English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Logs are widely used in modern software system management because they areoften the only data accessible that record system events at runtime. In recentyears, because of the ever-increasing log size, data mining techniques areoften utilized to help developers and operators conduct system reliabilitymanagement. A typical log-based system reliability management procedure is tofirst parse log messages because of their unstructured format; and apply datamining techniques on the parsed logs to obtain critical system behaviorinformation. Most of existing research studies focus on offline log parsing,which need to parse logs in batch mode. However, software systems, especiallydistributed systems, require online monitoring and maintenance. Thus, a logparser that can parse log messages in a streaming manner is highly in demand.To address this problem, we propose an online log parsing method, namely Drain,based on directed acyclic graph, which encodes specially designed rules forparsing. Drain can automatically generate a directed acyclic graph for a newsystem and update the graph according to the incoming log messages. Besides,Drain frees developers from the burden of parameter tuning by allowing them useDrain with no pre-defined parameters. To evaluate the performance of Drain, wecollect 11 log datasets generated by real-world systems, ranging fromdistributed systems, Web applications, supercomputers, operating systems, tostandalone software. The experimental results show that Drain has the highestaccuracy on all 11 datasets. Moreover, Drain obtains 37.15\%$\sim$ 97.14\%improvement in the running time over the state-of-the-art online parsers. Wealso conduct a case study on a log-based anomaly detection task using Drain inthe parsing step, which determines its effectiveness in system reliabilitymanagement.

A Directed Acyclic Graph Approach to Online Log Parsing