Predictive Process Monitoring related to the remaining time dimension: a value-driven framework

Lamghari, Zineb; Radgui, Maryam; Saidi, Rajaa; Rahmani, Moulay Driss

doi:10.1109/icssd47982.2019.9002939

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The PPM research pillar includes 22 surveys [71,72,116,141,154,157,160,167,207,211,216,222,247,256,273,275,281,286,290,297,316,318]. Similar to the surveys in the remaining event prediction pillar, the scope of the existing 22 surveys is limited.…”

Section: Related Surveysmentioning

confidence: 99%

A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas

Benzin¹,

Rinderle-Ma²

2023

Preprint

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Event prediction is the ability of anticipating future events, i.e., future real-world occurrences, and aims to support the user in deciding on actions that change future events towards a desired state. An event prediction method learns the relation between features of past events and future events. It is applied to newly observed events to predict corresponding future events that are evaluated with respect to the user's desired future state. If the predicted future events do not comply with this state, actions are taken towards achieving desirable future states. Evidently, event prediction is valuable in many application domains such as business and natural disasters.The diversity of application domains results in a diverse range of methods that are scattered across various research areas which, in turn, use different terminology for event prediction methods. Consequently, sharing methods and knowledge for developing future event prediction methods is restricted. To facilitate knowledge sharing on account of a comprehensive classification, integration, and assessment of event prediction methods, we combine taxonomies and take a systems perspective to integrate event prediction methods into a single system, elicit requirements and assess existing work with respect to the requirements. Based on the assessment, we identify open challenges and discuss future research directions.

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Section: Related Surveysmentioning

confidence: 99%

A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas

Benzin¹,

Rinderle-Ma²

2023

Preprint

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“…In this sense, we cannot provide a comprehensive overview of the whole prediction techniques. Therefore, we select one predictive method, related to the remaining time dimension, from the integrated list, published in [18,19,20]. Among this list, we find this method [21].…”

Section: Predictive Business Process Modelmentioning

confidence: 99%

An operational support approach for Mining Unstructured Business Processes

Lamghari

Radgui

Saidi

et al. 2021

RITA

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The refined process mining framework contains a set of activities that use extracted information from event logs, discovered models and normative ones. Among these activities, we find those dealing with running events in a Structured Business Process (SBP) context, which are the Detect, the Predict and the Recommend activities. These three activities are nominated as an operational support system that aims at detecting deviations, predicting events and recommending actions. In this regard, operational support systems perform well on SBP while, it stills a challenging task for an Unstructured Business Process (UBP). This puts forward the difficulty of predicting events and recommending actions for UBP, because of its complex structure. In this context, simplification and structuring operations must be applied. Therefore, the intervention of other process mining activities is required for business process simplification and structuring. To this end, we present an operational support approach dealing with UBP, using the refined process mining framework activities.

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Data-Driven Identification and Analysis of Waiting Times in Business Processes

Ali,

Milani,

Dumas

2024

Bus Inf Syst Eng

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Reducing waiting times in end-to-end business processes is a recurrent concern in the field of business process management. The uptake of data-driven approaches in this field in the past two decades, most notably process mining, has created new opportunities for fine-grained analysis of waiting times based on execution data. As a result, a wide range of approaches for waiting time identification and analysis on the basis of business process execution data have been reported in the literature. In many instances, different approaches have considered different notions of waiting time and different causes for waiting time. At present, there is a lack of a consolidated overview of these manifold approaches, and how they relate to or complement each other. The article presents a literature review that starts with the question of what approaches for identification and analysis of waiting time are available in the literature, and then refines this question by adding questions which shed light onto different causes and notions of waiting time. The survey leads to a multidimensional taxonomy of data-driven waiting time analysis techniques, in terms of purpose, causes, and measures. The survey identifies gaps in the field, chiefly a scarcity of integrated multi-causal approaches to analyze waiting times in business processes, and a lack of empirically validated approaches in the field.

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Predictive Process Monitoring related to the remaining time dimension: a value-driven framework

Cited by 3 publications

References 21 publications

A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas

A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas

An operational support approach for Mining Unstructured Business Processes

Data-Driven Identification and Analysis of Waiting Times in Business Processes

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