Trace Encoding

Converting a variable-length (and possibly multi-attribute) event trace prefix into a fixed-size feature representation consumable by a classifier or regressor. A design choice with large impact on PPM performance.

Encoding families (Teinemaa / Leontjeva / Verenich et al.)

Last-state encoding

Only the attributes of the latest event are used. Simplest, weakest.

Aggregate (boolean / frequency) encoding

Aggregate over all events in the prefix:

Boolean — has activity a occurred?
Frequency — how many times has a occurred? Does not preserve order.

Index-based encoding

Fixed-length padded representation: positions 1…k_max, each carrying the activity (and possibly attributes) at that index. Preserves order but explodes feature space for long traces.

Complex symbolic sequence encodings

HMM-based, N-gram-based — capture sub-sequence patterns without the sparsity of index encoding (Leontjeva et al. 2016).

Learned embeddings

Neural approaches learn dense representations end-to-end:

LLM string encoding (sequential, ρ_seq)

The newest family, designed for LLM-based PPM: a trace is serialised as a human-readable string consumable by a large language model. Padella, de Leoni & Dumas 2026 introduce ρ_seq:

ρ_seq(σ) = global(σ) ⊕ (activity(e₁), duration(e₁)) ⊕ … ⊕ (activity(eₙ), duration(eₙ)) ⊕ K(σ)

Design choices:

Global attributes preserved — they carry domain knowledge the LLM can leverage.
Local attributes deliberately omitted — to respect LLM context-length constraints and avoid documented long-context degradation (BABILong, Long-Context LLMs Struggle).
Activity name + duration per event — minimal control-flow + temporal information.
Target value appended — for in-context completed-trace examples; absent for the running query trace.

ρ_seq sits outside the classical encoding taxonomy: it is human-readable rather than fixed-size, semantically meaningful (activity names matter, not just IDs), and tightly coupled to the LLM's prompt design.

Data-awareness

Encoding of event attributes (data perspective) in addition to activities typically outperforms control-flow-only encodings (sources/2017-navarin-lstm-data-aware-remaining-time).

Trade-offs

Encoding	Order preserved	Feature size	Data-aware support
Last-state	–	Small	Partial
Aggregate	–	Medium	Yes
Index-based	✔	Large	Yes
Complex symbolic	Partial	Medium	Partial
Learned embeddings	✔	Dense, fixed	Yes
LLM string (ρ_seq)	✔	Variable string	Partial (global only)

concepts/predictive-process-monitoring · concepts/lstm-ppm · concepts/transformer-ppm · concepts/llm-based-ppm