Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Xiaoou Ding"'
Publikováno v:
大数据, Vol 10, Pp 54-67 (2024)
With the explosive growth of data, the digital economy, where data serves as a crucial element, continues to advance.In the context of data markets, establishing a fair and efficient pricing and trading system becomes paramount.Addressing fairness wi
Externí odkaz:
https://doaj.org/article/305359d9118b49b092c7860fc58928b4
Publikováno v:
大数据, Vol 9, Pp 59-68 (2023)
The advancement of data collection technology has led to a rapid increase in the size of datasets.Due to the big scale and high complexity of the data volume, serious data quality issues arise.Therefore, data cleaning is a necessary and important ste
Externí odkaz:
https://doaj.org/article/df0b1f162a974f80bd6c701d74a75234
Publikováno v:
Intelligent and Converged Networks, Vol 3, Iss 4, Pp 325-339 (2022)
Data cleaning is considered as an effective approach of improving data quality in order to help practitioners and researchers be devoted to downstream analysis and decision-making without worrying about data trustworthiness. This paper provides a sys
Externí odkaz:
https://doaj.org/article/1c73b01184a7427a9425faa56d5144bb
Publikováno v:
Database Systems for Advanced Applications ISBN: 9783031306778
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f6eaaa7d1a98765b6d38ce52835af532
https://doi.org/10.1007/978-3-031-30678-5_54
https://doi.org/10.1007/978-3-031-30678-5_54
Publikováno v:
Database Systems for Advanced Applications ISBN: 9783031306365
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e4d1d3abe713fd9b03dc81e61717d1b
https://doi.org/10.1007/978-3-031-30637-2_17
https://doi.org/10.1007/978-3-031-30637-2_17
Publikováno v:
大数据, Vol 5, Pp 2019047-1 (2019)
Industrial big data has become an important strategic resource for the transformation and upgrading of China’s manufacturing industry,and industrial big data analysis is attracting more and more attention.As an important data form of industrial big
Externí odkaz:
https://doaj.org/article/73865382d240499c8b6c1202174f0b6b
Publikováno v:
Proceedings of the VLDB Endowment. 12:1786-1789
The great amount of time series generated by machines has enormous value in intelligent industry. Knowledge can be discovered from high-quality time series, and used for production optimization and anomaly detection in industry. However, the original
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cf35b5aac450a64a489ee9ead47d1747
https://doi.org/10.14778/3352063.3352066
https://doi.org/10.14778/3352063.3352066
Publikováno v:
ICDE
With the growth of data from various sources, data quality is faced with multiple problems. In this paper, we study the multiple data cleaning on incompleteness and inconsistency with currency reasoning and determination. We introduce a 4-step method
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dfae1933e65554fb90d5baf133138abe
https://doi.org/10.1109/icde51399.2021.00243
https://doi.org/10.1109/icde51399.2021.00243
Publikováno v:
Knowledge-Based Systems. 233:107548
The explosive growth of time series captured by sensors in industrial pipelines gives rise to the flourish of intelligent industry. Exploiting the value of these time series is conductive to workload balancing and production optimization. Unfortunate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eb1efef68e57c2c7669be2cc07287f5e
https://doi.org/10.1016/j.knosys.2021.107548
https://doi.org/10.1016/j.knosys.2021.107548
Publikováno v:
Tsinghua Science and Technology. 22:227-242
Data quality is an important aspect in data application and management, and currency is one of the major dimensions influencing its quality. In real applications, datasets timestamps are often incomplete and unavailable, or even absent. With the incr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::408fb166d4c909c7eca2059a49a6e212
https://doi.org/10.23919/tst.2017.7914196
https://doi.org/10.23919/tst.2017.7914196