出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：We aim to model unknown file processing. As the content of log files often evolves over time, we
established a dynamic statistical model which learns and adapts processing and parsing rules.
First, we limit the amount of unstructured text by focusing only on those frequent patterns which
lead to the desired output table similar to Vaarandi [10]. Second, we transform the found
frequent patterns and the output stating the parsed table into a Hidden Markov Model (HMM).
We use this HMM as a specific, however, flexible representation of a pattern for log file
processing. With changes in the raw log file distorting learned patterns, we aim the model to
adapt automatically in order to maintain high quality output. After training our model on one
system type, applying the model and the resulting parsing rule to a different system with slightly
different log file patterns, we achieve an accuracy over 99%.
关键词：Hidden Markov Models; Parameter Extraction; Parsing; Text Mining; Information Retrieval
