Information is one of the most important assets of an organization. In recent years, the volume of data stored in organizations, varying user requirements, time constraints, and query management complexities have grown exponentially. Due to these problems, the performance modeling of queries in data warehouses (DWs) has assumed a key role in organizations. DWs make relevant information available to decision-makers; however, DW administration is becoming increasingly difficult and time-consuming. DW administrators spend too much time managing queries, which also affects data warehouse performance. To enhance the performance of overloaded data warehouses with varying queries, a prediction-based framework is required that forecasts the behavior of query performance metrics in a DW. In this study, we propose a cluster-based autonomic performance prediction framework using a case-based reasoning approach that determines the performance metrics of the data warehouse in advance by incorporating autonomic computing characteristics. This prediction is helpful for query monitoring and management. For evaluation, we used metrics for precision, recall, accuracy, and relative error rate. The proposed approach is also compared with existing lazy learning techniques. We used the standard TPC-H dataset. Experiments show that our proposed approach produce better results compared to existing techniques.

信息是组织最重要的资产之一。近年来，组织中存储的数据量，不断变化的用户需求，时间限制以及查询管理的复杂性呈指数增长。由于这些问题，数据仓库（DW）中的查询性能建模已在组织中发挥了关键作用。DW向决策者提供相关信息；但是，DW管理变得越来越困难和耗时。DW管理员花费太多时间来管理查询，这也会影响数据仓库的性能。为了提高具有各种查询的过载数据仓库的性能，需要一个基于预测的框架来预测DW中查询性能指标的行为。在这个研究中，我们提出了一种基于案例的推理方法的基于集群的自主性能预测框架，该框架通过结合自主计算特征预先确定数据仓库的性能指标。此预测有助于查询监视和管理。为了进行评估，我们使用了精度，召回率，准确性和相对错误率的指标。所提出的方法也与现有的懒惰学习技术进行了比较。我们使用了标准的TPC-H数据集。实验表明，与现有技术相比，我们提出的方法可产生更好的结果。为了进行评估，我们使用了精度，召回率，准确性和相对错误率的指标。所提出的方法也与现有的懒惰学习技术进行了比较。我们使用了标准的TPC-H数据集。实验表明，与现有技术相比，我们提出的方法可产生更好的结果。为了进行评估，我们使用了精度，召回率，准确性和相对错误率的指标。所提出的方法也与现有的懒惰学习技术进行了比较。我们使用了标准的TPC-H数据集。实验表明，与现有技术相比，我们提出的方法可产生更好的结果。