A Fuzzy Ontology-Driven Approach to Semantic Interoperability in e-Government Big Data

With the increasing production of information from e-government initiatives, there is also the need to transform a large volume of unstructured data into useful information for society. All this information should be easily accessible and made available in a meaningful and effective way in order to achieve semantic interoperability in electronic \ud government services, which is a challenge to be pursued by governments round the world. Our aim is to discuss the context of e-Government Big Data and to present a framework to promote semantic interoperability through automatic generation of ontologies from unstructured information found in the Internet. We propose the use of fuzzy mechanisms to deal with natural language terms and present some related works \ud found in this area. The results achieved in this study are based on the architectural definition and major components and requirements in order to compose the proposed framework. With this, it is possible to take advantage of the large volume of information generated from e-Government initiatives and use it to benefit society

MR-Radix: a multi-relational data mining algorithm

Abstract\ud \ud \ud \ud Background\ud \ud Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach.\ud \ud \ud \ud Methods\ud \ud Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix.\ud \ud \ud \ud Results\ud \ud This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine.\ud \ud \ud \ud Conclusion\ud \ud The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.This project was financed by CAPES. We thank David R. M. Mercer for English language review and translation

Social media data from two iconic Neotropical big cats: can this translate to action?

IntroductionThere has been a gradual increase in studies of social media data usage in biodiversity conservation. Social media data is an underused source of information with the potential to maximize the outcomes of established conservation measures. In this study, we assessed how structured social media data can provide insight into species conservation through a species conservation plan, based on predefined actions. MethodsWe established a framework centered on a set of steps that go from defining social media platforms and species of interest to applying general analysis of data based on data dimensions—three W’s framework (What, When, Who) and the public engagement that posts received. The final and most important step in our proposed framework is to assess the overlap between social media data outcomes and measures established in conservation plans. In our study, we used the Brazilian National Action Plan (BNAP) for big cats as our model. We extracted posts and metrics about jaguars (Panthera onca) and pumas (Puma concolor) from two social media platforms, Facebook and Twitter. ResultsWe obtained 159 posts for both jaguars and pumas on Facebook (manually) and 23,869 posts for the jaguar and 14,675 posts for the puma on Twitter (through an application user interface). Data were categorized for content and users (only Facebook data) based on analysis of the content obtained and similarities found between posts. We used descriptive statistics for analyzing the metrics extracted for each data dimension (what, when, who, and engagement). We also used algorithms to predict categories in the Twitter database. Our most important findings were based on the development of a matrix summarizing the overlapping actions and dimensions of the data. Our findings revealed that the most prominent category of information for jaguars on Facebook was the sighting of wildlife outside protected areas, while for pumas, it was the trespassing of property by wildlife. From the Twitter dataset, we observed that the most prominent category of information for jaguars was: the sighting of wildlife outside protected areas, while for pumas, it was wildlife depredation by direct or indirect means. We found temporal trends that highlight the importance of categories in understanding information peaks on Facebook and Twitter. DiscussionWhen we analyze online engagement, we see a predominance of positive reactions on Facebook, and on Twitter, we see a balanced reaction between positive and negative. We identified 10 of 41 actions in the BNAP that might benefit from social media data. Most of the actions that could benefit from our dataset were linked to human–wildlife conflicts and threats, such as wildlife–vehicle collisions. Communication and educational actions could benefit from all dimensions of the data. Our results highlight the variety of information on social media to inform conservation programs and their application to conservation actions. We believe that studies on the success of applying data to conservation measures are the next step in this process and could benefit from input from decision-makers

A Language for an Object-oriented Database Management System Based on the MRO Object Modeling Model

Este trabalho define uma linguagem para consulta e manipulação em bases de dados, baseada nos conceitos do MRO (Modelo de Representação de Objetos), denominada LAMRO (Linguagem de Acesso ao MRO). A implementação de um subconjunto de comandos da linguagem foi realizada através de um interpretador de comandos, utilizando o sistema GEO (GErenciador de Objetos) que suporta parte dos conceitos definidos no MRO. A linguagem é caracterizada como uma linguagem de comandos, e situa-se entre as linguagens definidas para um modelo de base de dados especifico. O processo de definição da linguagem utilizou padrões e procedimentos bem determinados para garantir que todos os conceitos do MRO estivessem presentes na linguagem. Foram criados na linguagem dois novos conceitos: Seleções de Objetos e Máscaras. A consulta aos objetos na base de dados é realizada por comandos que manipulam o conceito de Seleções de Objetos. Estes comandos permitem elaborar consultas utilizando a semântica presente na base de dados. As Seleções de Objetos obtidas, podem ser formatadas utilizando o conceito de Máscara. Os comandos que manipulam a seleção de objetos com Máscaras podem ser caracterizados como geradores de relatórios.This work defines a language for database queries, based on the MRO\'s (Object Representation Model) concepts, which is named LAMRO (Access Language to MRO). The implementation of a subset of commands for that language was perfomed through a cotrunand interpreter, using the GEO (Object Manager) system which supports partially the concepts defined in MRO. The LAMRO is characterized as a command language, and it is placed among the languages defined for a specific data base model. Well defined procedures and standards were used in the process of defining this language, which assured that every MRO\'s concepts were presents in the language. Two new concepts were created: Object Selection and Mask. The objects queries in the data base is performed by commands that manipulate the concept of Object Selection. These commands allow to elaborate queries using the semanthics present in the data base. The obtained Object Selection can be formated thorough the concept of Mask. The commands that permit Object Selection through Masks can be characterized as report generator

A Language for an Object-oriented Database Management System Based on the MRO Object Modeling Model

Este trabalho define uma linguagem para consulta e manipulação em bases de dados, baseada nos conceitos do MRO (Modelo de Representação de Objetos), denominada LAMRO (Linguagem de Acesso ao MRO). A implementação de um subconjunto de comandos da linguagem foi realizada através de um interpretador de comandos, utilizando o sistema GEO (GErenciador de Objetos) que suporta parte dos conceitos definidos no MRO. A linguagem é caracterizada como uma linguagem de comandos, e situa-se entre as linguagens definidas para um modelo de base de dados especifico. O processo de definição da linguagem utilizou padrões e procedimentos bem determinados para garantir que todos os conceitos do MRO estivessem presentes na linguagem. Foram criados na linguagem dois novos conceitos: Seleções de Objetos e Máscaras. A consulta aos objetos na base de dados é realizada por comandos que manipulam o conceito de Seleções de Objetos. Estes comandos permitem elaborar consultas utilizando a semântica presente na base de dados. As Seleções de Objetos obtidas, podem ser formatadas utilizando o conceito de Máscara. Os comandos que manipulam a seleção de objetos com Máscaras podem ser caracterizados como geradores de relatórios.This work defines a language for database queries, based on the MRO\'s (Object Representation Model) concepts, which is named LAMRO (Access Language to MRO). The implementation of a subset of commands for that language was perfomed through a cotrunand interpreter, using the GEO (Object Manager) system which supports partially the concepts defined in MRO. The LAMRO is characterized as a command language, and it is placed among the languages defined for a specific data base model. Well defined procedures and standards were used in the process of defining this language, which assured that every MRO\'s concepts were presents in the language. Two new concepts were created: Object Selection and Mask. The objects queries in the data base is performed by commands that manipulate the concept of Object Selection. These commands allow to elaborate queries using the semanthics present in the data base. The obtained Object Selection can be formated thorough the concept of Mask. The commands that permit Object Selection through Masks can be characterized as report generator

Jaguar movement behavior: using trajectories and association rule mining algorithms to unveil behavioral states and social interactions.

Animal movement data are widely collected with devices such as sensors and collars, increasing the ability of researchers to monitor animal movement and providing information about animal behavioral patterns. Animal behavior is used as a basis for understanding the relationship between animals and the environment and for guiding decision-making by researchers and public agencies about environmental preservation and conservation actions. Animal movement and behavior are widely studied with a focus on identifying behavioral patterns, such as, animal group formation, the distance between animals and their home range. However, we observed a lack of research proposing a unified solution that aggregates resources for analyses of individual animal behavior and of social interactions between animals. The primary scientific contribution of this work is to present a framework that uses trajectory analysis and association rule mining [Jaiswal and Agarwal, 2012] to provide statistical measures of correlation and dependence to determine the relationship level between animals, their social interactions, and their interactions with other environmental factors based on their individual behavior and movement data. We demonstrate the usefulness of the framework by applying it to movement data from jaguars in the Pantanal, Brazil. This allowed us to describe jaguar behavior, social interactions among jaguars and their behavior in different landscapes, thus providing a highly detailed investigation of jaguar movement decisions at the fine scale

A model for analysing data portal performance: The biodiversity case

Currently, many museums, botanic gardens and herbariums keep data of biological collections and using computational tools researchers digitalize and provide access to their data using data portals. The replication of databases in portals can be accomplished through the use of protocols and data schema. However, the implementation of this solution demands a large amount of time, concerning both the transfer of fragments of data and processing data within the portal. With the growth of data digitalization in institutions, this scenario tends to be increasingly exacerbated, making it hard to maintain the records updated on the portals. As an original contribution, this research proposes analysing the data replication process to evaluate the performance of portals. The Inter-American Biodiversity Information Network (IABIN) biodiversity data portal of pollinators was used as a study case, which supports both situations: conventional data replication of records of specimen occurrences and interactions between them. With the results of this research, it is possible to simulate a situation before its implementation, thus predicting the performance of replication operations. Additionally, these results may contribute to future improvements to this process, in order to decrease the time required to make the data available in portals. © Rinton Press

Teaching Digital Electronics during the COVID-19 Pandemic via a Remote Lab

Practical knowledge is essential for engineering education. With the COVID-19 pandemic, new challenges have arisen for remote practical learning (e.g., collaborations/experimentations with real equipment when face-to-face offerings are not possible). In this context, LabEAD is a remote lab project that aims to provide practical knowledge learning opportunities for Brazilian engineering students. This article describes how engineering project management methods consisting of application domains, requirement identification, technical solution specification, implementation, and delivery phases, were applied to the development of an Internet of Things (IoT) remote lab architecture. The distributed computing environment allows integration between students’ smartphones and IoT devices deployed in campus labs and in student residences. The code is open-source for facilitated replication and reuse, and the remote lab was built in six months to enable six experiments for the digital electronics lab during the COVID-19 pandemic, covering all the experiments of the original face-to-face offering. More than 70% of the 32 students preferred remote labs over simulations, and only 2 were not approved in the digital electronics course offered remotely.Student perceptions collected by questionnaires showed that they could successfully specify, develop, and present their projects using the remote lab infrastructure in four weeks