Attribute level lineage in uncertain data with dependencies

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• 作者：Liang Wang ; Liwei Wang ; Zhiyong Peng
• 关键词：uncertain data ; attribute level lineage ; dependency
• 刊名：Wuhan University Journal of Natural Sciences
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：21
• 期：5
• 页码：376-386
• 全文大小：464 KB
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Computer Science, general
  Physics
  Life Sciences
  Chinese Library of Science
  
• 出版者：Wuhan University, co-published with Springer
• ISSN：1993-4998
• 卷排序：21

文摘

In uncertain data management, lineages are often used for probability computation of result tuples. However, most of existing works focus on tuple level lineage, which results in imprecise data derivation. Besides, correlations among attributes cannot be captured. In this paper, for base tuples with multiple uncertain attributes, we define attribute level annotation to annotate each attribute. Utilizing these annotations to generate lineages of result tuples can realize more precise derivation. Simultaneously, they can be used for dependency graph construction. Utilizing dependency graph, we can represent not only constraints on schemas but also correlations among attributes. Combining the dependency graph and attribute level lineage, we can correctly compute probabilities of result tuples and precisely derivate data. In experiments, comparing lineage on tuple level and attribute level, it shows that our method has advantages on derivation precision and storage cost.