unstable-angina
发表于 2025-3-23 11:27:14
Conference proceedings 2019d in this volume were carefully reviewed and selected from 15 submissions. They deal with novel ideas and solutions related to the problems of exploring, assessing, monitoring, improving, and maintaining the quality of data and trust for Big Data. .
CRP743
发表于 2025-3-23 17:01:18
0302-9743 s presented in this volume were carefully reviewed and selected from 15 submissions. They deal with novel ideas and solutions related to the problems of exploring, assessing, monitoring, improving, and maintaining the quality of data and trust for Big Data. .978-3-030-19142-9978-3-030-19143-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
STING
发表于 2025-3-23 20:41:52
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Hemiplegia
发表于 2025-3-23 23:51:44
Comparing Postcolonial Literatureslogical) schemas. In contrast to existing approaches, we provide a fully automatable workflow that also enables regular reassessment. Our implementation allows to measure the quality dimensions correctness, completeness, pertinence, minimality, readability, and normalization.
HARP
发表于 2025-3-24 04:45:13
A Novel Data Quality Metric for Minimality,that fulfills all requirements for a sound data quality metric. In our ongoing research, we are evaluating the benefits of the metric in more detail and investigate its applicability for redundancy detection in data values.
背带
发表于 2025-3-24 09:12:15
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使更活跃
发表于 2025-3-24 11:23:31
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outer-ear
发表于 2025-3-24 18:33:46
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Conquest
发表于 2025-3-24 22:43:04
,CNR: Cross-network Recommendation Embedding User’s Personality,e the recommendation quality. For instance, users who used to watch . movie, may be less likely to receive . movie, leading to redundant type of items and decreasing user’s satisfaction. In this paper, we aim to exploit user’s personality type and incorporate it as a primary and enduring domain-inde
Homocystinuria
发表于 2025-3-25 02:03:07
Transitional Justice: A Likely Storypropose a feature-rich learning method to predict the importance of new emails. Furthermore, we characterize various factors affecting email importance in mailing list discussions. Experiments with publicly available mailing lists show that our prediction model outperforms baselines with large gains