Mining process models with prime invisible tasks

Wen, Lijie; Wang, Jianmin; Aalst, Wil M. P. van der; Huang, Biqing; Sun, Jun

doi:10.1016/j.datak.2010.06.001

Cited by 112 publications

(85 citation statements)

References 74 publications

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“…It examines causal relationships between tasks: for example, one specific task might always precede another specific task in every execution trace. An extension of the mining capacity of the α-algorithm to the detection of "prime invisible tasks" from an event log has been proposed with the α # algorithm [58], which tries to identify dependencies between tasks reflecting those used for routing purposes that exist only in a process model but are not recorded in its event log.…”

Section: Related Workmentioning

confidence: 99%

Statistical relational learning for workflow mining

Bellodi

Riguzzi

Lamma

2016

IDA

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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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Section: Related Workmentioning

confidence: 99%

Statistical relational learning for workflow mining

Bellodi

Riguzzi

Lamma

2016

IDA

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“…From [52] onwards, many techniques have been proposed. The α-algorithm [54] and its extensions α ++ [55], α # [56], and α $ [57] are algorithmic solutions that exploit behavioural relations between pairs of activities to discover a procedural process model from an event log. Differently from the declarative constraints of Declare, such relations are mutually exclusive: Since they do not overlap, they are by construction nonredundant.…”

Section: Related Workmentioning

confidence: 99%

Resolving inconsistencies and redundancies in declarative process models

Ciccio

Maggi

Montali

et al. 2017

Information Systems

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Declarative process models define the behaviour of business processes as a set of constraints. Declarative process discovery aims at inferring such constraints from event logs. Existing discovery techniques verify the satisfaction of candidate constraints over the log, but completely neglect their interactions. As a result, the inferred constraints can be mutually contradicting and their interplay may lead to an inconsistent process model that does not accept any trace. In such a case, the output turns out to be unusable for enactment, simulation or verification purposes. In addition, the discovered model contains, in general, redundancies that are due to complex interactions of several constraints and that cannot be cured using existing pruning approaches. We address these problems by proposing a technique that automatically resolves conflicts within the discovered models and is more powerful than existing pruning techniques to eliminate redundancies. First, we formally define the problems of constraint redundancy and conflict resolution. Second, we introduce techniques based on the notion of automata-product monoid, which guarantees the consistency of the discovered models and, at * Corresponding author. E-mail address: claudio.di.ciccio@wu.ac.at Postal address: Vienna University of Economics and Business, Institute for Information Business (Building D2, Entrance C) -Welthandelsplatz 1, A-1020 Vienna, Austria Phone number: +43 1 31336 5222Email addresses: claudio.di.ciccio@wu.ac.at (Claudio Di Ciccio), f.m.maggi@ut.ee (Fabrizio Maria Maggi), montali@inf.unibz.it (Marco Montali), jan.mendling@wu.ac.at (Jan Mendling) Preprint submitted to Information Systems 7th October 2016 the same time, keeps the most interesting constraints in the pruned set. The level of interestingness is dictated by user-specified prioritisation criteria. We evaluate the devised techniques on a set of real-world event logs.

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“…A lot of research has been done on mining of process models from event log data [1,5,8,36,47,48]. An early work that relied on the activity logs for producing formal process models corresponding to actual process execution is given in [5].…”

Section: Related Workmentioning

confidence: 99%

“…Results show its usefulness in activities such as process model discovery, re-engineering, software process improvement, etc. In [48], the focus is on detection of invisible tasks from event logs. Their definition of invisible tasks is tasks that exist in a process model, but not in its event log (such as initialize, skip, switch, redo, etc.).…”

Section: Related Workmentioning

confidence: 99%

Ontology Change Management and Identification of Change Patterns

2013

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Ontologies can support a variety of purposes, ranging from capturing the conceptual knowledge to the organisation of digital content and information. However, information systems are always subject to change and ontology change management can pose challenges. In this sense, the application and representation of ontology changes in terms of higher-level change operations can describe more meaningful semantics behind the applied change. In this paper, we propose a four-phase process that covers the operationalization, representation and detection of higher-level changes in ontology evolution life cycle. We present different levels of change operators based on the granularity and domain-specificity of changes. The first layer is based on generic atomic level change operators, whereas the next two layers are user-defined (generic/domain-specific) change patterns. We introduce layered change logs for the explicit operational representation of ontology changes. We formalised the change log using a graph-based approach. We introduce a technique to identify composite changes that not only assists in formulating ontology change log data in a more concise manner, but also helps in realizing the semantics and intent behind any applied change. Furthermore, we identify frequent change sequences that are applied as a reference to discover reusable, often domain-specific and usage-driven change patterns. We describe the pattern identification algorithms and evaluate their performance.

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Mining process models with prime invisible tasks

Cited by 112 publications

References 74 publications

Statistical relational learning for workflow mining

Statistical relational learning for workflow mining

Resolving inconsistencies and redundancies in declarative process models

Ontology Change Management and Identification of Change Patterns

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