Query Processing on Probabilistic Data: A Survey
Query Processing on Probabilistic Data: A Survey
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DOI：
10.1561/1900000052
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Guy Van den Broeck;Dan Suciu
中科院分区：
文献类型：
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作者：
Guy Van den Broeck;Dan Suciu
概率数据是由于需要对大型数据库中的不确定性进行建模而产生的。在过去二十年左右的时间里，数据库社区和人工智能社区都研究了概率关系数据的各个方面。概率数据的查询处理：调查介绍了文献中开发的主要方法，协调了两个研究社区并行开发的概念。首先广泛讨论主要的概率数据模型及其关系，然后简要概述模型计数及其与概率数据的关系。该专着继续讨论提升概率推理，这是数据库和人工智能社区并行开发的一套用于概率查询评估的技术。然后，它总结了查询编译，提出了一些理论结果，强调了各种查询评估技术对概率数据的局限性。最后简要讨论了一些基于该技术的流行概率数据集、系统和应用程序。
Probabilistic data is motivated by the need to model uncertainty in large databases. Over the last twenty years or so, both the Database community and the AI community have studied various aspects of probabilistic relational data. Query Processing on Probabilistic Data: A Survey presents the main approaches developed in the literature, reconciling concepts developed in parallel by the two research communities. It starts with an extensive discussion of the main probabilistic data models and their relationships, followed by a brief overview of model counting and its relationship to probabilistic data. The monograph proceeds to discuss lifted probabilistic inference, a suite of techniques developed in parallel by the Database and AI communities for probabilistic query evaluation. It then provides a summary of query compilation, presenting some theoretical results highlighting limitations of various query evaluation techniques on probabilistic data. It ends with a brief discussion of some popular probabilistic data sets, systems, and applications that build on this technology.