A hybrid model for business process event and outcome prediction

Le, Mai; Gabrys, Bogdan; Nauck, Detlef

doi:10.1111/exsy.12079

Cited by 16 publications

(11 citation statements)

References 19 publications

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“…Process mining techniques focus on extracting knowledge from the event data [4]. Considering the complexity and level of divergence in the real-world business environment, process mining has emerged as a solution of several process analytic techniques [6], such as process discovery to explore the process data through modelling and analysing the behaviour of events in the log [7], monitor the process to analyse the conformance of the extracted process data with the models generated as a result of the process discovery and enhance the quality of original process through extracted knowledge [8]. In 2015, Hompes et al proposed a generic framework for process discovery based on Markov Cluster algorithm [6].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Once done, further exploration of the log is more straightforward and can follow traditional process mining steps [5]. Things become more complicated when the process operates in an established real-world business environment and adjustments towards achieving business goals tend to change the process regularly [6]. This change is normally brought about by human intervention and is mostly for specific cases, for example, changing the pre-requisites of a course for specific students in an academic environment, or allow students to apply for medical leave through an online portal where the process normally expects a hard copy of the doctor's prescription.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, process discovery is performed by using a multi-stage framework through a proposed algorithm Novel Hierarchical Clustering (NoHiC) (Algorithm 1) that combines the methodologies of both, process modelling and unsupervised machine learning. Techniques such as Rules-based mining [6] and hierarchical clustering of the process instances [6] are proposed for simplification of large and complex real-world business processes, which are described in this paper as contrail processes (Section 3.1). We used process modelling techniques, such as fuzzy mining and heuristic mining, for analysing the fitness criteria of an event log [7].…”

Section: Introductionmentioning

confidence: 99%

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Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework

Tariq

Khan²,

Charles

et al. 2020

Algorithms

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Real-world business processes are dynamic, with event logs that are generally unstructured and contain heterogeneous business classes. Process mining techniques derive useful knowledge from such logs but translating them into simplified and logical segments is crucial. Complexity is increased when dealing with business processes with a large number of events with no outcome labels. Techniques such as trace clustering and event clustering, tend to simplify the complex business logs but the resulting clusters are generally not understandable to the business users as the business aspects of the process are not considered while clustering the process log. In this paper, we provided a multi-stage hierarchical framework for business-logic driven clustering of highly variable process logs with extensively large number of events. Firstly, we introduced a term contrail processes for describing the characteristics of such complex real-world business processes and their logs presenting contrail-like models. Secondly, we proposed an algorithm Novel Hierarchical Clustering (NoHiC) to discover business-logic driven clusters from these contrail processes. For clustering, the raw event log is initially decomposed into high-level business classes, and later feature engineering is performed exclusively based on the business-context features, to support the discovery of meaningful business clusters. We used a hybrid approach which combines rule-based mining technique with a novel form of agglomerative hierarchical clustering for the experiments. A case-study of a CRM process of the UK’s renowned telecommunication firm is presented and the quality of the proposed framework is verified through several measures, such as cluster segregation, classification accuracy, and fitness of the log. We compared NoHiC technique with two trace clustering techniques using two real world process logs. The discovered clusters through NoHiC are found to have improved fitness as compared to the other techniques, and they also hold valuable information about the business context of the process log.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework

Tariq

Khan²,

Charles

et al. 2020

Algorithms

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“…Data mining and machine learning techniques have been applied in various application domains for knowledge discovery and business intelligence (Gupta, Ahuja, Malhotra, Bala, & Kaur, 2017;Kim et al, 2017;Le, Gabrys, & Nauck, 2017;Lee et al, 2017;Márquez-Vera et al, 2016;Moro, Cortez, & Rita, 2018). Machine learning techniques have also been compared for customer churn prediction (Vafeiadis, Diamantaras, Sarigiannidis, & Chatzisavvas, 2015).…”

Section: Customer Churn Predictionmentioning

confidence: 99%

Churn prediction and social neighbour influences for different types of user groups in virtual worlds

et al. 2019

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In virtual worlds (VWs), users have more VW games alternatives, whereas VW companies consequently suffer from high customer turnover rate and low customer loyalty. Therefore, building a churn prediction model to facilitate subsequent churn management and customer retention is important. The churn behaviours and the impact of social neighbour influences to customer churn may be different for different types of users. Accordingly, we segment users into stable, unstable, and solitary user groups according to their social contact behaviours in VWs. Novel segmentation‐based churn prediction approaches are proposed for churn prediction in VWs by building prediction models for each type of user groups and considering the effect of social neighbour influences for different user groups. The proposed approaches are evaluated by conducting experiments with a dataset collected from a VW platform. The experimental results show different churn prediction performances under different user groups. The segmentation‐based churn prediction approaches perform better than do general approaches without considering user groups. Moreover, the results also reveal that social neighbour influences have a positive impact on stable and unstable users. The proposed work contributes to investigating the social neighbour influences on churn prediction for different types of user groups in VWs.

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“…Experiments were conducted on records of processes obtained from a telecommunication company. An approach by Le et al (2017) uses sequential k-nearest neighbor classification and an extension of Markov models to predict the next process steps by considering temporal features. Using the same process log data as Le et al (2014), they showed the superiority of this model over Markov and Hidden Markov Models (HMM).…”

Section: Related Workmentioning

confidence: 99%

A Novel Business Process Prediction Model Using a Deep Learning Method

2018

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The ability to proactively monitor business processes is a main competitive differentiator for firms. Process execution logs generated by process aware information systems help to make process specific predictions for enabling a proactive situational awareness. The goal of the proposed approach is to predict the next process event from the completed activities of the running process instance, based on the execution log data from previously completed process instances. By predicting process events, companies can initiate timely interventions to address undesired deviations from the desired workflow. The paper proposes a multi-stage deep learning approach that formulates the next event prediction problem as a classification problem. Following a feature pre-processing stage with n-grams and feature hashing, a deep learning model consisting of an unsupervised pre-training component with stacked autoencoders and a supervised fine-tuning component is applied. Experiments on a variety of business process log datasets show that the multi-stage deep learning approach provides promising results. The study also compared the results to existing deep recurrent neural networks and conventional classification approaches. Furthermore, the paper addresses the identification of suitable hyperparameters for the proposed approach, and the handling of the imbalanced nature of business process event datasets.

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A hybrid model for business process event and outcome prediction

Cited by 16 publications

References 19 publications

Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework

Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework

Churn prediction and social neighbour influences for different types of user groups in virtual worlds

A Novel Business Process Prediction Model Using a Deep Learning Method

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