Towards Integrated Data Analytics: Time Series Forecasting in DBMS
Towards Integrated Data Analytics: Time Series Forecasting in DBMS
复制标题
迈向集成数据分析：DBMS 中的时间序列预测
DOI：
10.1007/s13222-012-0108-4
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Wolfgang Lehner
中科院分区：
文献类型：
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作者：
Ulrike Fischer;Lars Dannecker;Laurynas Siksnys;Frank Rosenthal;Matthias Boehm;Wolfgang Lehner
将复杂的统计方法集成到数据库管理系统中越来越受到研究和工业界的关注，以便能够应对不断增加的数据量和日益增加的分析算法的复杂性。时间序列预测是一种重要的统计方法，它对于许多领域的决策过程至关重要。时间序列预测的深度集成在 DBMS 中提供了额外的高级功能。但更重要的是，它可以进行优化，提高整个预测过程的效率、一致性和透明度。为了实现高效的综合预测，我们建议增强具有预测功能的 DBMS 的传统 3 层 ANSI/SPARC 架构。本文概述了我们提出的增强功能，并介绍了如何使用能源数据管理领域的示例来处理预测查询。最后，我们提出了该领域出现的开放研究主题和挑战。
Integrating sophisticated statistical methods into database management systems is gaining more and more attention in research and industry in order to be able to cope with increasing data volume and increasing complexity of the analytical algorithms. One important statistical method is time series forecasting, which is crucial for decision making processes in many domains. The deep integration of time series forecasting offers additional advanced functionalities within a DBMS. More importantly, however, it allows for optimizations that improve the efficiency, consistency, and transparency of the overall forecasting process. To enable efficient integrated forecasting, we propose to enhance the traditional 3-layer ANSI/SPARC architecture of a DBMS with forecasting functionalities. This article gives a general overview of our proposed enhancements and presents how forecast queries can be processed using an example from the energy data management domain. We conclude with open research topics and challenges that arise in this area.