Efficient Discovery of Understandable Declarative Process Models from Event Logs

Maggi, Fabrizio Maria; Bose, R. P. Jagadeesh Chandra; Aalst, Wil M. P. van der

doi:10.1007/978-3-642-31095-9_18

Cited by 118 publications

(136 citation statements)

References 21 publications

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“…However, this assumption often does not hold as negative cases are generally not explicitly present in a real-life event log. In [16,14], LTL model checking techniques are used to classify negative and positive cases (i.e., constraint violations and fulfillments), thus avoiding the need for a preprocessing step to explicitly label the traces. The approach presented in this paper extends the one in [16,14] by using data attributes in order to enrich candidate control-flow constraints with data conditions.…”

Section: Related Workmentioning

confidence: 99%

“…Previous work on automated discovery of declarative process models [16,14] has focused on mining control-flow dependencies, such as "the execution of a task entails that another task must eventually be executed". This prior work, as well as the bulk of process discovery techniques for procedural languages, ignores data attributes attached to events, besides the event class.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Discovering Data-Aware Declarative Process Models from Event Logs

Maggi

Dumas

García‐Bañuelos

et al. 2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…The most prominent declarative control flow modeling frameworks are DecSerFlow [4] and its successor Declare [5], which offer a set of Linear Temporal Logic (LTL)-based constraint templates for modeling and rule verification purposes, bundled in the ConDec language. Many declarative process discovery algorithms have been developed, such as [2,6,7,8].…”

Section: Related Workmentioning

confidence: 99%

Multi-paradigm Process Mining: Retrieving Better Models by Combining Rules and Sequences

Smedt

Weerdt

Vanthienen

2014

On the Move to Meaningful Internet Systems: OTM 2014 Conferences

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Abstract. Business process mining is a well-established field of research which focuses on the automatic retrieval and analysis of process flows. The discovery and representation of these models is based on techniques that come in all shapes and forms. Most notably, procedurally-based algorithms such as Heuristics Miner have been used successfully for this purpose. Also, declarative process model miners have been proposed, which give other insights into the model by generating rules that apply on the activities. This paper proposes an integrated approach to combining these paradigms to discover process models that contain best of both worlds to enrich insights into the event logs under scrutiny.

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“…Techniques for the automated discovery of Declare maps from event logs have been proposed in [17][18][19]. These approaches, although promising, typically generate maps with too many constraints, have difficulties in correctly associating events, and do not provide diagnostic information.…”

Section: Correlations As a Means Of Enhancing Declare Mapsmentioning

confidence: 99%

“…3 A Declare map is a set of Declare constraints each one with its own graphical representation and LTL semantics (see [4] for a full overview of Declare). In recent years, approaches to discover Declare models from event logs [17][18][19] and approaches to check conformance of Declare models with respect to event logs [9,13] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Declare Maps Based on Event Correlations

Bose

Maggi

Aalst

2013

Lecture Notes in Computer Science

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Abstract. Traditionally, most process mining techniques aim at discovering procedural process models (e.g., Petri nets, BPMN, and EPCs) from event data. However, the variability present in less-structured flexible processes complicates the discovery of such procedural models. The "open world" assumption used by declarative models makes it easier to handle this variability. However, initial attempts to automatically discover declarative process models result in cluttered diagrams showing misleading constraints. Moreover, additional data attributes in event logs are not used to discover meaningful causalities. In this paper, we use correlations to prune constraints and to disambiguate event associations. As a result, the discovered process maps only show the more meaningful constraints. Moreover, the data attributes used for correlation and disambiguation are also used to find discriminatory patterns, identify outliers, and analyze bottlenecks (e.g., when do people violate constraints or miss deadlines). The approach has been implemented in ProM and experiments demonstrate the improved quality of process maps and diagnostics.

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Efficient Discovery of Understandable Declarative Process Models from Event Logs

Cited by 118 publications

References 21 publications

Discovering Data-Aware Declarative Process Models from Event Logs

Discovering Data-Aware Declarative Process Models from Event Logs

Multi-paradigm Process Mining: Retrieving Better Models by Combining Rules and Sequences

Enhancing Declare Maps Based on Event Correlations

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