• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information Systems >Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models
【24h】

Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models

机译：在数据模型的按需付费对帐中处理概率完整性约束

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Data models capture the structure and characteristic properties of data entities, e.g., in terms of a database schema or an ontology. They are the backbone of diverse applications, reaching from information integration, through peer-to-peer systems and electronic commerce to social networking. Many of these applications involve models of diverse data sources. Effective utilisation and evolution of data models, therefore, calls for matching techniques that generate correspondences between their elements. Various such matching tools have been developed in the past. Yet, their results are often incomplete or erroneous, and thus need to be reconciled, i.e., validated by an expert. This paper analyses the reconciliation process in the presence of large collections of data models, where the network induced by generated correspondences shall meet consistency expectations in terms of integrity constraints. We specifically focus on how to handle data models that show some internal structure and potentially differ in terms of their assumed level of abstraction. We argue that such a setting calls for a probabilistic model of integrity constraints, for which satisfaction is preferred, but not required. In this work, we present a model for probabilistic constraints that enables reasoning on the correctness of individual correspondences within a network of data models, in order to guide an expert in the validation process. To support pay-as-you-go reconciliation, we also show how to construct a set of high-quality correspondences, even if an expert validates only a subset of all generated correspondences. We demonstrate the efficiency of our techniques for real-world datasets comprising database schemas and ontologies from various application domains. (C) 2019 Elsevier Ltd. All rights reserved.
机译：数据模型例如根据数据库模式或本体来捕获数据实体的结构和特性。它们是各种应用程序的中坚力量，从信息集成到对等系统和电子商务，再到社交网络。其中许多应用程序涉及各种数据源的模型。因此，数据模型的有效利用和发展要求匹配技术能够在其元素之间生成对应关系。过去已经开发了各种这样的匹配工具。然而，它们的结果通常是不完整的或错误的，因此需要对账，即由专家进行验证。本文分析了在存在大量数据模型的情况下的对帐过程，其中由生成的对应关系引起的网络应在完整性约束方面满足一致性期望。我们特别关注于如何处理显示某些内部结构并可能在假定的抽象级别方面有所不同的数据模型。我们认为，这样的设置需要完整性约束的概率模型，对于该模型而言，满意是优选的，但不是必需的。在这项工作中，我们提出了一个概率约束模型，该模型可以对数据模型网络中各个对应关系的正确性进行推理，以指导验证过程的专家。为了支持现收现付对帐，即使专家仅验证所有生成的通信中的一部分，我们也将展示如何构造一组高质量的通信。我们演示了我们的技术对于包含来自不同应用程序领域的数据库模式和本体的真实数据集的效率。 （C）2019 Elsevier Ltd.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号