标题： 天文学来源序列化的评估 显示英文标题

标题： Evaluation of Provenance Serialisations for Astronomical Provenance

摘要： 天文数据处理流水线中的来源数据对于建立数据处理和产品中的信任和可重复性至关重要。此外，天文学家可以查询他们的来源数据以回答与异常检测、推荐和预测等领域相关的问题。下一代天文巡天望远镜（如Vera Rubin天文台或平方公里阵列）能够生成从拍字节到艾字节规模的数据，从而放大了来源存储或查询效率即使是很小的改进的重要性。为了确定天文学家应如何存储和查询其来源数据，本文报告了对turtle和JSON来源序列化的比较。分别选择了三元组存储Apache Jena Fuseki和图数据库系统Neo4j作为代表性的数据库管理系统(DBMS)，用于turtle和JSON。模拟的来源数据被上传到每个DBMS并进行查询，用于比较的指标是查询的准确性和时间以及数据上传时间。结果发现这两种序列化都适合此用途，并且两者具有相似的查询准确性。发现turtle来源在存储和上传数据方面更高效。关于查询，对于小型数据集($<$5MB)和简单的信息检索查询，也发现turtle序列化更高效。然而，发现JSON序列化来源对于涉及跨DBMS匹配模式的更复杂查询更高效，这种效果随着查询来源的大小而扩展。 显示英文摘要

摘要： Provenance data from astronomical pipelines are instrumental in establishing trust and reproducibility in the data processing and products. In addition, astronomers can query their provenance to answer questions routed in areas such as anomaly detection, recommendation, and prediction. The next generation of astronomical survey telescopes such as the Vera Rubin Observatory or Square Kilometre Array, are capable of producing peta to exabyte scale data, thereby amplifying the importance of even small improvements to the efficiency of provenance storage or querying. In order to determine how astronomers should store and query their provenance data, this paper reports on a comparison between the turtle and JSON provenance serialisations. The triple store Apache Jena Fuseki and the graph database system Neo4j were selected as representative database management systems (DBMS) for turtle and JSON, respectively. Simulated provenance data was uploaded to and queried over each DBMS and the metrics measured for comparison were the accuracy and timing of the queries as well as the data upload times. It was found that both serialisations are competent for this purpose, and both have similar query accuracy. The turtle provenance was found to be more efficient at storing and uploading the data. Regarding queries, for small datasets ($<$5MB) and simple information retrieval queries, the turtle serialisation was also found to be more efficient. However, queries for JSON serialised provenance were found to be more efficient for more complex queries which involved matching patterns across the DBMS, this effect scaled with the size of the queried provenance.