通过智能筛选预测点，简化日志同时保持甚至提升预测精度。
Log Optimization Simplification Method for Predicting Remaining Time
- 按相似性合并可简化的日志序列与自环结构
- 优化简化前后预测值偏差，防止过度压缩
- 适合需要高效日志处理的工业性能预测场景
信息系统在业务运行中生成大量事件日志，其中包含大量低价值冗余信息。直接基于此类日志进行性能预测会降低准确性。现有方法多聚焦于通过去除冗余特征来降维，但对简化前后的执行效率关注不足。本文提出一种预测点选择算法，避免对功能相似的全部数据点进行简化。通过选取序列或自环结构形成可简化段，并优化实际简化值与原始预测值之间的偏差，防止过简化。实验表明，简化后的日志仍保持预测能力，部分情况下甚至优于原始日志。
原文摘要 · Abstract (English)
Information systems generate a large volume of event log data during business operations, much of which consists of low-value and redundant information. When performance predictions are made directly from these logs, the accuracy of the predictions can be compromised. Researchers have explored methods to simplify and compress these data while preserving their valuable components. Most existing approaches focus on reducing the dimensionality of the data by eliminating redundant and irrelevant features. However, there has been limited investigation into the efficiency of execution both before and after event log simplification. In this paper, we present a prediction point selection algorithm designed to avoid the simplification of all points that function similarly. We select sequences or self-loop structures to form a simplifiable segment, and we optimize the deviation between the actual simplifiable value and the original data prediction value to prevent over-simplification. Experiments indicate that the simplified event log retains its predictive performance and, in some cases, enhances its predictive accuracy compared to the original event log.
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