A spatial reference and identification system for coastal ice-free land in East Antarctica

We describe a spatial reference system that uniquely identifies 4884 coastal island and continental rock features across East Antarctica. The system comprises a series of maps and a related database, and can be a foundation tool for a wide range of environmental studies.

NoSQL contra SQL en la Administración de datos masivos: Un estudio empírico

When developing a software project, it is important to choose the data base that best suits the needs of the project, whether it is relational or non-relational. This article compares the efficiency of these two types of databases in handling input and reading large amounts of data, using the SGDB MongoDB 3.2 and Microsoft SQL Server 2016. Concluding that, in projects where the handling of a large amount of data and a rapid response are primary requirements, it is better to use a non-relational database. In contrast, if the project requires the use of relationships between entities, without giving greater importance to the response time, it is better to opt for a related database. Resumen: Al momento de desarrollar un proyecto software es importante escoger la base de datos que mejor se ajuste a las necesidades del proyecto. Las opciones de un técnico pueden estar entre una base de datos relacional o no relacional. El presente artículo compara la eficiencia de estos dos tipos de base de datos desde el punto de vista de la entrada y lectura de grandes cantidades de datos. Utilizamos a SGDB MongoDB 3.2 y Microsoft SQL Server 2016 para este estudio empírico. Concluimos que, en proyectos donde el manejo de una gran cantidad de datos y una respuesta rápida son requerimientos primordiales, y considerando estas variables, consideramos que podría ser idóneo el uso de una base de datos no relacional. En contraste, si el proyecto requiere el uso de relaciones entre entidades, sin dar mayor importancia al tiempo de respuesta, podría ser mejor optar por una base de datos relacional.

DIGITAL ENGINEERING GEOLOGICAL MAP OF THE ATHENS PREFECTURE AREA AND RELATED DATABASE MANAGEMENT SYSTEM

A digital engineering-geological map of the Athens Prefecture area was compiled at an original scale of 1:10.000 by IGME in cooperation with Engineering Geology Laboratory of Patras University. The map is related to a database management system constructed according to the project’s special needs, including geotechnical and geological data mainly obtained by boreholes and trial pits. The main map (11 sheets) is accompanied by three thematic maps at an original scale 1:50.000 (hydrogeological, tectonic) and 1:250.000 (seismic epicenters map). It constitute a basic tool for every activity of the Prefecture’s services, in relation to urban development, civil and environmental protection policy, sustainable management of natural resources, continual data supply to citizens and to the technical world. The main advantage of the digital map is the ability of constant updating of the related database, while this procedure should be established to serve social needs. As the mapping was based on a combination of conventional geological mapping techniques and the information from geotechnical database, it is considered to provide useful information for planners and decision makers at a preliminary planning level.

Misinformation and Bias in Metadata Processing: Matching in Large Databases

This article discusses structural, systems, and other types of bias that arise in matching new records to large databases. The focus is databases for bibliographic utilities, but other related database concerns will be discussed. Problems of satisfying a “match” with sufficient flexibility and rigor in an environment of imperfect data are presented, and sources of unintentional variance are discussed.