处理时态数据的数据库技术(特邀论文)

Time Pub Date : 2018-10-01 DOI:10.4230/LIPIcs.TIME.2018.2
Michael H. Böhlen, Anton Dignös, J. Gamper, Christian S. Jensen
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引用次数: 6

摘要

尽管时间数据无处不在,并且对处理此类数据进行了大量研究,但数据库系统在很大程度上仍然是为处理某些建模现实的当前状态而设计的。最近,我们看到人们对处理历史或时间数据越来越感兴趣。SQL:2011标准引入了一些时态特性,商业数据库管理系统已经开始逐步提供时态功能。还有一个针对时序查询的更基本和更全面的解决方案的建议,它允许与关系数据库系统紧密集成,从而利用现有的查询优化和评估技术。随着时间维度的增加和数据量的增加,包括代表一种特殊时间数据的时间序列数据,对处理时间数据提出了新的挑战。尽管时间数据无处不在,并且对处理这些数据进行了大量研究,但数据库系统在很大程度上仍然是为处理某些建模现实的当前状态而设计的。最近,我们看到人们对处理历史或时间数据越来越感兴趣。SQL:2011标准引入了一些时态特性,商业数据库管理系统已经开始逐步提供时态功能。还有一个针对时序查询的更基本和更全面的解决方案的建议,它允许与关系数据库系统紧密集成,从而利用现有的查询优化和评估技术。随着时间维度的增加和数据量的增加,包括代表一种特殊时间数据的时间序列数据,对处理时间数据提出了新的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Database Technology for Processing Temporal Data (Invited Paper)
: Despite the ubiquity of temporal data and considerable research on processing such data, database systems largely remain designed for processing the current state of some modeled reality. More recently, we have seen an increasing interest in processing historical or temporal data. The SQL:2011 standard introduced some temporal features, and commercial database management systems have started to offer temporal functionalities in a step-by-step manner. There has also been a proposal for a more fundamental and comprehensive solution for sequenced temporal queries, which allows a tight integration into relational database systems, thereby taking advantage of existing query optimization and evaluation technologies. New challenges for processing temporal data arise with multiple dimensions of time and the increasing amounts of data, including time series data that represent a special kind of temporal data. Abstract Despite the ubiquity of temporal data and considerable research on processing such data, database systems largely remain designed for processing the current state of some modeled reality. More recently, we have seen an increasing interest in processing historical or temporal data. The SQL:2011 standard introduced some temporal features, and commercial database management systems have started to offer temporal functionalities in a step-by-step manner. There has also been a proposal for a more fundamental and comprehensive solution for sequenced temporal queries, which allows a tight integration into relational database systems, thereby taking advantage of existing query optimization and evaluation technologies. New challenges for processing temporal data arise with multiple dimensions of time and the increasing amounts of data, including time series data that represent a special kind of temporal data.
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