.png){kind=icon}
返回
Predictive intelligence of reliable analytics in distributed computing environments
.png){kind=icon}
OA
AI
DOI：10.1007/s10489-020-01712-5.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Lack of knowledge in the underlying data distribution in distributed large-scale data can be an obstacle when issuing analytics & predictive modelling queries. Analysts find themselves having a hard time finding analytics/exploration queries that satisfy their needs. In this paper, we study how exploration query results can be predicted in order to avoid the execution of 'bad'/non-informative queries that waste network, storage, financial resources, and time in a distributed computing environment. The proposed methodology involves clustering of a training set of exploration queries along with the cardinality of the results (score) they retrieved and then using query-centroid representatives to proceed with predictions. After the training phase, we propose a novel refinement process to increase the reliability of predicting the score of new unseen queries based on the refined query representatives. Comprehensive experimentation with real datasets shows that more reliable predictions are acquired after the proposed refinement method, which increases the reliability of the closest centroid and improves predictability under the right circumstances.
Keyword:
Predictive intelligence
Exploration query prediction
Centroid refinement
Machine learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
引用论文
.png){kind=icon}
A Survey of Top-k Query Processing Techniques in Relational Database Systems关系数据库系统中Top-k查询处理技术综述OAAI
OAAI分享收藏On the calibration of sensor arrays for pattern recognition using the minimal number of experiments
PREAI
分享收藏
查看更多内容