A data integration approach for smart cities: The case of natal

Souza, Arthur; Pereira, Jorge; Oliveira, Juliana; Trindade, Claudio Lovaglio Cançado; Cavalcante, Everton; Cacho, Nélio; Batista, Thaís; Lopes, Frederico

doi:10.1109/isc2.2017.8090820

Cited by 13 publications

(9 citation statements)

References 8 publications

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“…In this survey, different cohort harmonization techniques were highlighted, which will help in healthcare applications, such as ML, DL, and Ontology techniques. In addition, in an urban town, so many issues related to basic needs was mentioned by Souza et al [55]; the objective of that study was to make the urban town into a smart urban town. Data are generated by different departments in JSON, string, and maps.…”

Section: How Does the Data Harmonization Resolve The Issues Of Heterogeneity?mentioning

confidence: 99%

“…Domain Contributions [43] Oil and Gas High performance measure [44] Agriculture High performance, high availability, and high scalability, using the latest techniques [45] General-Purpose Data generation, storing, fetching, analysis, visualization, and decision-making [46] Banking Helps in auditing the multisource data [47] Healthcare Facilitate for navigation of HL7 FHIR core resources [48] General-Purpose Delivering automatic services to interoperable system [49] Healthcare Helps in developing an automatic system for disordered patient [50] Education To motivate researchers and academicians about the latest techniques [10] Healthcare Useful for decisions of scientific, clinical, and administrative work [51] Education Facilitate in online learning, storage, processing, and academic activities [52] General-Purpose Recommendation system, opinion mining, and parallelism can be targeted [53] Oil and Gas Helpful for decision-makers during exploration, drilling, and production [54] General-Purpose It will facilitate for fetching data and performance measure [65] Healthcare Helpful for disease prevention, tracking, and policy making [11] Healthcare Helps in boosting statistical power of sustainable and robust data [55] Infrastructure Geographic based smart city for aggregation, visualization, and analysis [56] Healthcare Helps in predicting the clinical codes of patient stays…”

Section: Study Referencementioning

confidence: 99%

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Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

et al. 2021

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As data size increases drastically, its variety also increases. Investigating such heterogeneous data is one of the most challenging tasks in information management and data analytics. The heterogeneity and decentralization of data sources affect data visualization and prediction, thereby influencing analytical results accordingly. Data harmonization (DH) corresponds to a field that unifies the representation of such a disparate nature of data. Over the years, multiple solutions have been developed to minimize the heterogeneity aspects and disparity in formats of big-data types. In this study, a systematic review of the literature was conducted to assess the state-of-the-art DH techniques. This study aimed to understand the issues faced due to heterogeneity, the need for DH and the techniques that deal with substantial heterogeneous textual datasets. The process produced 1355 articles, but among them, only 70 articles were found to be relevant through inclusion and exclusion criteria methods. The result shows that the heterogeneity of structured, semi-structured, and unstructured (SSU) data can be managed by using DH and its core techniques, such as text preprocessing, Natural Language Preprocessing (NLP), machine learning (ML), and deep learning (DL). These techniques are applied to many real-world applications centered on the information-retrieval domain. Several assessment criteria were implemented to measure the efficiency of these techniques, such as precision, recall, F-1, accuracy, and time. A detailed explanation of each research question, common techniques, and performance measures is also discussed. Lastly, we present readers with a detailed discussion of the existing work, contributions, and managerial and academic implications, along with the conclusion, limitations, and future research directions.

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Section: How Does the Data Harmonization Resolve The Issues Of Heterogeneity?mentioning

confidence: 99%

Section: Study Referencementioning

confidence: 99%

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

et al. 2021

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“…Diversos autores também relatam que tiveram problemas com banco de dados em relação as diferentes semânticas de dados [Chakraborty et al 2017, Shivaprabhu et al 2017, Souza et al 2017. Outras dificuldades enfrentadas estão relacionadas a compreensão do domínio de como geometrias GIS podem ser mapeadas e como elas podem ser exploradas em outros domínios (como gráficos), a abstração teórica de como diferentes camadas de pontos, linhas, geometrias podem ser mapeadas para multigramas, entre outros.…”

Section: Desafios (Qp3)unclassified

“…Isso ocorre devidoà grande heterogeneidade de stakeholders envolvidos nas soluções para cidades inteligentes, tais como cidadãos, governo, indústria, academia etc., que geram e consomem dados e informações relacionadas a seus próprios interesses. Outro fator que contribui para essa diversidadeé a ausência de políticas relacionadas ao compartilhamento e disponibilização desses dados, que geralmente possuem formatos definidos de acordo com a facilidade ou a capacidade técnica dos seus respectivos produtores [Souza et al 2017]. Outro aspecto importante diz respeitoà natureza desses dados, que são diversos, podem não estar devidamente estruturados e conter ruídos ou imprecisões que afetam de forma negativa a sua utilização por aplicações e usuários finais.…”

Section: Introductionunclassified

Integração, Relacionamento e Representação de Dados em Cidades Inteligentes: Uma Revisão de Literatura

Silva¹,

Lima²,

Cacho³

et al. 2018

Anais Do Workshop Brasileiro De Cidades Inteligentes (WBCI)

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A notable characteristic of smart cities is the increase in the amount of available data generated by several devices and computational systems, thus augmenting the challenges related to the development of software that involves the integration of larges volumes of data. In this context, this paper presents a literature review aimed to identify the main strategies used in the development of solutions for data integration, relationship, and representation in smart cities. This study systematically selected and analyzed eleven studies published from 2015 to 2017. The achieved results reveal gaps regarding solutions for the continuous integration of heterogeneous data sources towards supporting application development and decision-making.

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“…Therefore, in recent years, several applications have been developed adopting the FIWARE technology in different application domains including information management [13,14], Cloud resource management [15], smart mobility [16][17][18][19], smart parking [20], smart agriculture [21,22], energy [23], security [24][25][26][27], human-computer interaction [28], earthquake prevention [29], and healthcare [30].…”

Section: Introductionmentioning

confidence: 99%

How to Develop IoT Cloud e-Health Systems Based on FIWARE: A Lesson Learnt

Celesti

Fazio

Márquez

et al. 2019

JSAN

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Nowadays, the penetration of sensors and actuators in different application fields is revolutionizing all aspects of our daily life. One of the major sectors that is taking advantage of such cutting-edge cheap smart devices is healthcare. In this context, Remote Patient Monitoring (RPM) at home represents a tempting opportunity for hospitals to reduce clinical costs and to improve the quality of life of both patients and their families. It allows patients to be monitored remotely by means networks of Internet of Things (IoT) medical devices equipped with sensors and actuators that collect healthcare data from patients and send them to a Cloud-based Hospital Information System (HIS) for processing. Up to now, many different proprietary software systems have been developed as stand-along expensive solutions, presenting interoperability, extensibility, and scalability issues. In recent years, the European Commission (EC) has promoted the wide adoption of FIWARE technology, launching 16 Industrial Accelerators focusing on different application fields. One of these, i.e., FICHe, is specialized in healthcare, providing the guidelines on how to develop eHealth systems. This paper focuses on how to compose new cutting-edge IoT and Cloud-based Cyber Physical Health Sytem (CPHS) services and applications interconnected with remote medical sensors and actuators using FIWARE technology in the context envisioned by FICHe. In particular, we discuss the design and development of an RPM system implemented through the collaboration between the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) “Bonino Pulejo” (i.e., a clinical and research healthcare centre specialized in the treatment of neuro lesions), University of Messina, IBM Research, Telefónica, and the University of the Western Cape in South Africa. The description of our best practice provides a model and guidelines for the development of lightweight and low cost RPM services for rural and isolated areas, with the expectation of expanding healthcare to the developing world and in general allows us to outline how to deal with the real adoption of the FIWARE technology in an e-health project.

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A data integration approach for smart cities: The case of natal

Cited by 13 publications

References 8 publications

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

Integração, Relacionamento e Representação de Dados em Cidades Inteligentes: Uma Revisão de Literatura

How to Develop IoT Cloud e-Health Systems Based on FIWARE: A Lesson Learnt

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