Overlapping Analytic Stages in Online Process Mining

Tavares, Gabriel Marques; Ceravolo, Paolo; Costa, Victor G. Turrisi da; Damiani, Ernesto; Barbon, Sylvio

doi:10.1109/scc.2019.00037

Cited by 19 publications

(29 citation statements)

References 20 publications

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“…Tavares et al [Tavares et al 2019] proposed an online framework to deal with concept drifts, anomaly detection and process monitoring. The approach relies on a graph-based representation of the business process, which is updated with new events from the stream.…”

Section: Concept Drift Event Stream Frameworkmentioning

confidence: 99%

“…As these methods are not embedded in PM software, it is noticeable that they usually do not have a user interface, which decreases their spread among non-expert users. To enhance the comparison proposed in this paper, we selected [Zheng et al 2017] and [Tavares et al 2019] since they are available as open-source and can be used in this work.…”

Section: Introductionmentioning

confidence: 99%

“…Tavares et al [Tavares et al 2019] presented an online technique for detecting drifts. The Concept Drift in Event Stream Framework (CDESF) joins different PM tasks in an online environment.…”

Section: Introductionmentioning

confidence: 99%

“…The improvement over the original work proposes the addition of two upgrades: the Process Control System event log was extended with one year worth of recordings, and two more approaches were added to the comparison, TPCDD [Zheng et al 2017] and CDESF [Tavares et al 2019]. The new approaches follow more academic standards, which might leverage concept drift detection at the cost of not offering as much support for non-expert users.…”

Section: Introductionmentioning

confidence: 99%

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Comparing Concept Drift Detection with Process Mining Software

Omori

Tavares

Ceravolo

et al. 2020

iSys

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Organisations have seen a rise in the volume of data corresponding to business processes being recorded. Handling process data is a meaningful way to extract relevant information from business processes with impact on the company's values. Nonetheless, business processes are subject to changes during their executions, adding complexity to their analysis. This paper aims at evaluating currently available process mining tools and software that handle concept drifts, i.e. changes over time of the statistical properties of the events occurring in a process. We provide an in-depth analysis of these tools, comparing their differences, advantages, and disadvantages by testing against a log taken from a Process Control System. Thus, by highlighting the trade-off between the software, the paper gives the stakeholders the best options regarding their case use.

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Section: Concept Drift Event Stream Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparing Concept Drift Detection with Process Mining Software

Omori

Tavares

Ceravolo

et al. 2020

iSys

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“…A major goal related to online PM is to get a real-time response over executed activities, minimizing the latency of reaction to deviant behavior. This requires inspecting incoming events quickly and incrementally, ideally event by event in one pass, still, a few incremental algorithms are available in the literature [10], [19]. In fact, the offline PM algorithms presuppose complete cases and it may be hard to convert them into incremental procedures [20].…”

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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Analysis of Language Inspired Trace Representation for Anomaly Detection

Tavares

BarbonJr.

2020

ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium

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A great concern for organizations is to detect anomalous process instances within their business processes. For that, conformance checking performs model-aware analysis by comparing process logs to business models for the detection of anomalous process executions. However, in several scenarios, a model is either unavailable or its generation is costly, which requires the employment of alternative methods to allow a confident representation of traces. This work supports the analysis of language inspired process analysis grounded in the word2vec encoding algorithm. We argue that natural language encodings correctly model the behavior of business processes, supporting a proper distinction between common and anomalous behavior. In the experiments, we compared accuracy and time cost among different word2vec setups and classic encoding methods (token-based replay and alignment features), addressing seven different anomaly scenarios. Feature importance values and the impact of different anomalies in seven event logs were also evaluated to bring insights on the trace representation subject. Results show the proposed encoding overcomes representational capability of traditional conformance metrics for the anomaly detection task.

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Overlapping Analytic Stages in Online Process Mining

Cited by 19 publications

References 20 publications

Comparing Concept Drift Detection with Process Mining Software

Comparing Concept Drift Detection with Process Mining Software

Evaluation Goals for Online Process Mining: A Concept Drift Perspective

Analysis of Language Inspired Trace Representation for Anomaly Detection

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