Desire Lines in Big Data

Aalst, Wil M. P. van der

doi:10.1007/978-1-4614-6170-8_396

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…For an event to replay, if objects are not located as tokens in speci ed input places of the transition to re, we proposed a jump strategy to move tokens from their current location in the model to the required places. Interestingly, this approach not only allows us to force transition rings to nd more deviations in a trace, but also recorded jumps between places unveil the so-called desire lines, i.e., paths of objects which are unforeseen in the speci cation [17].…”

Section: Discussionmentioning

confidence: 99%

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Object-Centric Replay-Based Conformance Checking: Unveiling Desire Lines and Local Deviations

Карраскель¹,

Мешерауи

2021

Model. anal. inf. sist.

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Conformance checking methods diagnose to which extent a real system, whose behavior is recorded in an event log, complies with its specification model, e.g., a Petri net. Nonetheless, the majority of these methods focus on checking isolated process instances, neglecting interaction between instances in a system. Addressing this limitation, a series of object-centric approaches have been proposed in the field of process mining. These approaches are based on the holistic analysis of the multiple process instances interacting in a system, where each instance is centered on the handling of an object. Inspired by the object-centric paradigm, this paper presents a replay-based conformance checking method which uses a class of colored Petri nets (CPNs) -- a Petri net extension where tokens in the model carry values of some types (colors). Particularly, we consider conservative workflow CPNs which allow to describe the expected behavior of a system whose components are centered on the end-to-end processing of distinguishable objects. For describing a system’s real behavior, we consider event logs whose events have sets of objects involved in the execution of activities. For replay, we consider a jump strategy where tokens absent from input places of a transition to fire move from their current place of the model to the requested places. Token jumps allow to identify desire lines, i.e., object paths unforeseen in the specification. Also, we introduce local diagnostics based on the proportion of jumps in specific model components. The metrics allow to inform the severity of deviations in precise system parts. Finally, we report experiments supported by a prototype of our method. To show the practical value of our method, we employ a case study on trading systems, where orders from users are matched to trade.

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Section: Discussionmentioning

confidence: 99%

“…e method reports all token jumps between places and their frequency. As we will present, this information can be added to the input CPN model to unveil so-called desire lines [17], i.e., actual paths of objects which are unforeseen in the speci cation model.…”

Section: Introductionmentioning

confidence: 99%

Object-Centric Replay-Based Conformance Checking: Unveiling Desire Lines and Local Deviations

Карраскель¹,

Мешерауи

2021

Model. anal. inf. sist.

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A novel completeness definition of event logs and corresponding generation algorithm

Wen

et al. 2020

Expert Systems

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As the promotion of technologies and applications of Big Data, the research of business process management (BPM) has gradually deepened to consider the impacts and challenges of big business data on existing BPM technologies. Recently, parallel business process mining (e.g. discovering business models from business visual data, integrating runtime business data with interactive business process monitoring visualisation systems and summarising and visualising historical business data for further analysis, etc.) and multi‐perspective business data analytics (e.g. pattern detecting, decision‐making and process behaviour predicting, etc.) have been intensively studied considering the steep increase in business data size and type. However, comprehensive and in‐depth testing is needed to ensure their quality. Testing based solely on existing business processes and their system logs is far from sufficient. Large‐scale randomly generated models and corresponding complete logs should be used in testing. To test parallel algorithms for discovering process models, different log completeness and generation algorithms were proposed. However, they suffer from either state space explosion or non‐full‐covering task dependencies problem. Besides, most existing generation algorithms rely on random executing strategy, which leads to low and unstable efficiency. In this paper, we propose a novel log completeness type, that is, #TAR completeness, as well as its generation algorithm. The experimental results based on a series of randomly generated process models show that the #TAR complete logs outperform the state‐of‐the‐art ones with lower capacity, fuller dependencies covering and higher generating efficiency.

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Desire Lines in Big Data

Cited by 2 publications

References 15 publications

Object-Centric Replay-Based Conformance Checking: Unveiling Desire Lines and Local Deviations

Object-Centric Replay-Based Conformance Checking: Unveiling Desire Lines and Local Deviations

A novel completeness definition of event logs and corresponding generation algorithm

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