Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes

Leontjeva, Anna; Conforti, Raffaele; Francescomarino, Chiara Di; Dumas, Marlon; Maggi, Fabrizio Maria

doi:10.1007/978-3-319-23063-4_21

Cited by 134 publications

(135 citation statements)

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“…Declarative process mining involves identifying possible execution paths for bridging event data reflecting the clinical reality and clinical guidelines describing best-practice. Two papers concerned the analysis of process anomalies and exceptions [33], [34], and some others concerned common pathways [35]- [38]. Problems and challenges of process characteristic issues and event log quality issues were well discussed in three papers [39]- [41].…”

Section: Resultsmentioning

confidence: 99%

“…The ProM toolkit is a defacto standard in process mining research community and can be combined with other tools, such as GATE developer, WordNet, R, R Studio, and Java [25], Tilde, Alchemy and BUSL [47]. Other papers proposed their own tool [36], [38], [40], [48]- [53]. In case studies other than oncology, process mining has also been implemented using the DISCO commercial tool (www.fluxicon.com/disco), such as in [54]- [56].…”

Section: Resultsmentioning

confidence: 99%

“…Data limitations were identified in ten papers [20], [22], [33], [36], [37], [44], [48], [57], [60] and were mostly related to limited access to the data, data quality problems, attributes not available in the data being extracted, or the dataset available in inappropriate level of details. Technique limitations were identified in 13 papers [3], [21], [28], [31], [35], [38]- [40], [48], [50], [52], [63], [64] specifically if the study undertaken was done by implementing plug-ins/ functionalities available in tools. Team limitations were identified in two papers [11], [23] because the authors realize that they need medical domain experts to be included in the research team.…”

Section: F Limitations and Future Workmentioning

confidence: 99%

“…Some papers identified data improvements [28]- [30], [36]- [38], [48], [57], [64] in terms of improving data quality, dimensionality, and complexity. A larger number of important technical improvements were identified in 20 papers [3], [21]- [23], [29]- [31], [35], [39]- [41], [44], [48]- [52], [58], [60], [64].…”

Section: F Limitations and Future Workmentioning

confidence: 99%

See 3 more Smart Citations

Process mining in oncology: A literature review

Kurniati

Johnson

Hogg³

et al. 2016

2016 6th International Conference on Information Communication and Management (ICICM)

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Abstract-Process mining, an emerging data analytics method, has been used effectively in various healthcare contexts including oncology, the study of cancer. Cancer is a complex disease with many complicated care requirements and there is an urgent need to improve the cost and clinical effectiveness of cancer care pathways. Process mining of the e-health records of cancer patients may play an important future role and this paper presents a literature review of process mining in oncology as a contribution to this research. The search produced 758 articles which were manually reviewed by title, abstract, and full paper text review to develop the original pool of papers. An in-depth ancestor search was used to gather additional articles from the references of the original pool. These steps resulted in 37 papers. Through a thematic review process, the papers were analysed and five themes emerged. These were: 1) process and data types; 2) research questions; 3) techniques, perspectives and tools; 4) methodologies; 5) limitations and future work. This review can: (i) highlight the potential value of process mining for improving cancer care processes (ii) provide a useful overview of the current work undertaken; (iii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches; and (iv) identify research opportunities in this new field of study.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: F Limitations and Future Workmentioning

confidence: 99%

Section: F Limitations and Future Workmentioning

confidence: 99%

See 2 more Smart Citations

Process mining in oncology: A literature review

Kurniati

Johnson

Hogg³

et al. 2016

2016 6th International Conference on Information Communication and Management (ICICM)

View full text Add to dashboard Cite

show abstract

“…Trace profiles Description [87] Trace clustering with standard distance measures [66] and [62] compare the predicitive performance for different lengths of prefixes, i.e., evaluate the impact of earliness of prediction. These ideas are combined in [92], who first cluster similar traces and then use complex symbolic sequence encodings of traces for binary classification.…”

Section: Source Applicationmentioning

confidence: 99%

Feature extraction from event logs for predictive monitoring of business processes

Borchert¹

2018

Preprint

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Many organizations record information about their IT-driven processes in some form of event logs, be it for auditing or debugging purposes or simply by accident. Obtaining knowledge from such event logs is the goal of process mining. Process mining techniques can be used to discover process models from event logs or relate existing process models and observed behaviour in various ways. While we usually envision process models to carry some executable semantics, like Petri net systems or BPMN models, we can also think of purely statistical models or a combination of both. Statistical models are particularly suited for predictive tasks such as early classification of a running case or anomaly detection. In the context of business process management, these activities are denoted as predictive monitoring.However, most standard machine learning and data mining algorithms cannot be directly applied to complex event logs. Some of the most widespread algorithms, like Support Vector Machines or decision tree learners, require the data to be mapped into a vector space first. To this end, meaningful features need to be extracted from event logs. Now, it is not obvious what good features in the domain of business processes are: events describe observed behaviour and are typically equipped with information about execution times and quite often also resources and other attributes. Business processes usually show a certain degree of structure in terms of subprocesses and workflow patterns, which is reflected in the logs. Furthermore, processes may allow for a parallel execution of activities, so that the order in which events are recorded can have different interpretations. Treating the traces in an event log simply as symbolic sequences of activities neglects all of these potentially useful perspectives.In this master's thesis, we investigate feature extraction techniques for event log traces. We provide an overview over the field of predictive monitoring and analyze existing trace profiles proposed in the literature. Based on this, we apply the results obtained in recent research on process discovery to find meaningful abstractions over related subsequences of events.We assess different feature sets by evaluating their predictive power in a supervised classification setting as well as a semi-supervised outlier detection setting. For this purpose, we use two datasets from public administration, describing complex processes in EU agricultural subsidy management. Our particular goal in this domain is to predict negative outcomes, for instance, additional work due to legal claims or corrections necessary after initial payment decisions. iv

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Predicting process performance: A white‐box approach based on process models

Verenich

Dumas

Rosa

et al. 2019

J Software Evolu Process

Self Cite

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Predictive business process monitoring methods exploit historical process execution logs to provide predictions about running instances of a process. These predictions enable process workers and managers to preempt performance issues or compliance violations. A number of approaches have been proposed to predict quantitative process performance indicators for running instances of a process, including remaining cycle time, cost, or probability of deadline violation. However, these approaches adopt a black-box approach, insofar as they predict a single scalar value without decomposing this prediction into more elementary components. In this paper, we propose a white-box approach to predict performance indicators of running process instances. The key idea is to first predict the performance indicator at the level of activities and then to aggregate these predictions at the level of a process instance by means of flow analysis techniques. The paper develops this idea in the context of predicting the remaining cycle time of ongoing process instances. The proposed approach has been evaluated on real-life event logs and compared against several baselines. KEYWORDS explainable artificial intelligence, flow analysis, predictive process monitoring, process mining, transparent models INTRODUCTIONPredictive business process monitoring techniques seek to predict the future state or properties of ongoing executions of a process based on models extracted from historical event logs. A wide range of predictive business process monitoring techniques have been proposed to predict, for example, compliance violations, 1,2 the next activity or the remaining sequence of activities of a process instance, 3,4 or quantitative process performance indicators, such the remaining cycle time of a process instance. 5-7 These predictions can be used to alert process workers to problematic process instances or to support resource allocation decisions, eg, to allocate additional resources to instances that are at risk of a deadline violation.This article addresses the problem of predicting quantitative process performance indicators, with a specific focus on predicting the remaining cycle time of ongoing process instances. Existing approaches to this problem adopt a "black-box" approach by building stochastic models or regression models which, given a process instance, predict the remaining execution time as a single scalar value, without seeking to explain this prediction in terms of more elementary components. Yet, quantitative performance indicators such as cost or time are aggregations of corresponding performance indicators of the activities composing the process. For example, the cycle time of a process instance with sequentially performed activities consists of the sum of the cycle time of the activities performed in that process instance. In this respect, existing known as flow analysis. 8 The idea of flow analysis is to estimate a quantitative performance indicator at the level of a process by aggregating the estimated values of this perfo...

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Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes

Cited by 134 publications

References 21 publications

Process mining in oncology: A literature review

Process mining in oncology: A literature review

Feature extraction from event logs for predictive monitoring of business processes

Predicting process performance: A white‐box approach based on process models

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