  This paper presents DAALder (Database-Assisted Automata Learning, with Dutch
suffix from leerder), a new algorithm for learning state machines, or automata,
specifically deterministic finite-state automata (DFA). When learning state
machines from log data originating from software systems, the large amount of
log data can pose a challenge. Conventional state merging algorithms cannot
efficiently deal with this, as they require a large amount of memory. To solve
this, we utilized database technologies to efficiently query a big trace
dataset and construct a state machine from it, as databases allow to save large
amounts of data on disk while still being able to query it efficiently.
Building on research in both active learning and passive learning, the proposed
algorithm is a combination of the two. It can quickly find a characteristic set
of traces from a database using heuristics from a state merging algorithm.
Experiments show that our algorithm has similar performance to conventional
state merging algorithms on large datasets, but requires far less memory.
