An automated method to analyze personal record cards generated by Organismo Coordinador de Operaciones Antisubversivas (OCOA) during the civic-military dictatorship in Uruguay between 1973 and 1985 is presented. These personal records are part of Archivo Berrutti, a collection of digitized documents, partially processed by the Uruguayan government. The main goal of this study is to extract the maximum amount of information from the personal record cards to ease the analysis by specialized teams. To achieve the goal, a methodology which combines image processing and pattern recognition techniques has been developed. This methodology takes advantage of the known geometric structure of the cards to straighten them, identify and extract relevant pieces, classify them, extract relevant fields, and transcribe crucial information such as names and identification numbers.

The application of graph databases to different domains is gaining momentum. The Resource Description Framework (RDF) is one of the data models supported by graph databases, and SPARQL is the standard query language for RDF graphs. These databases are also known as RDF triplestores. Many triplestores are implemented over the relational data model, using tables to store graphs and translating SPARQL queries into SQL queries, and this approach can lead to unnecessary overheads. On the other hand, in the context of High- Performance Computing (HPC), implementations over hybrid hardware platforms using Numerical Linear Algebra (NLA) operations have become an effective and efficient computing strategy in the last decade. In particular, Graphics Processing Units (GPUs) have been adopted to perform general-purpose computations due to their high performance, reasonable prices, and an attractive relationship between computing capacity and energy consumption. In the context described above, this paper presents an initial study on the efficient implementation of a set of SPARQL queries in terms of NLA operations. Additionally, we evaluate the performance of implementing these operations on GPUs.

By February 2021, Uruguay was experiencing the first wave of the COVID-19 pandemic, while many countries were already suffering the second wave. Several countries took various measures to prevent the saturation of the health system, ranging from closure of restaurants and suspension of classes to nighttime traffic restrictions. In this paper, we explore the effect of mobility restriction measures on the infection incidence in countries that are in some way similar to Uruguay: they have between one and twelve million inhabitants, a reasonable testing effort and they had the epidemic under control at some point. For these countries, we study mobility indexes provided by Google, an index on governmental measures compiled by the University of Oxford, and the daily new cases per 100,000 inhabitants. First, we observed that the mobility reported by Google is directly related to government measures: the higher the level of restrictive measures, the lower the mobility index. Then, we analyze the influence of mobility reduction on the growth/decrease speed of the 7-day average of new cases per 100,000 inhabitants (P7) and show that high levels of mobility reduction lead to a decrease in the index. Finally, we related the required duration of mobility restrictions with the P7 maximum and also point out the risk of lifting the measures too early.

Multi-tier architectures have been the de facto standard for web applications, leaving little room for alternative solutions. Despite this, there is diversity in the proposals, especially in the tiers' number, size, and responsibilities. In particular, the database-centric approach aims to implement application logic and behavior within an RDBMS. In this work, we present, model, and propose to extend the database-centric approach into an RDBMS-only architecture, where the whole multi-tiered application is implemented in the database server. We present a characterization and description of the architecture and an early prototype that implements the proposal. It is important to note that both the database-centric and the proposed RDBMS-only architectures are a particular case of a three-layered model that needs to be differentiated from monolithic systems. Our preliminary results show that this approach is not only feasible but also advisable in some cases.

Publication and sharing of multidimensional (MD) data on the Semantic Web (SW) opens new opportunities for the use of On-Line Analytical Processing (OLAP). The RDF Data Cube (QB) vocabulary, the current standard for statistical data publishing, however, lacks key MD concepts such as dimension hierarchies and aggregate functions. QB4OLAP was proposed to remedy this. However, QB4OLAP requires extensive manual annotation and users must still write queries in SPARQL, the standard query language for RDF, which typical OLAP users are not familiar with. In this demo, we present QB2OLAP, a tool for enabling OLAP on existing QB data. Without requiring any RDF, QB(4OLAP), or SPARQL skills, it allows semi-automatic transformation of a QB data set into a QB4OLAP one via enrichment with QB4OLAP semantics, exploration of the enriched schema, and querying with the high-level OLAP language QL that exploits the QB4OLAP semantics and is automatically translated to SPARQL.