GraphLMI: A data driven system for exploring labor market information through graph databases

Giabelli, Anna; Malandri, Lorenzo; Mercorio, Fabio; Mezzanzanica, Mario

doi:10.1007/s11042-020-09115-x

Cited by 17 publications

(18 citation statements)

References 37 publications

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“…A graph database can efficiently process large-scale, complex, interconnected, and changeable data, and its computational efficiency is far higher than that of a traditional relational database [ 23 ]. A graph database is scalable and flexible, is suitable for complex relationship management and relationship query reasoning, and reveals implicit relationships between entities [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study

Z¹,

Yao²,

Zou³

et al. 2021

JMIR Mhealth Uhealth

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Background The COVID-19 epidemic is still spreading globally. Contact tracing is a vital strategy in epidemic emergency management; however, traditional contact tracing faces many limitations in practice. The application of digital technology provides an opportunity for local governments to trace the contacts of individuals with COVID-19 more comprehensively, efficiently, and precisely. Objective Our research aimed to provide new solutions to overcome the limitations of traditional contact tracing by introducing the organizational process, technical process, and main achievements of digital contact tracing in Hainan Province. Methods A graph database algorithm, which can efficiently process complex relational networks, was applied in Hainan Province; this algorithm relies on a governmental big data platform to analyze multisource COVID-19 epidemic data and build networks of relationships among high-risk infected individuals, the general population, vehicles, and public places to identify and trace contacts. We summarized the organizational and technical process of digital contact tracing in Hainan Province based on interviews and data analyses. Results An integrated emergency management command system and a multi-agency coordination mechanism were formed during the emergency management of the COVID-19 epidemic in Hainan Province. The collection, storage, analysis, and application of multisource epidemic data were realized based on the government’s big data platform using a centralized model. The graph database algorithm is compatible with this platform and can analyze multisource and heterogeneous big data related to the epidemic. These practices were used to quickly and accurately identify and trace 10,871 contacts among hundreds of thousands of epidemic data records; 378 closest contacts and a number of public places with high risk of infection were identified. A confirmed patient was found after quarantine measures were implemented by all contacts. Conclusions During the emergency management of the COVID-19 epidemic, Hainan Province used a graph database algorithm to trace contacts in a centralized model, which can identify infected individuals and high-risk public places more quickly and accurately. This practice can provide support to government agencies to implement precise, agile, and evidence-based emergency management measures and improve the responsiveness of the public health emergency response system. Strengthening data security, improving tracing accuracy, enabling intelligent data collection, and improving data-sharing mechanisms and technologies are directions for optimizing digital contact tracing.

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Section: Methodsmentioning

confidence: 99%

Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study

Z¹,

Yao²,

Zou³

et al. 2021

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…Besides, customized skill bases were manually tailored for the same purpose. In [23], [69], the authors enriched the ESCO skill base by capturing the relevant n-gram expressions. Then, they tagged skills from job ads of different sectors.…”

Section: ) Skill Identification From Multiple Sectorsmentioning

confidence: 99%

“…Sector specific [15], [21], [22], [39], [40], [62], [63], [66] Soft and Hard skills Sector specific [69], [125] Soft and Hard skills All sectors [33], [47], [64] Soft skills Sector specific Job ads and Resumes [26] Soft and Hard skills All sectors [44] Soft skills sentences All sectors [27] Soft and Hard skills All sectors [25] soft and Hard skills All sectors Job ads and Interviews [34] Soft and Hard skills Sector specific LinkedIn resumes [32] Soft and Hard skills All sectors Curricula development job ads [68] soft and hard skills sector specific [67] Soft and hard skills Sector specific Job ads and University programs [89] Hard skills Sector specific…”

Section: ) Skill Extraction and Job Market Analysismentioning

confidence: 99%

“…Moreover, in [19], the authors studied multi-perspective popularity of skills; they identified popular skills by examining the generated graph of co-occurring skills from different perspectives (salary-wise or company-wise). Furthermore, Giabelli et al [69] generated a graph from online job ads and developed skill bases of different countries to compare and evaluate country-based labor market dynamics for supporting policy and decision-making activities at the European level. The graph encompassed different nodes such as skills, occupations, cities and sectors.…”

Section: Skill Mismatch and Alignmentmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Skill Identification From Online Job Ads

2021

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“…A graph database can efficiently process large-scale, complex, interconnected, and changeable data, and its computational efficiency is far higher than that of a traditional relational database [23]. A graph database is scalable and flexible, is suitable for complex relationship management and relationship query reasoning, and reveals implicit relationships between entities [24].…”

Section: The Graph Database and Its Algorithmmentioning

confidence: 99%

Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study (Preprint)

Z¹,

Yao²,

Zou³

et al. 2020

Preprint

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BACKGROUND The COVID-19 epidemic is still spreading globally. Contact tracing is a vital strategy in epidemic emergency management; however, traditional contact tracing faces many limitations in practice. The application of digital technology provides an opportunity for local governments to trace the contacts of individuals with COVID-19 more comprehensively, efficiently, and precisely. OBJECTIVE Our research aimed to provide new solutions to overcome the limitations of traditional contact tracing by introducing the organizational process, technical process, and main achievements of digital contact tracing in Hainan Province. METHODS A graph database algorithm, which can efficiently process complex relational networks, was applied in Hainan Province; this algorithm relies on a governmental big data platform to analyze multisource COVID-19 epidemic data and build networks of relationships among high-risk infected individuals, the general population, vehicles, and public places to identify and trace contacts. We summarized the organizational and technical process of digital contact tracing in Hainan Province based on interviews and data analyses. RESULTS An integrated emergency management command system and a multi-agency coordination mechanism were formed during the emergency management of the COVID-19 epidemic in Hainan Province. The collection, storage, analysis, and application of multisource epidemic data were realized based on the government’s big data platform using a centralized model. The graph database algorithm is compatible with this platform and can analyze multisource and heterogeneous big data related to the epidemic. These practices were used to quickly and accurately identify and trace 10,871 contacts among hundreds of thousands of epidemic data records; 378 closest contacts and a number of public places with high risk of infection were identified. A confirmed patient was found after quarantine measures were implemented by all contacts. CONCLUSIONS During the emergency management of the COVID-19 epidemic, Hainan Province used a graph database algorithm to trace contacts in a centralized model, which can identify infected individuals and high-risk public places more quickly and accurately. This practice can provide support to government agencies to implement precise, agile, and evidence-based emergency management measures and improve the responsiveness of the public health emergency response system. Strengthening data security, improving tracing accuracy, enabling intelligent data collection, and improving data-sharing mechanisms and technologies are directions for optimizing digital contact tracing.

show abstract

GraphLMI: A data driven system for exploring labor market information through graph databases

Cited by 17 publications

References 37 publications

Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study

Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study

A Survey on Skill Identification From Online Job Ads

Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study (Preprint)

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