pubs2024

@inproceedings{Sousa202316,

title = {COVID-19 Cases and Their Impact on Global Air Traffic},

author = {R. Sousa and J. Gomes and J. Gomes and M. Arcipreste and P. Guimarães and D. Oliveira and J. Machado},

editor = {Peixoto H. Machado J.M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172721784&doi=10.1007%2f978-3-031-38204-8_2&partnerID=40&md5=64a4cb6986ae3fc0935b9c82e33c5145},

doi = {10.1007/978-3-031-38204-8_2},

issn = {18678211},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},

volume = {485 LNICST},

pages = {16-27},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The air transport industry has marked unprecedented changes throughout the pandemic period of Covid-19 infection. Mostly in the number of flights canceled, liquidation of airlines and disconnection between points worldwide. The existing documentation relating to air traffic, in the specific period of this study, can be extracted, processed and visualized through tools widely used to support case study assumptions, especially in the context of Big Data. This document addresses to the use of a Big Data architecture to survey, analyze and explore different data sources and consequent loading, transformation and visual representation of the results obtained in order to verify the impact of the number of cases of infection by Covid-19 in air traffic. Based on the results obtained through the described methodology, it can be stated that the number of cases of infection by Covid-19 presents a significant impact on the number of flights that occurred ever since (around 50% less flights). © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},

note = {cited By 0; Conference of 3rd International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022 ; Conference Date: 16 November 2022 Through 18 November 2022; Conference Code:298899},

keywords = {Air traffics; Air transport industry; Big data architecture; Case-studies; Covid-19 world impact; Data architectures; Data-source; GDP; Global air traffic; Survey analysis, Air transportation; Aviation; COVID-19; Metadata, Big data},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lopes2023297,

title = {Big Data in Healthcare Institutions: An Architecture Proposal},

author = {J. Lopes and R. Sousa and A. Abelha and J. Machado},

editor = {Zeng D. Huang H. Hou R.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163403837&doi=10.1007%2f978-3-031-33614-0_20&partnerID=40&md5=32838fc94653d1c8bf46169b58d98913},

doi = {10.1007/978-3-031-33614-0_20},

issn = {18678211},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},

volume = {480 LNICST},

pages = {297-311},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Healthcare institutions are complex organizations dedicated to providing care to the population. Continuous improvement has made the care provided a factor of excellence in the population, improving people’s daily lives and increasing average life expectancy. Even so, the resulting aging has caused patterns to increase day by day and the paradigm of medicine to shift from reaction to prevention. Often, the principle of evidence-based medicine is compromised by lack of evidence on pathogenic mechanisms, risk prediction, lack of resources, and effective therapeutic strategies. This is even more evident in pandemic situations. The current data management tools (centered in a single machine) do not have an ideal behavior for the processing of large amounts of information. This fact combined with the lack of sensitivity for the health area makes it imminent the need to create and implement an architecture that performs this management and processing effectively. In this sense, this paper aims to study the problem of knowledge construction from Big Data in health institutions. The main goal is to present an architecture that deals with the adversities of the big data universe when applied to health. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},

note = {cited By 0; Conference of 11th and 12th EAI International Conference on Big Data Technologies and Applications, BDTA 2021 and BDTA 2022 ; Conference Date: 10 December 2022 Through 11 December 2022; Conference Code:295539},

keywords = {Architecture; Computer architecture; Health care; Information management; Information use; Medical information systems; Real time systems, Average life expectancy; Continuous improvements; Daily lives; Evidence-based medicine; Healthcare information system; Healthcare institutions; Pathogenic mechanisms; Real-time information systems; Risk predictions; Systems architecture, Big data},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sousa2022199,

title = {The Covid-19 Influence on the Desire to Stay at Home: A Big Data Architecture},

author = {R. Sousa and D. Oliveira and A. Carneiro and L. Pinto and A. Pereira and A. Peixoto and H. Peixoto and J. Machado},

editor = {Tino P. Camacho D. Yin H.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144825704&doi=10.1007%2f978-3-031-21753-1_20&partnerID=40&md5=07933409c2e62457b481165cbb7438e0},

doi = {10.1007/978-3-031-21753-1_20},

issn = {03029743},

year  = {2022},

date = {2022-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {13756 LNCS},

pages = {199-210},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The COVID-19 pandemic has had an impact on many aspects of society in recent years. The ever-increasing number of daily cases and deaths makes people apprehensive about leaving their homes without a mask or going to crowded places for fear of becoming infected, especially when vaccination was not available. People were expected to respect confinement rules and have their public events cancelled as more restrictions were imposed. As a result of the pandemic’s insecurity and instability, people became more at ease at home, increasing their desire to stay at home. The present research focuses on studying the impact of the COVID-19 pandemic on the desire to stay at home and which metrics have a greater influence on this topic, using Big Data tools. It was possible to understand how the number of new cases and deaths influenced the desire to stay at home, as well as how the increase in vaccinations influenced it. Moreover, investigated how gatherings and confinement restrictions affected people’s desire to stay at home. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of 23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022 ; Conference Date: 24 November 2022 Through 26 November 2022; Conference Code:287419},

keywords = {Behavior analysis; Business-intelligence; Data architectures; Data tools; New case; Research focus; Stay at home, Big data, COVID-19},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Alves2022393,

title = {Survey for Big Data Platforms and Resources Management for Smart Cities},

author = {C. Alves and A. Chaves and C. Rodrigues and E. Ribeiro and A. Silva and D. Durães and J. Machado and P. Novais},

editor = {Martinez de Pison F. J. Perez Garcia H. Garcia Bringas P.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139072493&doi=10.1007%2f978-3-031-15471-3_34&partnerID=40&md5=e9ed3982c3388f81adf3383436500587},

doi = {10.1007/978-3-031-15471-3_34},

issn = {03029743},

year  = {2022},

date = {2022-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {13469 LNAI},

pages = {393-404},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Currently, smart cities are a hot topic and their tendency will be to optimize resources and promote efficient strategies for the preservation of the planet as well as to increase the quality of life of its inhabitants. In this sense, this research presents an initial component of investigation about Big Data Platforms for Smart Cities in order to be implemented in integrated and innovative solutions for development in urban centers. For this, a survey was carried out on “Big Data Platforms”, “Data Science Platforms”, “Security & Privacy” and “Resources Management”. The extraction of the results of this research was done through the SCOPUS repository in articles from the last 5 years to conclude what has been done so far and what will be the trends in the coming years, define proposals for possible solutions for smart cities and identify the right technologies for the design of a smart city architecture. © 2022, Springer Nature Switzerland AG.},

note = {cited By 1; Conference of 17th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2022 ; Conference Date: 5 September 2022 Through 7 September 2022; Conference Code:283099},

keywords = {Big data, Big data platform; Data platform; Data resources; Efficient strategy; Hot topics; Platform management; Privacy; Quality of life; Resource management; Security, Data privacy; Data Science; Information management; Internet of things; Smart city; Surveys},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lima2015679,

title = {Big data for stock market by means of mining techniques},

author = {L. Lima and F. Portela and M. F. Santos and A. Abelha and J. Machado},

editor = {Correia A. M. Rocha A. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926313922&doi=10.1007%2f978-3-319-16486-1_67&partnerID=40&md5=3c0f77005ba2f642b35e948510de3797},

doi = {10.1007/978-3-319-16486-1_67},

issn = {21945357},

year  = {2015},

date = {2015-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {353},

pages = {679-688},

publisher = {Springer Verlag},

abstract = {Predict and prevent future events are the major advantages to any company. Big Data comes up with huge power, not only by the ability of processes large amounts and variety of data at high velocity, but also by the capability to create value to organizations. This paper presents an approach to a Big Data based decision making in the stock market context. The correlation between news articles and stock variations it is already proved but it can be enriched with other indicators. In this use case they were collected news articles from three different web sites and the stock history from the New York Stock Exchange. In order to proceed to data mining classification algorithms the articles were labeled by their sentiment, the direct relation to a specific company and geographic market influence. With the proposed model it is possible identify the patterns between this indicators and predict stock price variations with accuracies of 100 percent. Moreover the model shown that the stock market could be sensitive to news with generic topics, such as government and society but they can also depend on the geographic cover. © Springer International Publishing Switzerland 2015.},

note = {cited By 6; Conference of World Conference on Information Systems and Technologies, WorldCIST 2015 ; Conference Date: 1 April 2015 Through 3 April 2015; Conference Code:115919},

keywords = {Big data, Commerce; Data mining; Decision making; Electronic document exchange; Finance; Financial markets; Forecasting; Information systems, Data mining classification algorithms; Geographic market; Large amounts; Mining techniques; New York Stock Exchange; News articles; Stock predictions; Text mining},

pubstate = {published},

tppubtype = {inproceedings}

}