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TripleProv: Efficient Processing of Lineage Queries in a Native RDF Store

机译：TripleProv：在本地RDF存储中有效处理沿袭查询

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摘要

Given the heterogeneity of the data one can find on the Linked Data cloud, being able to trace back the provenance of query results is rapidly becoming a must-have feature of RDF systems. While provenance models have been extensively discussed in recent years, little attention has been given to the efficient implementation of provenance-enabled queries inside data stores. This paper introduces TripleProv: a new system extending a native RDF store to efficiently handle such queries. TripleProv implements two different storage models to physically co-locate lineage and instance data, and for each of them implements algorithms for tracing provenance at two granularity levels. In the following, we present the overall architecture of our system, its different lineage storage models, and the various query execution strategies we have implemented to efficiently answer provenance-enabled queries. In addition, we present the results of a comprehensive empirical evaluation of our system over two different datasets and workloads.
机译：鉴于人们可以在链接数据云上找到数据的异构性，因此能够追溯查询结果的来源正迅速成为RDF系统的必备功能。尽管近年来对起源模型进行了广泛讨论，但很少有人关注在数据存储内部有效实现起源查询的问题。本文介绍了TripleProv：这是一个扩展本机RDF存储以有效处理此类查询的新系统。 TripleProv实现了两种不同的存储模型，以在物理上共同定位沿袭和实例数据，并且对于每种存储模型，都实现了用于在两个粒度级别上跟踪出处的算法。在下文中，我们介绍了系统的整体体系结构，其不同的世系存储模型，以及为有效地回答基于来源的查询而实施的各种查询执行策略。此外，我们还针对两个不同的数据集和工作负载对系统进行了全面的经验评估。

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