  Modern, large scale monitoring systems have to process and store vast amounts
of log data in near real-time. At query time the systems have to find relevant
logs based on the content of the log message using support structures that can
scale to these amounts of data while still being efficient to use. We present
our novel Compressed Probabilistic Retrieval algorithm (COPR), capable of
answering Multi-Set Multi-Membership-Queries, that can be used as an
alternative to existing indexing structures for streamed log data. In our
experiments, COPR required up to 93% less storage space than the tested
state-of-the-art inverted index and had up to four orders of magnitude less
false-positives than the tested state-of-the-art membership sketch.
Additionally, COPR achieved up to 250 times higher query throughput than the
tested inverted index and up to 240 times higher query throughput than the
tested membership sketch.
