Unuvar, Lakshmanan, Doganata 2016 — Path Information for Parallel Processes

Pre-deep-learning PPM targeting semi-structured business processes with parallel execution paths. Companion to sources/2013-lakshmanan-markov-semi-structured from the same IBM T.J. Watson research stream.

Contribution

Five models for representing the execution path as a feature for prediction.
Information-gain-based test for independence of parallel paths.
Decision-tree outcome prediction evaluated on a simulated marketing-campaign business process.

Position

Part of the classical decision-tree lineage of PPM that preceded deep learning. Its insistence on path representation as a prediction feature resurfaces in modern suffix-prediction and trace-encoding research.

Connections

Concepts: concepts/outcome-prediction · concepts/trace-encoding Authors: entities/merve-unuvar · entities/geetika-lakshmanan