Applying Inductive Logic Programming to Process Mining

Lamma, Evelina; Mello, Paola; Riguzzi, Fabrizio; Storari, Sergio

doi:10.1007/978-3-540-78469-2_16

Cited by 50 publications

(58 citation statements)

References 13 publications

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“…Some of these algorithms [12,8,4] assume that every trace in the input log is labeled as a "positive" or a "negative" case, where a negative case is one that should not occur. The problem of mining a declarative model is mapped to one of discriminating between positive and negative cases.…”

Section: Related Workmentioning

confidence: 99%

Discovering Data-Aware Declarative Process Models from Event Logs

Maggi

Dumas

García‐Bañuelos

et al. 2013

Lecture Notes in Computer Science

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Abstract. A wealth of techniques are available to automatically discover business process models from event logs. However, the bulk of these techniques yield procedural process models that may be useful for detailed analysis, but do not necessarily provide a comprehensible picture of the process. Additionally, barring few exceptions, these techniques do not take into account data attributes associated to events in the log, which can otherwise provide valuable insights into the rules that govern the process. This paper contributes to filling these gaps by proposing a technique to automatically discover declarative process models that incorporate both control-flow dependencies and data conditions. The discovered models are conjunctions of first-order temporal logic expressions with an associated graphical representation (Declare notation). Importantly, the proposed technique discovers underspecified models capturing recurrent rules relating pairs of activities, as opposed to full specifications of process behavior -thus providing a summarized view of key rules governing the process. The proposed technique is validated on a real-life log of a cancer treatment process.

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Section: Related Workmentioning

confidence: 99%

Discovering Data-Aware Declarative Process Models from Event Logs

Maggi

Dumas

García‐Bañuelos

et al. 2013

Lecture Notes in Computer Science

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“…To this purpose we summarize the Declarative Process Model Learner (DPML) proposed in [31], which takes as input a set of process execution traces, previously labeled as compliant or not, and produces a set of ICs which correctly classifies them. In particular, DPML solves the following problem: Given • for all t…”

Section: Learning Logical Integrity Constraintsmentioning

confidence: 99%

“…A refinement operator is used to explore the search space according to the language bias and the notion of generality among ICs [31].…”

Section: Learning Logical Integrity Constraintsmentioning

confidence: 99%

“…DPML (Declarative Process Model Learner) [31] is a process mining technique for learning models expressed in a subset of SCIFF. DPML discovers a set of declarative constraints from a set of traces labeled as compliant or not.…”

Section: Introductionmentioning

confidence: 99%

“…To realize the first goal, we start from the DPML algorithm [31] that induces expressive logical business rules and then we turn them into probabilistic rules using Markov Logic (ML) [43,20]. Markov Logic is a SRL formalism which adds a weight (real number) to each formula of a first-order knowledge base reflecting "how strong" the constraint is.…”

Section: Introductionmentioning

confidence: 99%

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Statistical relational learning for workflow mining

Bellodi

Riguzzi

Lamma

2016

IDA

Self Cite

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The management of business processes can support efficiency improvements in organizations. One of the most interesting problems is the mining and representation of process models in a declarative language. Various recently proposed knowledge-based languages showed advantages over graph-based procedural notations. Moreover, rapid changes of the environment require organizations to check how compliant are new process instances with the deployed models. We present a Statistical Relational Learning approach to Workflow Mining that takes into account both flexibility and uncertainty in real environments. It performs automatic discovery of process models expressed in a probabilistic logic. It uses the existing DPML algorithm for extracting first-order logic constraints from process logs. The constraints are then translated into Markov Logic to learn their weights. Inference on the resulting Markov Logic model allows a probabilistic classification of test traces, by assigning them the probability of being compliant to the model. We applied this approach to three datasets and compared it with DPML alone, five Petri net-and EPC-based process mining algorithms and Tilde. The technique is able to better classify new execution traces, showing higher accuracy and areas under the PR/ROC curves in most cases.

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