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Lecture by Prof. Dr. Armando Manuel Barreiros Malheiro da Silva Events

Lecture Cycle at the Federal University of Pará –…

On September 5, 2019, the Federal University of Pará (UFPA) had the opportunity to welcome Professor Armando Manuel Barreiros Malheiro da Silva, from the University of Porto.

This opportunity was made possible by the support of PROCAD Amazônia, represented by Professor Marise Teles Condurú, as well as the support of the Faculties of Library (FABIB) and Archivology Sciences (FAArq) and the Graduate Program in Information Science (PPGCI).

The lectures were given in the Auditorium of the Geosciences Institute at UFPA, who kindly provided the space and equipment for the lectures.

The professor brought insights on topics from the areas of knowledge: Information Science, Library and Archivology, bringing methodological themes and historical trajectory of these areas to the context of sustainability and regional development.

Read more “Lecture Cycle at the Federal University of Pará – Prof. Armando Manuel Barreiros Malheiro da Silva, Ph. D.”
Black Mirror Nosedive screeshot. Copyright to Black Mirror. Book Chapter

Science Fiction and Reality of Data Collecting in Online…

The goal of this study was to relate a science fiction narrative to the current context of Data Collection in OSNS – Online Social Network Services, aiming to build a perception of adherence between the imagined and the real.

An exploratory and descriptive methodology was adopted, with a qualitative and quantitative approach, with a method composed of an exploratory analysis of existing SRSO personal data collecting processes and a descriptive analysis of the form of personal data collecting identified in the sequences from a narrative of an audiovisual production.

The research universe was delimited to the SRSO to data collecting of Facebook, Twitter, and LinkedIn, with the sample composed by the episode Nosedive, part of the Black Mirror series.

For the découpage process of the sequences from the episode, it was identified characteristics referring to the data collection interfaces from OSNS, to filter the elements related to the theme in the sequences content.

The results confirmed the relationship between aspects of the OSNS data collection processes analyzed, with 7 similarities.

Diagram of Entity Relationship Model developed for data collecting. Copyright Fernando de Assis Rodrigues. Book Chapter

Identifying semantic characteristics of user interaction datasets through application…

In an evaluation of a decision, the analyzed fact need to receive inputs from multiple data sources – structuring, integrating, storing, and processing collected data into an output that supports a better understanding of the fact from data, allowing new dimensions of analysis.

The goal of this study is to identify the semantics characteristics of data attributes at the moment of collecting, from dataset’s structures found on data export interfaces on user’s interactions analysis tools, on Internet communication channels, and on web analytics data tools involved in a scientific journal management, through an application of a process of data analysis and data modeling techniques.

The research was delimited to exportable dataset’s available in interfaces from Open Journal Systems, Google Analytics, and Search Console, Twitter Analytics, and Facebook Insights.

It was adopted an exploratory analysis methodology to identify characteristics about how data are available and structured on these data resources. Entity-Relationship Modeling concepts were applied to design and to store data collected from the services, resources, datasets, and attributes.

Also, the collected data was processed into another data structure, adopting the online analytical processing cube as a three-dimensional representation of elements, acting as perspectives of analysis.

This data analysis identified semantic dissonances on definitions of attributes on entities, that may interfering with the development process of relationships between attributes from different datasets, decreasing the potential of interoperability.

Keywords: Data Analysis. Data Collecting. Data. Online Social Networks. User data.

Authors

  1. Fernando de Assis Rodrigues
  2. Pedro Henrique Santos Bisi
  3. Ricardo César Gonçalves Sant’Ana

Full text available at

  1. ISKO
  2. Research Gate
Sociograph of authors. Copyright Fernando de Assis Rodrigues. Conference Paper

Domain Analysis of scientific production about Data Collecting on…

The goal of this study is to identify scientific studies about the thematic of data collecting. For this purpose, it was adopted the domain analysis method on the scientific papers, by an application of Citation and Co-citation Analysis.

The identification of representatives from the thematic of data collecting and the existent dialog among them were obtained by authors and papers metadata sets processing, available on IEEE Xplore(r) Digital Library. As a search strategy, it was used on advanced search the terms ‘Data Collecting’, ‘Data Collect’, and ‘Data Gathering’, concatenated by the boolean operator ‘OR’. This process recovered 2,278 scientific papers and the sample was set only by scientific papers published in scientific journals between the years 1954 and 2018, with a total of 281 papers.

For each paper, the reference section was collected in the HTML document format. It was applied an algorithm to convert formats from HTML documents to CSV files and also to serialize the IEEE Editorial Style found on collected reference data. The algorithm processed 5,867 references and discarded 270 because they are not fit into the IEEE Editorial Style standards adopted on serialization.

From these references, was identified as a total of 8,267 authors. In Citation and Co-citation Analysis, it was applied the Price’s square root law to delimit the authors’ group to 91 participants, rounded to 94 participants because of the 91st participant had the same total of citation of his 3 successors.

After that, the “Cited and Who cited” and the “Absolute Frequency of Co-citation” matrices were generated from an application of an algorithm. By those data, the identification of nationality and the institutional affiliation were obtained by a manual process. Was calculated the social networks indexes i) Network Density, representing the relationship intensity between authors on the network and ii) Centrality Degree, representing the number of relationships received by an author.

The analyzed data resulted in a Network Density value of 3.20 with a standard deviation of 3.34, that is, each researcher has approximately 3 relationships with other network nodes. Also, the resulted value of Centrality Degree was 20.93%, demonstrating dispersion on the network, once that each node has 20.93% of probability to receive some interaction from the network.

This dispersion is associated with the analyzed domain amplitude, once that Data Collecting is a recurrent theme on distinct knowledge areas, but still adherent to IEEE scientific journals context.

When results of the Centrality Degree of each author are analyzed, it is possible to observe a relationship between the results of received citations, indicating that the 13 best-ranked authors by Centrality Degree are also the most cited ones, representing 25.16% of all citations from the network. Also in this group was identified an average of 7.69% from the total of cites to each author, with amplitude varying between 6.12% and 11.76%.

It was concluded that this thematic, although widely cited, shows an American core, related to the institutions UC, USC, and MIT.

Keywords: Data Collecting. Domain Analysis. IEEE.

Authors

  1. Fernando de Assis Rodrigues
  2. Fábio Mosso Moreira
  3. Ricardo César Gonçalves Sant’Ana

Full text available at

  1. X EIICA
  2. Research Gate