Propuesta de metodología para el desarrollo de proyectos de analítica prescriptiva a partir de un Metaanálisis

Trabajo de investigaciónEste trabajo propone una metodología para el desarrollo de proyectos de Analítica Prescriptiva a partir de un Metaanálisis, en cual se reviso de manera sistemática el estado del arte, metodologías y usos en distintas áreas del conocimiento de dicha analítica, encontrando patrones en sus procesos que son comunes a metodologías orientadas a Data Mining como KDD, CRISP-DM y SEMMA.GLOSARIO RESUMEN INTRODUCCIÓN 1. PLANTEAMIENTO DEL PROBLEMA 2. JUSTIFICACIÓN 3. OBJETIVOS 4. ALCANCES Y LIMITACIONES 5. MARCO CONCEPTUAL 6. MARCO TEÓRICO 7. ESTADO DEL ARTE 8. METODOLOGÍA 9. DESARROLLO DEL PROYECTO 10. CONCLUSIONES REFERENCIAS ANEXOSPregradoIngeniero de Sistema

Saf Sci

Big data and analytics have shown promise in predicting safety incidents and identifying preventative measures directed towards specific risk variables. However, the safety industry is lagging in big data utilization due to various obstacles, which may include lack of data readiness (e.g., disparate databases, missing data, low validity) and personnel competencies. This paper provides a primer on the application of big data to safety. We then describe a safety analytics readiness assessment framework that highlights system requirements and the challenges that safety professionals may encounter in meeting these requirements. The proposed framework suggests that safety analytics readiness depends on (a) the quality of the data available, (b) organizational norms around data collection, scaling, and nomenclature, (c) foundational infrastructure, including technological platforms and skills required for data collection, storage, and analysis of health and safety metrics, and (d) measurement culture, or the emergent social patterns between employees, data acquisition, and analytic processes. A safety-analytics readiness assessment can assist organizations with understanding current capabilities so measurement systems can be matured to accommodate more advanced analytics for the ultimate purpose of improving decisions that mitigate injury and incidents.CC999999/ImCDC/Intramural CDC HHSUnited States

A machine learning approach to air traffic interdependency modelling and its application to trajectory prediction

Air Traffic Management is evolving towards a Trajectory-Based Operations paradigm. Trajectory prediction will hold a key role supporting its deployment, but it is limited by a lack of understanding of air traffic associated uncertainties, specifically contextual factors. Trajectory predictors are usually based on modelling aircraft dynamics based on intrinsic aircraft features. These aircraft operate within a known air route structure and under given meteorological conditions. However, actual aircraft trajectories are modified by the air traffic control depending on potential conflicts with other traffics. This paper introduces surrounding air traffic as a feature for ground-based trajectory prediction. The introduction of air traffic as a contextual factor is addressed by identifying aircraft which are likely to lose the horizontal separation. For doing so, this paper develops a probabilistic horizontal interdependency measure between aircraft supported by machine learning algorithms, addressing time separations at crossing points. Then, vertical profiles of flight trajectories are characterised depending on this factor and other intrinsic features. The paper has focused on the descent phase of the trajectories, using datasets corresponding to an en-route Spanish airspace volume. The proposed interdependency measure demonstrates to identify in advance conflicting situations between pairs of aircraft for this use case. This is validated by identifying associated air traffic control actions upon them and their impact on the vertical profile of the trajectories. Finally, a trajectory predictor for the vertical profile of the trajectory is developed, considering the interdependency measure and other operational factors. The paper concludes that the air traffic can be included as a factor for the trajectory prediction, impacting on the location of the top of descent for the specific case which has been studied

DART: A Data Analytics Readiness Assessment Tool For Use In Occupational Safety

The safety industry is lagging in Big Data utilization due to various obstacles, which may include lack of analytics readiness (e.g. disparate databases, missing data, low validity) or competencies (e.g. personnel capable of cleaning data and running analyses). A safety-analytics maturity assessment can assist organizations with understanding their current capabilities. Organizations can then mature more advanced analytics capabilities to ultimately predict safety incidents and identify preventative measures directed towards specific risk variables. This study outlines the creation and use of an industry-specific readiness assessment tool. The proposed safety-analytics assessment evaluates the (a) quality of the data currently available, (b) organizational norms around data collection, scaling, and nomenclature, (c) foundational infrastructure for technological capabilities and expertise in data collection, storage, and analysis of safety and health metrics, and (d) measurement culture around employee willingness to participate in reporting, audits, inspections, and observations and how managers use data to improve workplace safety. The Data Analytics Readiness Tool (DART) was piloted at two manufacturing firms to explore the tool's reliability and validity. While there were reliability concerns for inter-rater agreement across readiness factors for individual variables, DART users agreed on and accurately assessed organizational capabilities for each level of analytics