摘要翻译：
在诸如科学或社区数据库之类的大规模协作项目中，用户经常需要对单个数据项的内容达成一致或不一致。另一方面，信任关系经常存在于用户之间，允许他们默认地接受或拒绝其他用户的信任。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。以前对相关问题（更新协调问题）的解决方案依赖于处理更新的顺序，因此不能保证全局一致的快照。本文首次提出了社区数据库中冲突自动解决问题的原则性解决方案。我们的语义是基于逻辑程序的所有稳定模型的某些元组。虽然评估稳定模型通常是众所周知的困难，即使对于非常简单的逻辑程序，我们证明冲突解决问题允许一个PTIME解。据我们所知，我们的算法是第一个允许以原则性的方式解决冲突的PTIME算法。我们进一步讨论了否定信念的扩展，并证明其中一些扩展是困难的。这项工作是在华盛顿大学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。
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英文标题：
《Data Conflict Resolution Using Trust Mappings》
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作者：
Wolfgang Gatterbauer, Dan Suciu
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Databases        数据库
分类描述：Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
涵盖数据库管理、数据挖掘和数据处理。大致包括ACM学科类E.2、E.5、H.0、H.2和J.1中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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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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PDF链接：
https://arxiv.org/pdf/1012.3320