SGProv: Summarization Mechanism for Multiple Provenance Graphs
SGProv: Summarization Mechanism for Multiple Provenance Graphs
复制标题
SGProv：多来源图的汇总机制
DOI：
10.1109/mcse.2010.13
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Alexandre A. B. Lima
中科院分区：
文献类型：
--
作者：
Daniele El;M. Mattoso;Alexandre A. B. Lima
科学工作流管理系统（SWfMS）是科学实验自动化的强大工具。完成一项科学实验需要多次执行工作流。数据溯源（通常由SWfMS在工作流执行期间收集）对于理解、重现和分析科学实验非常重要。溯源是关于数据的派生，因此它通常以有向无环图的形式表示。对于每次工作流执行，都会生成一个溯源图。在多次运行工作流并探索不同参数之后，会生成大量的图。由此产生的溯源数据库需要大量的存储空间，并且对其进行查询涉及处理大量的图。典型的溯源查询会处理许多图以获取数据派生路径（谱系）。本文提出了SGProv，一种溯源图的汇总机制，它使用图数据库来存储和查询这些图。其目标是生成一个单一的小型汇总图，该图代表实验期间生成的所有溯源图，消除冗余数据。这种汇总方法旨在通过仅使用汇总图来回答溯源查询，而无需重建原始图，从而减少溯源查询的处理时间。对来自典型工作流执行的汇总图进行溯源查询的结果表明，在查询结果无数据丢失的情况下性能得到了提高。
Scientific workflow management systems (SWfMS) are powerful tools in the automation of scientific experiments. Several workflow executions are necessary to accomplish one scientific experiment. Data provenance, typically collected by SWfMS during workflow execution, is important to understand, reproduce and analyze scientific experiments. Provenance is about data derivation, thus it is typically represented in the form of a directed acyclic graph. For each workflow execution, a provenance graph is generated. Numerous graphs are generated after several workflow runs, exploring different parameters. The resulting provenance database requires considerable storage space and querying it involves handling a large volume of graphs. Typical provenance queries process many graphs to get data derivation paths (lineage). This article proposes SGProv, a summarization mechanism for provenance graphs, using a graph database to store and query them. The goal is to generate a single small summary graph that represents all provenance graphs generated during an experiment, eliminating redundant data. This summarization approach aims to reduce the processing time of provenance queries by using only the summary graph to answer them without the need for rebuilding the original graphs. Results of provenance queries on the summary graph, from typical workflow executions, show performance improvements without data loss on query results.
影响因子：
2.2
作者：
Gil, Yolanda;Deelman, Ewa;Myers, Jim
通讯作者：
Myers, Jim