3 datasets found
  1. OpenResume: Advancing Career Trajectory Modeling with Anonymized and...

    • zenodo.org
    Updated Feb 24, 2025
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    Michiharu Yamashita; Thanh Tran; Dongwon Lee; Michiharu Yamashita; Thanh Tran; Dongwon Lee (2025). OpenResume: Advancing Career Trajectory Modeling with Anonymized and Synthetic Resume Datasets [Dataset]. http://doi.org/10.1109/bigdata62323.2024.10825519
    Explore at:
    Dataset updated
    Feb 24, 2025
    Dataset provided by
    Institute of Electrical and Electronics Engineershttp://www.ieee.ro/
    Authors
    Michiharu Yamashita; Thanh Tran; Dongwon Lee; Michiharu Yamashita; Thanh Tran; Dongwon Lee
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Overview

    The OpenResume dataset is designed for researchers and practitioners in career trajectory modeling and job-domain machine learning, as described in the IEEE BigData 2024 paper. It includes both anonymized realistic resumes and synthetically generated resumes, offering a comprehensive resource for developing and benchmarking predictive models across a variety of career-related tasks. By employing anonymization and differential privacy techniques, OpenResume ensures that research can be conducted while maintaining privacy. The dataset is available in this repository. Please see the paper for more details: 10.1109/BigData62323.2024.10825519

    If you find this paper useful in your research or use this dataset in any publications, projects, tools, or other forms, please cite:

    @inproceedings{yamashita2024openresume,

    title={{OpenResume: Advancing Career Trajectory Modeling with Anonymized and Synthetic Resume Datasets}},

    author={Yamashita, Michiharu and Tran, Thanh and Lee, Dongwon},

    booktitle={2024 IEEE International Conference on Big Data (BigData)},

    year={2024},

    organization={IEEE}

    }

    @inproceedings{yamashita2023james,

    title={{JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning}},

    author={Yamashita, Michiharu and Shen, Jia Tracy and Tran, Thanh and Ekhtiari, Hamoon and Lee, Dongwon},

    booktitle={2023 IEEE International Conference on Data Science and Advanced Analytics (DSAA)},

    year={2023},

    organization={IEEE}

    }

    Data Contents and Organization

    The dataset consists of two primary components:

    • Realistic Data: An anonymized dataset utilizing differential privacy techniques.
    • Synthetic Data: A synthetic dataset generated from real-world job transition graphs.

    The dataset includes the following features:

    • Anonymized User Identifiers: Unique IDs for anonymized users.
    • Anonymized Company Identifiers: Unique IDs for anonymized companies.
    • Normalized Job Titles: Job titles standardized into the ESCO taxonomy.
    • Job Durations: Start and end dates, either anonymized or synthetically generated with differential privacy.

    Detailed information on how the OpenResume dataset is constructed can be found in our paper.

    Dataset Extension

    Job titles in the OpenResume dataset are normalized into the ESCO occupation taxonomy. You can easily integrate the OpenResume dataset with ESCO job and skill databases to perform additional downstream tasks.

    • Applicable Tasks:
      • Next Job Title Prediction (Career Path Prediction)
      • Next Company Prediction (Career Path Prediction)
      • Turnover Prediction
      • Link Prediction
      • Required Skill Prediction (with ESCO dataset integration)
      • Existing Skill Prediction (with ESCO dataset integration)
      • Job Description Classification (with ESCO dataset integration)
      • Job Title Classification (with ESCO dataset integration)
      • Text Feature-Based Model Development (with ESCO dataset integration)
      • LLM Development for Resume-Related Tasks (with ESCO dataset integration)
      • And more!

    Intended Uses

    The primary objective of OpenResume is to provide an open resource for:

    1. Evaluating and comparing newly developed career models in a standardized manner.
    2. Fostering AI advancements in career trajectory modeling and job market analytics.

    With its manageable size, the dataset allows for quick validation of model performance, accelerating innovation in the field. It is particularly useful for researchers who face barriers in accessing proprietary datasets.

    While OpenResume is an excellent tool for research and model development, it is not intended for commercial, real-world applications. Companies and job platforms are expected to rely on proprietary data for their operational systems. By excluding sensitive attributes such as race and gender, OpenResume minimizes the risk of bias propagation during model training.

    Our goal is to support transparent, open research by providing this dataset. We encourage responsible use to ensure fairness and integrity in research, particularly in the context of ethical AI practices.

    Ethical and Responsible Use

    The OpenResume dataset was developed with a strong emphasis on privacy and ethical considerations. Personal identifiers and company names have been anonymized, and differential privacy techniques have been applied to protect individual privacy. We expect all users to adhere to ethical research practices and respect the privacy of data subjects.

    Related Work

    JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning
    Michiharu Yamashita, Jia Tracy Shen, Thanh Tran, Hamoon Ekhtiari, and Dongwon Lee
    IEEE Int'l Conf. on Data Science and Advanced Analytics (DSAA), 2023

    Fake Resume Attacks: Data Poisoning on Online Job Platforms
    Michiharu Yamashita, Thanh Tran, and Dongwon Lee
    The ACM Web Conference 2024 (WWW), 2024

  2. Cloud Domain Name System (DNS) Market Report | Global Forecast From 2025 To...

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). Cloud Domain Name System (DNS) Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-cloud-domain-name-system-dns-market
    Explore at:
    pdf, pptx, csvAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Cloud Domain Name System (DNS) Market Outlook



    The global Cloud Domain Name System (DNS) market size was valued at USD 1.9 billion in 2023 and is projected to reach USD 6.8 billion by 2032, growing at a CAGR of 15.2% from 2024 to 2032. The market growth is primarily driven by the increasing adoption of cloud-based services and the rising need for advanced network management and security solutions. The shift towards digital transformation in various industries is also playing a pivotal role in the expansion of the Cloud DNS market, as businesses seek efficient ways to manage and optimize their online presence.



    One of the significant growth factors for the Cloud DNS market is the burgeoning demand for scalable and flexible cloud solutions. With the rapid expansion of internet services and the proliferation of smart devices, there is an increasing need for robust DNS solutions that can handle high volumes of traffic without compromising on performance. The scalability offered by cloud-based DNS solutions ensures that businesses can efficiently manage traffic spikes and maintain seamless online services, which is crucial in today's digital economy.



    Another key driver is the growing emphasis on cybersecurity. As cyber threats become more sophisticated, organizations are investing heavily in secure DNS solutions to protect their networks from attacks such as DDoS, phishing, and cache poisoning. Cloud DNS providers offer enhanced security features, including real-time threat detection and mitigation, which are essential for safeguarding sensitive data and maintaining business continuity. This heightened focus on security is propelling the adoption of Cloud DNS solutions across various sectors.



    The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into Cloud DNS solutions is also contributing to market growth. These technologies enable predictive analytics and automated management, allowing organizations to optimize their DNS infrastructure and improve overall network performance. AI and ML can also enhance threat detection capabilities, providing an additional layer of security for cloud-based DNS services. The continuous innovation in this space is expected to drive further adoption and expansion of the Cloud DNS market.



    In this context, DNS Security Service has emerged as a crucial component in the Cloud DNS landscape. As businesses increasingly rely on cloud-based DNS solutions, the need for robust security measures to protect against cyber threats becomes paramount. DNS Security Service provides an additional layer of protection, safeguarding DNS infrastructure from attacks such as DNS spoofing, DDoS, and cache poisoning. By integrating advanced security protocols and real-time threat detection capabilities, DNS Security Service ensures the integrity and availability of DNS services, which is vital for maintaining uninterrupted online operations and protecting sensitive data. This focus on security is driving the adoption of DNS Security Service across various industries, as organizations seek to enhance their cybersecurity posture and ensure business continuity.



    Regionally, North America is expected to dominate the Cloud DNS market during the forecast period, owing to the presence of major technology companies and early adoption of advanced cloud solutions. The region's robust IT infrastructure and high awareness of cybersecurity issues contribute to its leading position. However, Asia Pacific is anticipated to witness the highest growth rate, driven by the rapid digital transformation in emerging economies such as China and India, increasing internet penetration, and the growing adoption of cloud services by small and medium enterprises (SMEs).



    Deployment Type Analysis



    The Cloud DNS market can be segmented based on deployment type into Public Cloud, Private Cloud, and Hybrid Cloud. Each of these deployment models offers unique benefits and caters to different business needs, driving the overall growth of the market. Public Cloud DNS services are widely adopted due to their cost-effectiveness and ease of deployment. These services are hosted by third-party providers and are accessible over the internet, making them an attractive option for businesses seeking scalable solutions without significant capital expenditure. Public Cloud DNS solutions are particularly popular among SMEs for their affordability and flexibility.



    Private Cloud DNS, on t

  3. D

    Domain Name Security Service Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated May 24, 2025
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    Data Insights Market (2025). Domain Name Security Service Report [Dataset]. https://www.datainsightsmarket.com/reports/domain-name-security-service-495539
    Explore at:
    ppt, doc, pdfAvailable download formats
    Dataset updated
    May 24, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The Domain Name System Security Extension (DNSSEC) market, valued at $1086 million in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 9.1% from 2025 to 2033. This growth is fueled by the increasing sophistication of cyberattacks targeting domain names, coupled with rising regulatory mandates for enhanced online security. Businesses across various sectors, including finance, healthcare, and e-commerce, are prioritizing DNS security to protect their brand reputation, customer data, and operational continuity. The increasing adoption of cloud-based solutions, the proliferation of IoT devices creating a larger attack surface, and the growing awareness of DNS-based attacks like DNS tunneling and cache poisoning are major drivers. Competition is fierce, with established players like Cloudflare, Akamai, and GoDaddy vying for market share alongside emerging players offering specialized solutions. The market is segmented by deployment type (cloud, on-premises), security features (DNSSEC, DDoS protection, traffic filtering), and end-user industry (government, enterprise, etc.), with cloud-based solutions experiencing rapid adoption due to scalability and cost-effectiveness. While the market faces restraints such as the complexity of implementation and the need for skilled professionals, the overall trajectory remains positive, driven by a growing understanding of the critical role DNS security plays in overall cybersecurity posture. The forecast period of 2025-2033 will likely see continued market consolidation as major players acquire smaller companies to strengthen their product offerings and expand their reach. Innovation in areas such as AI-powered threat detection and automated security response systems will further shape the market landscape. Regional variations in adoption rates are anticipated, with North America and Europe likely to maintain dominant positions due to advanced technological infrastructure and stringent data privacy regulations. However, emerging economies in Asia-Pacific and Latin America are also expected to witness significant growth, driven by increasing internet penetration and rising cybersecurity awareness. This growth will be further fueled by the increasing adoption of multi-factor authentication (MFA) and other security measures integrated within the DNS infrastructure, bolstering the overall security of online services and applications.

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Click to copy link
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Michiharu Yamashita; Thanh Tran; Dongwon Lee; Michiharu Yamashita; Thanh Tran; Dongwon Lee (2025). OpenResume: Advancing Career Trajectory Modeling with Anonymized and Synthetic Resume Datasets [Dataset]. http://doi.org/10.1109/bigdata62323.2024.10825519
Organization logo

OpenResume: Advancing Career Trajectory Modeling with Anonymized and Synthetic Resume Datasets

Explore at:
15 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Feb 24, 2025
Dataset provided by
Institute of Electrical and Electronics Engineershttp://www.ieee.ro/
Authors
Michiharu Yamashita; Thanh Tran; Dongwon Lee; Michiharu Yamashita; Thanh Tran; Dongwon Lee
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Overview

The OpenResume dataset is designed for researchers and practitioners in career trajectory modeling and job-domain machine learning, as described in the IEEE BigData 2024 paper. It includes both anonymized realistic resumes and synthetically generated resumes, offering a comprehensive resource for developing and benchmarking predictive models across a variety of career-related tasks. By employing anonymization and differential privacy techniques, OpenResume ensures that research can be conducted while maintaining privacy. The dataset is available in this repository. Please see the paper for more details: 10.1109/BigData62323.2024.10825519

If you find this paper useful in your research or use this dataset in any publications, projects, tools, or other forms, please cite:

@inproceedings{yamashita2024openresume,

title={{OpenResume: Advancing Career Trajectory Modeling with Anonymized and Synthetic Resume Datasets}},

author={Yamashita, Michiharu and Tran, Thanh and Lee, Dongwon},

booktitle={2024 IEEE International Conference on Big Data (BigData)},

year={2024},

organization={IEEE}

}

@inproceedings{yamashita2023james,

title={{JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning}},

author={Yamashita, Michiharu and Shen, Jia Tracy and Tran, Thanh and Ekhtiari, Hamoon and Lee, Dongwon},

booktitle={2023 IEEE International Conference on Data Science and Advanced Analytics (DSAA)},

year={2023},

organization={IEEE}

}

Data Contents and Organization

The dataset consists of two primary components:

  • Realistic Data: An anonymized dataset utilizing differential privacy techniques.
  • Synthetic Data: A synthetic dataset generated from real-world job transition graphs.

The dataset includes the following features:

  • Anonymized User Identifiers: Unique IDs for anonymized users.
  • Anonymized Company Identifiers: Unique IDs for anonymized companies.
  • Normalized Job Titles: Job titles standardized into the ESCO taxonomy.
  • Job Durations: Start and end dates, either anonymized or synthetically generated with differential privacy.

Detailed information on how the OpenResume dataset is constructed can be found in our paper.

Dataset Extension

Job titles in the OpenResume dataset are normalized into the ESCO occupation taxonomy. You can easily integrate the OpenResume dataset with ESCO job and skill databases to perform additional downstream tasks.

  • Applicable Tasks:
    • Next Job Title Prediction (Career Path Prediction)
    • Next Company Prediction (Career Path Prediction)
    • Turnover Prediction
    • Link Prediction
    • Required Skill Prediction (with ESCO dataset integration)
    • Existing Skill Prediction (with ESCO dataset integration)
    • Job Description Classification (with ESCO dataset integration)
    • Job Title Classification (with ESCO dataset integration)
    • Text Feature-Based Model Development (with ESCO dataset integration)
    • LLM Development for Resume-Related Tasks (with ESCO dataset integration)
    • And more!

Intended Uses

The primary objective of OpenResume is to provide an open resource for:

  1. Evaluating and comparing newly developed career models in a standardized manner.
  2. Fostering AI advancements in career trajectory modeling and job market analytics.

With its manageable size, the dataset allows for quick validation of model performance, accelerating innovation in the field. It is particularly useful for researchers who face barriers in accessing proprietary datasets.

While OpenResume is an excellent tool for research and model development, it is not intended for commercial, real-world applications. Companies and job platforms are expected to rely on proprietary data for their operational systems. By excluding sensitive attributes such as race and gender, OpenResume minimizes the risk of bias propagation during model training.

Our goal is to support transparent, open research by providing this dataset. We encourage responsible use to ensure fairness and integrity in research, particularly in the context of ethical AI practices.

Ethical and Responsible Use

The OpenResume dataset was developed with a strong emphasis on privacy and ethical considerations. Personal identifiers and company names have been anonymized, and differential privacy techniques have been applied to protect individual privacy. We expect all users to adhere to ethical research practices and respect the privacy of data subjects.

Related Work

JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning
Michiharu Yamashita, Jia Tracy Shen, Thanh Tran, Hamoon Ekhtiari, and Dongwon Lee
IEEE Int'l Conf. on Data Science and Advanced Analytics (DSAA), 2023

Fake Resume Attacks: Data Poisoning on Online Job Platforms
Michiharu Yamashita, Thanh Tran, and Dongwon Lee
The ACM Web Conference 2024 (WWW), 2024

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