A Framework for Optimal Approximate Query Evaluation based on Workload Forecasting

基于工作负载预测的最优近似查询评估框架

• 批准号: 0415023
• 负责人: Byung Lee
• 金额: --
• 依托单位: University of Vermont & State Agricultural College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0415023&HistoricalAwards=false
• 关键词: Framework; Optimal; Approximate; Query; Evaluation

The project aims to use workload forecasting in approximate query evaluation applications. For this purpose, existing forecasting techniques are leveraged to develop an approach for forecasting a sequence of workloads for a future time interval based on the data access pattern. These applications operate in two phases: off-line compression and on-line query evaluation. In the first phase, an access pattern is extracted from a sequence of accessed data elements mapped from the queries in a log, and is used to forecast a workload for each subinterval of a given future time interval. This generates a sequence of workloads. The workload information is then used to compress the data elements targeted for each subinterval. The compressed data elements are then stored on disk and, at run-time, loaded and translated into a main memory data structure needed for query evaluation in each subinterval. These steps of forecast, compression, and loading combined directly influence the trade-off between the approximate query result accuracy and query speed, thereby necessitating an optimization. The research will have immediate impacts in application areas needing the approximate query evaluation abilities of DBMSs, particularly those needing to use limited system resources effectively through workload forecasting. Furthermore, the developed techniques will have impacts in various fields of science and engineering by enabling efficient and effective utilization of limited resources based on forecasted workload.

该项目旨在将工作量预测用于近似查询评估应用程序。为此，利用现有预测技术来开发一种用于基于数据访问模式来预测未来时间间隔的工作负荷序列的方法。这些应用程序分两个阶段运行：离线压缩和在线查询评估。在第一阶段，从从日志中的查询映射的被访问数据元素的序列中提取访问模式，并使用该访问模式来预测给定未来时间间隔的每个子间隔的工作负载。这将生成一系列工作负载。然后使用工作负载信息来压缩针对每个子间隔的数据元素。然后将压缩的数据元素存储在磁盘上，并在运行时将其加载并转换为每个子间隔中的查询评估所需的主存储器数据结构。预测、压缩和加载这些步骤结合在一起，直接影响到近似查询结果精度和查询速度之间的权衡，从而需要进行优化。这项研究将对需要数据库管理系统近似查询评估能力的应用领域产生直接影响，特别是那些需要通过负载预测有效利用有限系统资源的应用领域。此外，所开发的技术将在科学和工程的各个领域产生影响，能够根据预测的工作量有效地利用有限的资源。