois sistemas de avaliação de situações de emergência foram desenvolvidos, um deles orientado pelo modelo Quantify e outro dirigido pelo modelo do estado da arte (User-Fusion). Em estudo de caso, eventos de roubo relatados ao serviço de atendimento a emergências da Polícia Militar do Estado de São Paulo (PMESP) foram submetidos aos sistemas e avaliados por operadores da PMESP, revelando índices superiores de SAW pelo
emprego do modelo Quantify. Tais resultados positivos corroboram com a necessidade deste novo modelo e metodologia, além de revelar uma oportunidade de enriquecimento do sistema atual de atendimento a emergências utilizado pela PMES
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufscar.br:ufscar/8104 |
| Date | 26 January 2016 |
| Creators | Botega, Leonardo Castro |
| Contributors | Araujo, Regina Borges de |
| Publisher | Universidade Federal de São Carlos, Câmpus São Carlos, Programa de Pós-graduação em Ciência da Computação, UFSCar |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis |
| Source | reponame:Repositório Institucional da UFSCAR, instname:Universidade Federal de São Carlos, instacron:UFSCAR |
| Rights | info:eu-repo/semantics/openAccess |
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