  Detecting complex patterns in large volumes of event logs has diverse
applications in various domains, such as business processes and fraud
detection. Existing systems like ELK are commonly used to tackle this
challenge, but their performance deteriorates for large patterns, while they
suffer from limitations in terms of expressiveness and explanatory capabilities
for their responses. In this work, we propose a solution that integrates a
Complex Event Processing (CEP) engine into a broader query processsor on top of
a decoupled storage infrastructure containing inverted indices of log events.
The results demonstrate that our system excels in scalability and robustness,
particularly in handling complex queries. Notably, our proposed system delivers
responses for large complex patterns within seconds, while ELK experiences
timeouts after 10 minutes. It also significantly outperforms solutions relying
on FlinkCEP and executing MATCH_RECOGNIZE SQL queries.
