Efficient Construction of Behavior Graphs for Uncertain Event Data

Pegoraro, Marco; Uysal, Merih Seran; Aalst, Wil M. P. van der

doi:10.1007/978-3-030-53337-3_6

Cited by 10 publications

(8 citation statements)

References 12 publications

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“…At n = 15,000, the space needed by the multiset of behavior graphs is 59.2%, a sizable improvement in memory requirements when analyzing uncertain event logs of substantial dimensions. This improvement in memory consumption is a consequence of the new technique used in this paper to obtain the timestamp list, which enables such refinement with respect to the technique illustrated in [5]. TrRed Improved Figure 14.…”

Section: Performance Of Behavior Graph Constructionmentioning

confidence: 97%

“…In a previous paper [5], we presented a time-effective algorithm for the construction of the behavior graph of an uncertain process trace, attaining quadratic time complexity on the number of events in the trace.…”

Section: Case Id Eventmentioning

confidence: 99%

“…For instance, the trace a, {b, c}, [d, e] is a trace containing 4 events, of which the first is an indeterminate event with activity label a, the second is an uncertain event that can have either b or c as activity label, and the last two events have an interval as timestamp (and the two ranges overlap). Let us consider the following event log: a, b, e, f , g, h 80 , a, {b, c}, [e, f ], g, i 15 , a, {b, c, d}, [e, f ], g, j 5 .…”

Section: Process Discovery Over Uncertain Datamentioning

confidence: 99%

“…Figure 4. The Uncertain Directly-Follows Graph (UDFG) computed based on the uncertain event log a, b, e, f , g, h 80 , a, {b, c}, [e, f ], g, i15 , a, {b, c, d}, [e, f ], g, j5 . The arcs are labeled with the minimum and maximum number of directly-follows relationship observable between activities in the corresponding trace.…”

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Efficient Time and Space Representation of Uncertain Event Data

2020

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Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches assume that event data is accurately captured behavior. However, this is not realistic in many applications: data can contain uncertainty, generated from errors in recording, imprecise measurements, and other factors. Recently, new methods have been developed to analyze event data containing uncertainty; these techniques prominently rely on representing uncertain event data by means of graph-based models explicitly capturing uncertainty. In this paper, we introduce a new approach to efficiently calculate a graph representation of the behavior contained in an uncertain process trace. We present our novel algorithm, prove its asymptotic time complexity, and show experimental results that highlight order-of-magnitude performance improvements for the behavior graph construction.

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Section: Performance Of Behavior Graph Constructionmentioning

confidence: 97%

Section: Case Id Eventmentioning

confidence: 99%

Section: Process Discovery Over Uncertain Datamentioning

confidence: 99%

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confidence: 99%

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Efficient Time and Space Representation of Uncertain Event Data

2020

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“…Explicit uncertainty is considered in the work of Pegoraro et al [12,13]. Event logs are annotated with explicit uncertainty and this information is used when discovering process models or checking conformance.…”

Section: Related Workmentioning

confidence: 99%

May I Take Your Order? On the Interplay Between Time and Order in Process Mining

Aalst¹,

Santos²

2021

Preprint

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Process mining starts from event data. The ordering of events is vital for the discovery of process models. However, the timestamps of events may be unreliable or imprecise. To further complicate matters, also causally unrelated events may be ordered in time. The fact that one event is followed by another does not imply that the former causes the latter. This paper explores the relationship between time and order. Moreover, it describes an approach to preprocess event data having timestamp-related problems. This approach avoids using accidental or unreliable orders and timestamps, creates partial orders to capture uncertainty, and allows for exploiting domain knowledge to (re)order events. Optionally, the approach also generates interleavings to be able to use existing process mining techniques that cannot handle partially ordered event data. The approach has been implemented using ProM and can be applied to any event log.

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