Felipe Vásquez

The visibility of women in the labor context of software production is very weak, due to their low participation and socio-cultural difficulties to grow in the labor context. This article aims to show women's role and contribution in the whole life cycle of software development and thus promote gender equity and equality in this area. Methodologically, we have taken the systematic mapping technique to search in the technical literature as a basis. This allows us to obtain a comprehensive view of the work related to gender gaps women face in their work performance in software manufacturing. To complement this study, a form including closed questions was designed to obtain information about the role women play in software production and their expectations in terms of their professional contribution in this field. The main findings allow us to infer that there are biases when defining leaders and roles in software development. It is hoped that the findings of this study will provide inspiring information for women who wish to lead teams in the field of software development by showing the reduction of gender gaps and the possibility of successfully exercising leadership in this field.

The use and management of Big Data in the political sphere has posed unprecedented challenges concerning democratic governance, equity, and the rule of law. As Big Data establishes itself as a resource of growing value, it is imperative to address one of the most critical challenges: data sustainability. Data sustainability involves social and ethical considerations relating to the correct use of personal data. Lack of informed consent and transparency in collecting and using personal data raises serious concerns regarding privacy and individual rights. It is necessary to define regulations and public policies that guarantee citizens’ digital rights based on ethical and democratic standards associated with data management. This article aims to review the literature in the context of data sustainability to identify how Big Data is used, particularly emphasizing its application in the policy domain and the challenges it poses for democratic governance, equity, and law. We have used systematic mapping methodology to collect relevant papers, finding 28 papers associated with democratic governance and Big Data in the context of data sustainability. From the review of these papers, there appears to be a lack of proposals focusing on applying or implementing democratic governance and Big Data. Furthermore, there seem to be no measures to assess the application of Big Data in democratic governance. From these, the need to move towards the definition of formal models that integrate Big Data practices in democratic governance is identified.

The transparency of electronic procedures has become an important strategy to reduce corruption within state organizations and thus promote the sustainable and efficient management of fiscal resources, vital elements in the development of a country. E-government processes have become an important line of development, in which substantial investments have been made to have processes that allow for transparency in a large part of the country’s activities, specifically in the contracting and purchasing of public properties and services. The objective of the study is to present an overview of the work on initiatives that have been used around transparency and electronic procedures of electronic governments to identify which of these initiatives are associated with transparency and which effectively apply to electronic procedures for transparency to learn how these procedures allow for sustainable development of governments. The methodology used in this work was a systematic mapping of the literature, and the main findings suggest that this is a little-explored area.

Los incendios forestales han sido un problema que ha ido en aumento en las últimas décadas. En los recientes años se ha estado utilizando tecnología de Big Data para el procesamiento de grandes volúmenes de datos provenientes de sensores, fotos, imágenes satelitales y datos de valor de la experiencia en terreno. En Chile, no existen sistemas de Big Data para apoyar la gestión en incendios forestales. En este trabajo se busca proponer una arquitectura de Big Data para la gestión del volumen de datos provistos por imágenes satelitales, y que apoye la gestión de incendios en Chile. Esta arquitectura fue probada por medio de un prototipo implementado con herramientas de Cloud Computing, procesando imágenes satelitales, enfocado en el análisis de quemas controladas en la región de La Araucanía. Los resultados muestran que las imágenes resultantes son valiosas para la toma de decisiones en la gestión de quemas dentro de la región. Si bien existe mucho que mejorar, los resultados son alentadores de cara al valor que generan las imágenes resultantes, y a la mejora de este prototipo y de la propia arquitectura. Wildfires have been a growing problem in the last decades. In recent years, Big Data technology has been used to process large volumes of data from sensors, photos, satellite and images, as well as valuable data from field experience. In Chile, there are no Big Data systems to support forest fire management. This work aims to propose a Big Data architecture for managing the volume of data provided by satellite images and supporting fire management in Chile. This architecture was tested through a prototype implemented with Cloud Computing tools, which processes satellite images and is focused on the analysis of controlled burns in the region of La Araucanía. The results show that the resulting images are valuable for decision-making in the management of burns within the region. Although there is much to improve, the results are encouraging in terms of the value generated by the resulting images and the improvement of this prototype and the architecture itself.

Wildland fires have been a rising problem on the worldwide level, generating ecological and economic losses. Specifically, between wildland fire types, uncontrolled fires are critical due to the potential damage to the ecosystem and their effects on the soil, and, in the last decade, different technologies have been applied to fight them. Selecting a specific technology and Decision Support Systems (DSS) is fundamental, since the results and validity of this could drastically oscillate according to the different environmental and geographic factors of the terrain to be studied. Given the above, a systematic mapping was realized, with the purpose of recognizing the most-used DSS and context where they have been applied. One hundred and eighty-three studies were found that used different types of DSS to solve problems of detection, prediction, prevention, monitoring, simulation, administration, and access to routes. The concepts key to the type of solution are related to the use or development of systems or Information and Communication Technologies (ICT) in the computer science area. Although the use of BA and Big Data has increased in recent years, there are still many challenges to face, such as staff training, the friendly environment of DSS, and real-time decision-making.