Crowdsourcing chart digitizer: task design and quality control for making legacy open data machine-readable

Oyama, Satoshi; Baba, Yukino; Ohmukai, Ikki; Dokoshi, Hiroaki; Kashima, Hisashi

doi:10.1007/s41060-016-0025-y

Cited by 4 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, also a number of publications in the ''data exploitation'' phase (96) were classified as ''validation research''. Most ''opinion papers'' are classified on the ''creation phase'' of the open data life-cycle (35). Evaluation papers were mainly focused on ''exploitation'' (36) and ''exploration'' (14) phases.…”

Section: G Combining Facets -The Systematic Map(s)mentioning

confidence: 99%

“…Another example of a semantic web solution proposal is that of a unified framework for converting open legacy data into graphical images and into a machine-readable and reusable format using crowdsourcing [34] to allow elder people to access open data. Meanwhile, [35] has addressed the information needs of people with mobility impairments relating to walking distances and the existence of ramps, steps, seats, etc.…”

Section: Rq3-what Domains Have Been Considered By the Researchers? -mentioning

confidence: 99%

See 1 more Smart Citation

Systematic Mapping of Open Data Studies: Classification and Trends From a Technological Perspective

et al. 2021

View full text Add to dashboard Cite

The objective of this paper is to classify and analyse all research on open data performed in the scientific community from a technological viewpoint, providing a detailed exploration based on six key facets: publication venue, impact, subject, domain, life-cycle phases and type of research. This paper therefore provides a consolidated overview of the open data arena that allows readers to identify well-established topics, trends, and open research issues. Additionally, we provide an extensive qualitative discussion of the most interesting findings to pave the way for future research. Our first identification phase resulted in 893 relevant peer-reviewed articles, published between 2006 and 2019 in a wide variety of venues. Analysis of the results shows that open data research grew slowly from 2006 but increased significantly as from 2009. In 2019, research interest in open data from a technological perspective overall decreased. This fact could indicate that research is beginning to stabilise, i.e., the open data research hype is over, and the research field is reaching maturity. Main findings are (i) increasing effort in researching on Semantic Web technologies as a mechanism to publish and reuse linked open data, (ii) software systems are proposed to solve open data technical problems; and (iii) considering technological aspects of legislation and standardization is needed to widely introduce open data in society. Finally, we provide complementary insights regarding open data innovation projects, with special emphasis on publication (e.g., open data portals) and consumption (e.g., open data as business enabler) of open data.

show abstract

Section: G Combining Facets -The Systematic Map(s)mentioning

confidence: 99%

Section: Rq3-what Domains Have Been Considered By the Researchers? -mentioning

confidence: 99%