Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Hsu, I-Ching; Lin, Yi-Hong

doi:10.1002/spe.2892

Cited by 7 publications

(6 citation statements)

References 39 publications

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“…(2017) proposed strategies to support LOD for data discoverability. In addition, Hsu and Lin (2020) integrate LOD with machine learning and cloud computing to facilitate data recommendation features. Overall, the research in this section investigates the potential of LOD and semantic technologies to improve data accessibility. Data mining and visualization.…”

Section: Resultsmentioning

confidence: 99%

Towards a taxonomy of research areas in open government data

Mohamad¹,

Sylvester

Campbell‐Meier

2023

OIR

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PurposeThis study aimed to develop a taxonomy of research areas in open government data (OGD) through a bibliometric mapping tool and a qualitative analysis software.Design/methodology/approachIn this study, the authors extracted metadata of 442 documents from a bibliographic database. The authors used a bibliometric mapping tool for familiarization with the literature. After that, the authors used qualitative analysis software to develop taxonomy.FindingsThis paper developed taxonomy of OGD with three research areas: implementation and management, architecture, users and utilization. These research areas are further analyzed into seven topics and twenty-eight subtopics. The present study extends Charalabidis et al. (2016) taxonomy by adding two research topics, namely the adoption factors and barriers of OGD implementations and OGD ecosystems. Also, the authors include artificial intelligence in the taxonomy as an emerging research interest in the literature. The authors suggest four directions for future research: indigenous knowledge in open data, open data at local governments, development of OGD-specific theories and user studies in certain research themes.Practical implicationsEarly career researchers and doctoral students can use the taxonomy to familiarize themselves with the literature. Also, established researchers can use the proposed taxonomy to inform future research. Taxonomy-building procedures in this study are applicable to other fields.Originality/valueThis study developed a novel taxonomy of research areas in OGD. Taxonomy building is significant because there is insufficient taxonomy of research areas in this discipline. Also, conceptual knowledge through taxonomy creation is a basis for theorizing and theory-building for future studies.

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Section: Resultsmentioning

confidence: 99%

Towards a taxonomy of research areas in open government data

Mohamad¹,

Sylvester

Campbell‐Meier

2023

OIR

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“…Open data lakes: We classify research on open data lakes into (1) data platforms, [34][35][36][37] (2) government open data portals, 33,38,39 (3) systems operating on open data lakes, [40][41][42][43] and ( 4) custom open data lakes. 16 First, research on data platforms is mainly to improve or expand CKAN, and there are also studies on new data platforms that focus on sustainability and improve user consumption environments, such as SuDaMa.…”

Section: Related Workmentioning

confidence: 99%

Demeter: An automatic framework for data migration in open data lakes

Kim,

Han,

Son

et al. 2023

Softw Pract Exp

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An open data lake stores various forms and types of open data, and there is an increasing demand to manage raw data in tables rather than files for efficient data exploration and analysis. In this paper, we investigate the data management of open data lakes and recognize the limitations of table migration and related problems. First, open data lakes have problems of preprocessing complexity, scale limitation, and platform dependency due to the traditional data management method and open data characteristics. Second, existing studies for table migration have problems of lack of scalability, migration incompleteness, and scale limitation. In this work, we present a novel automation framework, called Demeter, which solves three problems inherent in open data lakes by expanding automation. Specifically, it supports automating catalog collection and preprocessing tasks to solve preprocessing complexity and scale limitation. It also supports platform universality for representative data platforms through the automation of catalog analysis and detailed processing logic. Demeter then solves three problems in table migration by adopting Airbyte, an open‐source ELT platform, and by enhancing automation capability with the Airbyte manager. We verify that Demeter resolves all the problems above through extensive experiments and proves its scalability and universality. In addition, significantly outperforms CKAN by Demeter up to 508.5% in automation performance, up to 207.28% in processing time, and up to 917.17% in migration performance. These results indicate that Demeter is an excellent automation framework that increases the utilization of large‐scale open data and supports reliable Internet‐scale migration.

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“…Experiences on LOD practices in some countries are shared [45]. Research on the semantic web involves integrating machine learning and cloud computing for data recommendations on OGD platforms [46].…”

Section: Linked Open Data (Lod) and Semantic Web -mentioning

confidence: 99%

Towards A Taxonomy of Emerging Topics in Open Government Data: A Bibliometric Mapping Approach

Mohamad¹,

Sylvester²,

Campbell‐Meier³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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The purpose of this paper is to capture the emerging research topics in Open Government Data (OGD) through a bibliometric mapping approach. Previous OGD research has covered the evolution of the discipline with the application of bibliometric mapping tools. However, none of these studies have extended the bibliometric mapping approach for taxonomy building. Realizing this potential, we used a bibliometric tool to perform keyword analysis as a foundation for taxonomy construction. A set of keyword clusters was constructed, and qualitative analysis software was used for taxonomy creation. Emerging topics were identified in a taxonomy form. This study contributes towards the development of an OGD taxonomy. This study contributes to the procedural realignment of a past study by incorporating taxonomy building elements for taxonomy creation. These contributions are significant because there is insufficient taxonomy research in the OGD discipline. The taxonomy building procedures extended in this study are applicable to other fields.

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Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Cited by 7 publications

References 39 publications

Towards a taxonomy of research areas in open government data

Towards a taxonomy of research areas in open government data

Demeter: An automatic framework for data migration in open data lakes

Towards A Taxonomy of Emerging Topics in Open Government Data: A Bibliometric Mapping Approach

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