Data modelling for emergency response

Emergency response is one of the most demanding phases in disaster management. The fire brigade, paramedics, police and municipality are the organisations involved in the first response to the incident. They coordinate their work based on welldefined policies and procedures, but they also need the most complete and up-todate information about the incident, which would allow a reliable decision-making.\ud There is a variety of systems answering the needs of different emergency responders, but they have many drawbacks: the systems are developed for a specific sector; it is difficult to exchange information between systems; the systems offer too much or little information, etc. Several systems have been developed to share information during emergencies but usually they maintain the nformation that is coming from field operations in an unstructured way.\ud This report presents a data model for organisation of dynamic data (operational and situational data) for emergency response. The model is developed within the RGI-239 project ‘Geographical Data Infrastructure for Disaster Management’ (GDI4DM)

Implanting Life-Cycle Privacy Policies in a Context Database

Ambient intelligence (AmI) environments continuously monitor surrounding individuals' context (e.g., location, activity, etc.) to make existing applications smarter, i.e., make decision without requiring user interaction. Such AmI smartness ability is tightly coupled to quantity and quality of the available (past and present) context. However, context is often linked to an individual (e.g., location of a given person) and as such falls under privacy directives. The goal of this paper is to enable the difficult wedding of privacy (automatically fulfilling users' privacy whishes) and smartness in the AmI. interestingly, privacy requirements in the AmI are different from traditional environments, where systems usually manage durable data (e.g., medical or banking information), collected and updated trustfully either by the donor herself, her doctor, or an employee of her bank. Therefore, proper information disclosure to third parties constitutes a major privacy concern in the traditional studies

Metamorphic testing framework for SQL queries with null values

Trabajo de Fin de Máster en Ingeniería Informática, Facultad de Informática UCM, Departamento de Sistemas Informáticos y Computación, Curso 2019/2020La falta de información dentro de las bases de datos relacionales expresada mediante valores nulos presenta un problema a la hora de garantizar la calidad de los datos y de las consultas SQL sobre esos datos. Esto ocurre debido a que existen múltiples interpretaciones de los valores nulos y en muchos casos las consultas no consideran que se puedan producir valores nulos como resultado de su evaluación, o bien no reflejan en el código la interpretación correcta de dichos valores. Los valores nulos han estado presentes en las bases de datos desde prácticamente las primeras implementaciones de sistemas de gestión de bases de datos relacionales, pero la implementación del estándar SQL plantea múltiples problemas. Al realizar consultas sobre una base de datos que maneja valores nulos, los resultados pueden no ser los esperados, ya sea por omisión de resultados (falsos negativos) o por resultados incorrectos (falsos positivos). Por esta razón, en este trabajo se propone una herramienta que analice diferentes consultas SQL y permita al desarrollador detectar posibles errores en aquellas consultas que tengan valores nulos utilizando un marco de testing metamórfico. Después de estudiar la bibliografía relacionada sobre pruebas de bases de datos, esta parece ser la primera propuesta que aplica relaciones metamórficas a consultas SQL con valores nulos.The lack of information within relational databases expressed by null values poses important problems when trying to ensure the quality of the data and of the SQL queries evaluated on that data. This occurs because there are multiple interpretations of null values and in many cases the queries either do not consider that null values can occur as a result of their evaluation, or they do not reflect the correct interpretation of these values in the code. Null values have been present in databases since the first implementations of relational database management systems, but the implementation of the SQL standard can generate multiple problems. When querying a database that handles null values, the results may not be produced as expected, either due to the omission of results (false negatives) or incorrect results (false positives). For this reason, this work proposes a tool that analyzes different SQL queries and allows the developer to detect possible errors in those queries that have null values using a metamorphic testing framework. After studying the related literature on database testing, this appears to be the first proposal of the application of metamorphic relationships to SQL queries on null values.Depto. de Sistemas Informáticos y ComputaciónFac. de InformáticaTRUEunpu

Implementing imperfect information in fuzzy databases

Information in real-world applications is often vague, imprecise and uncertain. Ignoring the inherent imperfect nature of real-world will undoubtedly introduce some deformation of human perception of real-world and may eliminate several substantial information, which may be very useful in several data-intensive applications. In database context, several fuzzy database models have been proposed. In these works, fuzziness is introduced at different levels. Common to all these proposals is the support of fuzziness at the attribute level. This paper proposes first a rich set of data types devoted to model the different kinds of imperfect information. The paper then proposes a formal approach to implement these data types. The proposed approach was implemented within a relational object database model but it is generic enough to be incorporated into other database models.ou