Detecting and Identifying Data Drifts in Process Event Streams Based on Process Histories

Stertz, Florian; Rinderle-Ma, Stefanie

doi:10.1007/978-3-030-21297-1_21

Cited by 18 publications

(13 citation statements)

References 7 publications

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“…In the e-commerce process example, it may no longer be required to attach the credit card image to execute the credit card validation activity. The proposals depicted in [32,59,68] explicitly deal with this perspective.…”

Section: 34mentioning

confidence: 99%

“…Despite the identified drawbacks, authors in [67] present an entirely distinct approach to detect drifts based on discovery and conformance for online environments. This work is extended in [68] to identify concept drifts from the data perspective, validated using a real-world dataset.…”

Section: 74mentioning

confidence: 99%

“…Another drawback is that the set of pre-defined templates describes changes in a low level of abstraction, e.g., adding an activity, which results in reporting a lot of low-level changes. The main consequence of the identified limitations is that the drift characterization method hardly works in a real-world environment [68].…”

Section: Change Localizationmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Concept Drift in Process Mining

et al. 2021

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Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in PM and bring forward a taxonomy of existing techniques for drift detection and online PM for evolving environments. Existing works depict that (i) PM still primarily focuses on offline analysis, and (ii) the assessment of concept drift techniques in processes is cumbersome due to the lack of common evaluation protocol, datasets, and metrics.

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Section: 34mentioning

confidence: 99%

Section: 74mentioning

confidence: 99%

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A Survey on Concept Drift in Process Mining

et al. 2021

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“…For example, [2] explores the changes of frequencies between different options when there is an exclusive choice in process models. Stertz et al [17] detects the change of event attribute values in business processes in an online setting. Brockho et al [20] applies the earth mover's distance to detect process drifts from both the control-flow and time perspective.…”

Section: Introductionmentioning

confidence: 99%

Detecting Branching Condition Changes in Process Models

Lu¹,

Chen²,

Poon³

2021

Preprint

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Business processes are continuously evolving in order to adapt to changes due to various factors. One important process drift perspective yet to be investigated is the detection of branching condition changes in the process model. None of the existing process drift detection methods focus on detecting changes of branching conditions in process models. Existing branching condition detection methods do not take changes within the process into account, hence results are inadequate to represent the changes of decision criteria of the process. In this paper, we present a method which can detect branching condition changes in process models. The method takes both process models and event logs as input, and translates event logs into decision sequences for change points detection. The proposed method is evaluated by simulated event logs.

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“…Traditional (offline) PM techniques cover three main 34 tasks: process discovery where a new model is inferred based 35 on the information contained in the event log; conformance 36 checking where a model is compared with the event log, to 37 analyze possible deviations; process enhancement where the model is updated to reach better performance results [1]. In recent years, researchers have achieved significant results in proposing adaptations to classic offline techniques to handle online processing, mainly for process discovery [5], [7], [8], [9], [10], [11], [12] and conformance checking [13], [14], [15], [16], [17], [18].…”

mentioning

confidence: 99%

Evaluation Goals for Online Process Mining: A Concept Drift Perspective

Ceravolo

Tavares²,

Barbon

et al. 2022

IEEE Trans. Serv. Comput.

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Online process mining refers to a class of techniques for analyzing in real-time event streams generated by the execution 5 of business processes. These techniques are crucial in the reactive monitoring of business processes, timely resource allocation and 6 detection/prevention of dysfunctional behavior. Many interesting advances have been made by the research community in recent 7 years, but there is no consensus on the exact set of properties these techniques have to achieve. This article fills the gap by identifying 8 a set of evaluation goals for online process mining and examining their fulfillment in the state of the art. We discuss parameters and 9 techniques regulating the balance between conflicting goals and outline research needed for their improvement. Concept drift detection 10 is crucial in this sense but, as demonstrated by our experiments, it is only partially supported by current solutions. Q1 11 Index Terms-Online process mining, event stream, requirements and goals, concept drift Ç 12 1 INTRODUCTION 13 P ROCESS Mining (PM) is a set of data science techniques 14 focused on the analysis of event logs [1]. Events are 15 recorded when executing a Business Process and collected 16 into cases, i.e., end to end sequences of events relevant to the 17 same process instance. Traditional PM algorithms were 18 designed to work offline, analyzing historical batches of logs 19 gathering the complete course of cases, if necessary with 20 multiple passes of analysis. This is, however, insufficient, 21 from a business standpoint, when the real-time assessment 22 of processes is crucial to timely manage resources and 23 quickly react to dysfunctional behaviors [2]. Today's fast-24 changing market requires systematic adjustments of pro-25 cesses in response to changes in the organization's operat-26 ing system or to trends emerging from the environment [3]. 27 Recently, the notion of online PM has emerged in reference 28 to analytics capable of handling real-time event streams [4], 29 [5]. An event stream differs from an event log because it is an 30 unbounded sequence of events ingested one-by-one and 31 allowing for limited actions in terms of iteration and memory 32 or time consumption [6].

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Detecting and Identifying Data Drifts in Process Event Streams Based on Process Histories

Cited by 18 publications

References 7 publications

A Survey on Concept Drift in Process Mining

A Survey on Concept Drift in Process Mining

Detecting Branching Condition Changes in Process Models

Evaluation Goals for Online Process Mining: A Concept Drift Perspective

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