使用信任映射解决数据冲突
Data Conflict Resolution Using Trust Mappings
摘要：
在科学或社区数据库等大规模协作项目中，用户通常需要同意或不同意单个数据项的内容。另一方面，信任关系通常存在于用户之间，允许他们默认接受或拒绝其他用户的信念。然而，随着这些信任关系变得复杂，定义和计算冲突信息的一致快照变得困难。以前解决相关问题（更新协调问题）的方法依赖于处理更新的顺序，因此不能保证全局一致的快照。本文提出了社区数据库中自动冲突解决问题的第一个原则性解决方案。我们的语义是基于一个逻辑程序的所有稳定模型的某些元组。虽然稳定的模型一般是众所周知的是很难，即使是非常简单的逻辑程序，我们表明，冲突解决问题承认PTIME解决方案。据我们所知，我们是第一个允许以原则性方式解决冲突的PTIME算法。我们进一步讨论扩展到负信念，并证明这些扩展是很难的。这项工作是在华盛顿大学的BeliefDB项目的背景下完成的，该项目侧重于社区数据库中冲突的有效管理。
英文摘要：
In massively collaborative projects such as scientific or community databases, users often need to agree or disagree on the content of individual data items. On the other hand, trust relationships often exist between users, allowing them to accept or reject other users' beliefs by default. As those trust relationships become complex, however, it becomes difficult to define and compute a consistent snapshot of the conflicting information. Previous solutions to a related problem, the update reconciliation problem, are dependent on the order in which the updates are processed and, therefore, do not guarantee a globally consistent snapshot. This paper proposes the first principled solution to the automatic conflict resolution problem in a community database. Our semantics is based on the certain tuples of all stable models of a logic program. While evaluating stable models in general is well known to be hard, even for very simple logic programs, we show that the conflict resolution problem admits a PTIME solution. To the best of our knowledge, ours is the first PTIME algorithm that allows conflict resolution in a principled way. We further discuss extensions to negative beliefs and prove that some of these extensions are hard. This work is done in the context of the BeliefDB project at the University of Washington, which focuses on the efficient management of conflicts in community databases.
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