Data Base Error Trapping and Prediction
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数据库错误检测与预测
- 研究问题
- 在数据库错误总量未知时，如何利用复核结果预测尚未发现的错误数，并判断先验信息对数据库质量评估的作用？
- 方法与思路
- 作者分别建模双人独立处理后比对记录的方法，以及加入已知错误后由单人检查的方法，推导未检出错误数的预测分布。
- 材料陈述的贡献
- 摘要指出双人比对数据可同时支持低错误率与高错误率情形，强调先验信息的重要性，并展示两类检查方法的预测分析。
- 阅读关注点
- 应核对错误产生与检出过程的模型结构、观测数据能区分哪些参数，以及先验选择如何改变剩余错误数的预测。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；摘要未列出检出机制的具体假设，不能判断预测结论对先验的稳健程度。
原文摘要
We develop and analyze models for a class of problems involving inferences about uncertain numbers of errors in data bases. In particular, we study two error detection methods. In the duplicate performance method, all items in a data base are processed by two individuals (or machines), and the resulting records are compared to find disagreements, which are then resolved. In the known errors method, a data base is first extended to include additional items known to be in error, and then the extended data base is checked by a single individual. For both methods, we lay out the underlying structure of the model and generate inferences in terms of predictive distributions for the numbers of undetected errors. The role of prior information is important in these problems of data base quality management. In the first method of error checking, for example, observed data are always equally consistent with small error rates and few remaining errors and with high error rates and many remaining errors. Most of our illustrative analyses use fairly conservative prior specifications, and the results are compared with those in the less formal development of Strayhorn. In practice, of course, appropriately realistic priors should be used, and some possibilities are mentioned. Models of the type studied here are applicable in a wide variety of important practical problems in data quality management, with examples in industrial quality control and reliability control being of particular note.