Activity prediction in process mining using the WoMan framework

Ferilli, Stefano; Angelastro, Sergio

doi:10.1007/s10844-019-00543-2

Cited by 10 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Francescomarino combined machine learning approaches for clustering and classification, in order to classify new traces during their execution to predict how they will behave in the future [8]. Ferilli and Angelastro use a heuristic strategy and a Naive Bayes approach [9]. Le combines a sequential k-nearest neighbor classification with an extension of Markov models [10].…”

Section: A Process Modelsmentioning

confidence: 99%

Adjacency Matrix Deep Learning Prediction Model for Prognosis of the Next Event in a Process

Razo

Darabi

2023

IEEE Access

View full text Add to dashboard Cite

Prediction of the next event is important for organizations to improve and optimize their system process to achieve organizational goals. Existing predictive models are limited since they use discovery algorithms that might not be able to conserve the sequences of events as reported in the event log. Discovery algorithms alter the sequence of events in two ways, either the algorithms generate an additional sequence of events not found in the event logs or remove the order of events. Since prediction relies on these process algorithms, the prediction model can suffer and produce underperforming results. Models that do not use discovery algorithms, such as deep learning models, ignore completely the sequence of events. To overcome these limitations, we propose a new algorithm called AXDP (Adjacency Matrix Deep Learning Prediction Model). AXDP predicts the next event of a process using graph theory techniques, specifically adjacency matrices and predicts using the power of deep learning models. AXDP has a major advantage, in that sequence of events is conserved, resulting in better prediction of the next event. When testing AXDP on eight publicly available datasets, AXDP outperforms what we believe to be the most recent and best predictive models that exist for the prediction of the next event for six of the eight datasets.INDEX TERMS Deep learning, process mining, event logs, adjacency matrix, max eigenvalues.

show abstract

Section: A Process Modelsmentioning

confidence: 99%

Adjacency Matrix Deep Learning Prediction Model for Prognosis of the Next Event in a Process

Razo

Darabi

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The WoMan framework [69][70][71] is a logic-based framework for Process Mining and Management. It is based on First-Order Logic (FOL) descriptions, ensuring understandable models and behavior.…”

Section: Process Mining For Automatic (Urban or Suburban) Behavioral ...mentioning

confidence: 99%

An Interpretable and Explainable AI Framework for Urban-Suburban Traffic Analysis and Understanding

Ferilli¹,

Pierro²,

Redavid³

et al. 2023

Preprint

View full text Add to dashboard Cite

Studying and understanding the behavior of people and vehicles on public roads can be of utmost importance for supporting the activities of many institutional stakeholders. It may allow automated supervision of the ongoing situation in a given place, with warnings or alarms raised in case of anomalies. It may be used to plan their interventions on road and town organization. It may provide them with advanced support to decision-making. The number of involved entities and places to manage makes it infeasible to manually handle all the traffic-related tasks. Moreover, the complexity of the tasks to be carried out requires the adoption of advanced approaches. Many AI solutions are nowadays mature to support these requirements. In some cases, the motivations and objectives of traffic management require the AI outcomes to be understandable, interpretable and explainable. In this paper, we propose TrAnSIT (TRaffic ANalysis Supervision and Interpretation Tool), an AI-based framework that combines several modules, each aimed at tackling a specific traffic-related task, so as to cover a wide landscape of traffic-related issues, from overall urban or suburban traffic management to surveying specific road segments that fall under the scope of one camera. Most of these modules are based on AI techniques that support a human-level understanding of the outcomes.

show abstract

“…Process mining can also be used together with a big data analysis procedure to structure the knowledge hidden in more irregular data sources, such as the text of emails (Yang et al, 2014). The generation of a model enables prediction capabilities: given the state of execution of a process, knowing how the situation might evolve allows the supervisors to take the proper corrective actions (Ferilli & Angelastro, 2019).…”

Section: To-be Analysis (Information-based Model Generation)mentioning

confidence: 99%

Exploiting a combined process mining approach to enhance the discovery and analysis of support processes in manufacturing

Lugaresi

Ciappina

Rossi

et al. 2022

International Journal of Computer Integrated Manufacturing

View full text Add to dashboard Cite

Recently, production plants have become very complex environments, in which the final output is the result of the favourable interplay of several processes. Successful management of such domains strictly depends on the ability to grasp the current disposition of both the physical and the managerial processes. To achieve this goal, the use of structured data-based methods has proven to be very effective. Yet, literature lacks successful applications, especially regarding production support processes (e.g., order acquisition, procure-to-pay, product development), which are directly connected to the overall system performance. This work proposes an approach to enabling automated mapping and controlling of production support processes starting from execution data recorded by IT systems. The limitations of existing methodologies are addressed by exploiting the combined application of two process mining algorithms: heuristic and inductive miner. This approach's managerial implications are described within the application to a real case study, in which the main phases of the procure-to-pay process (P2P) of a manufacturing company are identified and analysed automatically. The proposed approach proves its effectiveness in the context of application. The numerical results demonstrate that process mining can effectively bring tangible benefits in terms of viable improvements not only to physical production processes, but also to information flows and production support processes that are highly crucial for guaranteeing the prosperity of an enterprise, yet extremely hard to manage and control.

show abstract

Activity prediction in process mining using the WoMan framework

Cited by 10 publications

References 24 publications

Adjacency Matrix Deep Learning Prediction Model for Prognosis of the Next Event in a Process

Adjacency Matrix Deep Learning Prediction Model for Prognosis of the Next Event in a Process

An Interpretable and Explainable AI Framework for Urban-Suburban Traffic Analysis and Understanding

Exploiting a combined process mining approach to enhance the discovery and analysis of support processes in manufacturing

Contact Info

Product

Resources

About