DISSERTAÇÃO DE MESTRADO - PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO

URI Permanente para esta coleçãohttps://tedebc-teste.ufma.br/handle/tede/1314

Áreas de Concentração e Linhas de Pesquisa: Automação e Contrôle Ciência da Computação Sistemas de Energia Elétrica

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    Mobcons-AT: uma ferramenta de autoria de restrições de mobilidade baseada na transformação de modelos
    (Universidade Federal do Maranhão, 2018-07-30) AZEVEDO JÚNIOR, Adalberto Teixeira; SILVA, Francisco José da Silva e; 406504133-34; http://lattes.cnpq.br/0770343284012942; COUTINHO, Luciano Reis; 504488203-82; http://lattes.cnpq.br/590156473265585; COUTINHO, Luciano Reis; 504488203-82; http://lattes.cnpq.br/590156473265585; SILVA, Francisco José da Silva e; 406504133-34; http://lattes.cnpq.br/0770343284012942; VALE, Samyr Beliche; http://lattes.cnpq.br/1531971102610447; COSTA, Fabio Moreira; http://lattes.cnpq.br/0925150626762308
    There are many situations in which there is a need to monitor in realtime the location and behavior of people and/or vehicles in order to detect possible irregularities and control where they are located and how they move, such as in companies, public transportation and public security. In this dissertation, Mobility Constraints Authoring Tool (MobCons-AT) is presented, an authoring tool for enduser programming that allows the specification of mobility restrictions rules that must be followed by mobile devices. The rules are specified by means of a domain specific language called Mobility Constraints Specification Language (MobCons-SL), the central part of the tool. Once specified in MobCons-SL, these rules are automatically transformed into artifacts that allow real-time detection of constraint violations by using Complex Event Processing (CEP). This approach allows the reduction in the delivery time of solution to the customer and necessary cost for the development of customized software solutions for real-time detection of mobility constraints. This dissertation also describes the use of MobCons-AT in five case studies, showing its applicability for diverse scenarios.
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    Monitoramento ubíquo da saúde mental: detectando padrões de sociabilidade enriquecidos por contexto através do processamento de eventos complexos
    (Universidade Federal do Maranhão, 2020-02-10) MOURA, Ivan Rodrigues de; COUTINHO, Luciano Reis; 504488203-82; http://lattes.cnpq.br/5901564732655853; SILVA, Francisco José da Silva e; 406504133-34; http://lattes.cnpq.br/0770343284012942; SILVA, Francisco José da Silva e; 406504133-34; http://lattes.cnpq.br/0770343284012942; COUTINHO, Luciano Reis; 504488203-82; http://lattes.cnpq.br/5901564732655853; SOARES NETO, Carlos de Salles; http://lattes.cnpq.br/1512846862093142; CAMARGO, Raphael Yokoingawa de; http://lattes.cnpq.br/5519687175393434
    Traditionally, the monitoring of individuals with mental disorders is conducted through face-to-face meetings with professionals specialized in mental health. Today, however, computational methods can use ubiquitous devices (e.g., smartphones and wearable technologies) to monitor social behaviors related to mental health rather than relying on self-reports. These devices represent a valuable source of contextual data that allows the identification of the social activities experienced by individuals in their daily routine. Therefore, the use of these technologies to identify social activities habit enables the recognition of abnormal social behaviors that may be mental disorders indicative. For this reason, this study presents a new approach to monitoring mental health through social situation awareness. This work introduces an algorithm capable of detecting sociability patterns, i.e., it characterizes the periods of the day when the individual socializes habitually. The recognition of social routine is performed under different context conditions (e.g., workday and weekend), which allows differentiating abnormal social behaviors from changes in social habits expected in certain situations. The solution presented is also able to identify changes in social patterns that may be indicative of mental disorders presence. The implementation of the proposed algorithm used the combination of the Frequent Patterns Mining approach with the Complex Events Processing, which allows the realization of the continuous social data stream processing. The evaluation performed demonstrated that context-based recognition provides a better understanding of the social routine, also indicating that the proposed solution is capable of detecting sociability patterns similar to a batch algorithm. Additionally, it was validated the performance of the social behavior change detection mechanism.