Conformance Checking Based on Partially Ordered Event Data

Lu, Xixi; Fahland, Dirk; Aalst, Wil M. P. van der

doi:10.1007/978-3-319-15895-2_7

Cited by 51 publications

(44 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another relevant research direction [40][41] relies on the notion of partiallyordered event data and introduces techniques for conformance checking of this type of event representations. In particular, [40] presents the notion of partiallyordered trace (p-trace).…”

Section: Related Work and Discussionmentioning

confidence: 99%

“…In particular, [40] presents the notion of partiallyordered trace (p-trace). As in the case of CPOGs, a p-trace allows for explicit concurrency between events of the same trace.…”

Section: Related Work and Discussionmentioning

confidence: 99%

“…However, the use of data attributes is narrower compared to the approach presented in this paper: data attributes are split into read/write accesses to data values, and simple rules to extract concurrency and dependency are introduced to take into account the role of a data access within a trace. We believe that the techniques for relating control flow and data presented in this paper may be applied in the scope of [40] [41].…”

Section: Related Work and Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Mining Conditional Partial Order Graphs from Event Logs

Mokhov

Carmona

Beaumont

2016

Transactions on Petri Nets and Other Models of Concurrency XI

View full text Add to dashboard Cite

Process mining techniques rely on event logs: the extraction of a process model (discovery) takes an event log as the input, the adequacy of a process model (conformance) is checked against an event log, and the enhancement of a process model is performed by using available data in the log. Several notations and formalisms for event log representation have been proposed in the recent years to enable efficient algorithms for the aforementioned process mining problems. In this paper we show how Conditional Partial Order Graphs (CPOGs), a recently introduced formalism for compact representation of families of partial orders, can be used in the process mining field, in particular for addressing the problem of compact and easy-to-comprehend representation of event logs with data. We present algorithms for extracting both the control flow as well as the relevant data parameters from a given event log and show how CPOGs can be used for efficient and effective visualisation of the obtained results. We demonstrate that the resulting representation can be used to reveal the hidden interplay between the control and data flows of a process, thereby opening way for new process mining techniques capable of exploiting this interplay.

show abstract

Section: Related Work and Discussionmentioning

confidence: 99%

“…In particular, [40] presents the notion of partiallyordered trace (p-trace). As in the case of CPOGs, a p-trace allows for explicit concurrency between events of the same trace.…”

Section: Related Work and Discussionmentioning

confidence: 99%

Section: Related Work and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Mining Conditional Partial Order Graphs from Event Logs

Mokhov

Carmona

Beaumont

2016

Transactions on Petri Nets and Other Models of Concurrency XI

View full text Add to dashboard Cite

show abstract

“…Alignment-based techniques rely on total ordering of events; thus, diagnostics obtained by these techniques can be unreliable when timestamps of events are coarse or incorrect. Lu et al [18] describe how partially ordered traces can be obtained from sequential event logs and propose an approach for computing partially ordered alignments using these partially ordered traces. Alignment-based approaches specify the obtained diagnostic information in terms of low level deviations; our work, instead, is focused on deriving high-level deviations.…”

Section: Related Workmentioning

confidence: 99%

Subgraph Mining for Anomalous Pattern Discovery in Event Logs

Genga

Potena

Martino

et al. 2017

New Frontiers in Mining Complex Patterns

View full text Add to dashboard Cite

Abstract. Conformance checking allows organizations to verify whether their IT system complies with the prescribed behavior by comparing process executions recorded by the IT system against a process model (representing the normative behavior). However, most of the existing techniques are only able to identify low-level deviations, which provide a scarce support to investigate what actually happened when a process execution deviates from the specification. In this work, we introduce an approach to extract recurrent deviations and generate anomalous patterns from them, which are able to provide meaningful diagnostics of what happened in the system. To detect anomalous behaviors, we apply frequent subgraph mining techniques together with an ad-hoc conformance checking technique. Anomalous patterns are then derived by applying frequent items algorithms to determine highly-correlated deviations, among which ordering relations are inferred. The approach has been validated by means of a set of experiments.

show abstract

“…Therefore, it is interesting to also search for more local patterns in the event log -using episode discovery -while still exploiting the notion of process instances. Another useful application of episode discovery is conformance checking based on partial orders [3].…”

Section: Introductionmentioning

confidence: 99%

Discovery of Frequent Episodes in Event Logs

Leemans

Aalst

2015

Lecture Notes in Business Information Processing

Self Cite

View full text Add to dashboard Cite

Abstract. Lion's share of process mining research focuses on the discovery of end-to-end process models describing the characteristic behavior of observed cases. The notion of a process instance (i.e., the case) plays an important role in process mining. Pattern mining techniques (such as frequent itemset mining, association rule learning, sequence mining, and traditional episode mining) do not consider process instances. An episode is a collection of partially ordered events. In this paper, we present a new technique (and corresponding implementation) that discovers frequently occurring episodes in event logs thereby exploiting the fact that events are associated with cases. Hence, the work can be positioned in-between process mining and pattern mining. Episode discovery has its applications in, amongst others, discovering local patterns in complex processes and conformance checking based on partial orders. We also discover episode rules to predict behavior and discover correlated behaviors in processes. We have developed a ProM plug-in that exploits efficient algorithms for the discovery of frequent episodes and episode rules. Experimental results based on real-life event logs demonstrate the feasibility and usefulness of the approach.

show abstract

Conformance Checking Based on Partially Ordered Event Data

Cited by 51 publications

References 17 publications

Mining Conditional Partial Order Graphs from Event Logs

Mining Conditional Partial Order Graphs from Event Logs

Subgraph Mining for Anomalous Pattern Discovery in Event Logs

Discovery of Frequent Episodes in Event Logs

Contact Info

Product

Resources

About