scholarly journals Discovering Object-centric Petri Nets

2020 ◽  
Vol 175 (1-4) ◽  
pp. 1-40
Author(s):  
Wil M.P. van der Aalst ◽  
Alessandro Berti
Keyword(s):  
Petri Nets ◽  
Petri Net ◽  
Single Case ◽  
Real Life ◽  
Process Models ◽  
Event Data ◽  
Multiple Products ◽  
Event Logs ◽  
Different Types ◽  
Complex Relationships

Techniques to discover Petri nets from event data assume precisely one case identifier per event. These case identifiers are used to correlate events, and the resulting discovered Petri net aims to describe the life-cycle of individual cases. In reality, there is not one possible case notion, but multiple intertwined case notions. For example, events may refer to mixtures of orders, items, packages, customers, and products. A package may refer to multiple items, multiple products, one order, and one customer. Therefore, we need to assume that each event refers to a collection of objects, each having a type (instead of a single case identifier). Such object-centric event logs are closer to data in real-life information systems. From an object-centric event log, we want to discover an object-centric Petri net with places that correspond to object types and transitions that may consume and produce collections of objects of different types. Object-centric Petri nets visualize the complex relationships among objects from different types. This paper discusses a novel process discovery approach implemented in PM4Py. As will be demonstrated, it is indeed feasible to discover holistic process models that can be used to drill-down into specific viewpoints if needed.

scholarly journals Discovering Object-centric Petri Nets

2020 ◽  
Author(s):  
Wil M.P. van der Aalst ◽  
Alessandro Berti
Keyword(s):  
Petri Nets ◽  
Petri Net ◽  
Single Case ◽  
Real Life ◽  
Process Models ◽  
Multiple Products ◽  
Event Logs ◽  
Event Log ◽  
Different Types ◽  
Complex Relationships

Techniques to discover Petri nets from event data assume precisely one case identifier per event. These case identifiers are used to correlate events, and the resulting discovered Petri net aims to describe the life-cycle of individual cases. In reality, there is not one possible case notion, but multiple intertwined case notions. For example, events may refer to mixtures of orders, items, packages, customers, and products. A package may refer to multiple items, multiple products, one order, and one customer. Therefore, we need to assume that each event refers to a collection of objects, each having a type (instead of a single case identifier). Such object-centric event logs are closer to data in real-life information systems. From an object-centric event log, we want to discover an object-centric Petri net with places that correspond to object types and transitions that may consume and produce collections of objects of different types. Object-centric Petri nets visualize the complex relationships among objects from different types. This paper discusses a novel process discovery approach implemented in PM4Py. As will be demonstrated, it is indeed feasible to discover holistic process models that can be used to drill-down into specific viewpoints if needed.

scholarly journals Cross-Organization Emergency Response Process Mining: An Approach Based on Petri Nets

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Cong Liu ◽  
Huiling Li ◽  
Qingtian Zeng ◽  
Ting Lu ◽  
Caihong Li
Keyword(s):  
Petri Nets ◽  
Emergency Response ◽  
Resource Sharing ◽  
Process Mining ◽  
Process Models ◽  
Response Process ◽  
Extensive Experience ◽  
Event Logs ◽  
Collaboration Patterns ◽  
Sharing Patterns

To support effective emergency disposal, organizations need to collaborate with each other to complete the emergency mission that cannot be handled by a single organization. In general, emergency disposal that involves multiple organizations is typically organized as a group of interactive processes, known as cross-organization emergency response processes (CERPs). The construction of CERPs is a time-consuming and error-prone task that requires practitioners to have extensive experience and business background. Process mining aims to construct process models by analyzing event logs. However, existing process mining techniques cannot be applied directly to discover CERPs since we have to consider the complexity of various collaborations among different organizations, e.g., message exchange and resource sharing patterns. To tackle this challenge, a CERP model mining method is proposed in this paper. More specifically, we first extend classical Petri nets with resource and message attributes, known as resource and message aware Petri nets (RMPNs). Then, intra-organization emergency response process (IERP) models that are represented as RMPNs are discovered from emergency drilling event logs. Next, collaboration patterns among emergency organizations are formally defined and discovered. Finally, CERP models are obtained by merging IERP models and collaboration patterns. Through comparative experimental evaluation using the fire emergency drilling event log, we illustrate that the proposed approach facilitates the discovery of high-quality CERP models than existing state-of-the-art approaches.

scholarly journals The RALph miner for automated discovery and verification of resource-aware process models

2020 ◽  
Vol 19 (6) ◽  
pp. 1415-1441
Author(s):  
Cristina Cabanillas ◽  
Lars Ackermann ◽  
Stefan Schönig ◽  
Christian Sturm ◽  
Jan Mendling
Keyword(s):  
Process Model ◽  
Model Verification ◽  
Process Models ◽  
Resource Assignment ◽  
Process Discovery ◽  
Event Logs ◽  
Different Types ◽  
Automated Discovery ◽  
And Performance ◽  
Resource Aware

Abstract Automated process discovery is a technique that extracts models of executed processes from event logs. Logs typically include information about the activities performed, their timestamps and the resources that were involved in their execution. Recent approaches to process discovery put a special emphasis on (human) resources, aiming at constructing resource-aware process models that contain the inferred resource assignment constraints. Such constraints can be complex and process discovery approaches so far have missed the opportunity to represent expressive resource assignments graphically together with process models. A subsequent verification of the extracted resource-aware process models is required in order to check the proper utilisation of resources according to the resource assignments. So far, research on discovering resource-aware process models has assumed that models can be put into operation without modification and checking. Integrating resource mining and resource-aware process model verification faces the challenge that different types of resource assignment languages are used for each task. In this paper, we present an integrated solution that comprises (i) a resource mining technique that builds upon a highly expressive graphical notation for defining resource assignments; and (ii) automated model-checking support to validate the discovered resource-aware process models. All the concepts reported in this paper have been implemented and evaluated in terms of feasibility and performance.

Modeling With Colored Petri Nets

2021 ◽  
pp. 397-424
Author(s):  
Dmitry A. Zaitsev ◽  
Tatiana R. Shmeleva
Keyword(s):  
Performance Evaluation ◽  
Petri Nets ◽  
Petri Net ◽  
Real Life ◽  
Colored Petri Nets ◽  
Correctness Proof ◽  
Model Driven ◽  
Cpn Tools ◽  
Complex Models ◽  
And Performance

Aviation and aerospace systems are complex and concurrent and require special tools for their specification, verification, and performance evaluation. The tool in demand should be easily integrated into the standard loop of model-driven development. Colored Petri nets represent a combination of a Petri net graph and a functional programming language ML that makes it powerful and convenient tool for specification of real-life system and solving both tasks: correctness proof i.e. verification and performance evaluation. This chapter studies basic and advanced features of CPN Tools – a powerful modeling system which uses graphical language of colored Petri nets. Starting with a concept of colored hierarhical timed Petri net, it goes through declaration of color sets and functions to peculiarities of hierarchical design of complex models and specification of timed characteristics. The authors accomplish the chapter with a real-life case study of performance evaluation for switched Ethernet network.

Process discovery and Petri nets

2009 ◽  
Vol 19 (6) ◽  
pp. 1091-1124 ◽  
Author(s):  
NADIA BUSI ◽  
G. MICHELE PINNA
Keyword(s):  
Petri Nets ◽  
Petri Net ◽  
Process Model ◽  
Process Mining ◽  
Model Design ◽  
Abstract Representation ◽  
Process Discovery ◽  
Event Logs ◽  
Mining Methods ◽  
Research Domain

The aim of the research domain known as process mining is to use process discovery to construct a process model as an abstract representation of event logs. The goal is to build a model (in terms of a Petri net) that can reproduce the logs under consideration, and does not allow different behaviours compared with those shown in the logs. In particular, process mining aims to verify the accuracy of the model design (represented as a Petri net), basically checking whether the same net can be rediscovered. However, the main mining methods proposed in the literature have some drawbacks: the classical α-algorithm is unable to rediscover various nets, while the region-based approach, which can mine them correctly, is too complex.In this paper, we compare different approaches and propose some ideas to counter the weaknesses of the region-based approach.

scholarly journals A New Modular Petri Net for Modeling Large Discrete-Event Systems: A Proposal Based on the Literature Study

Computers ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 83 ◽  
Author(s):  
Reggie Davidrajuh
Keyword(s):  
Petri Nets ◽  
Petri Net ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Real Life ◽  
World Systems ◽  
Literature Study ◽  
State Spaces ◽  
Event Systems ◽  
And Performance

Petri net is a highly useful tool for modeling of discrete-event systems. However, Petri net models of real-life systems are enormous, and their state-spaces are usually of infinite size. Thus, performing analysis on the model becomes difficult. Hence, slicing of Petri Net is suggested to reduce the size of the Petri nets. However, the existing slicing algorithms are ineffective for real-world systems. Therefore, there is a need for alternative methodologies for slicing that are effective for Petri net models of large real-life systems. This paper proposes a new Modular Petri Net as a solution. In modular Petri net, large Petri net models are decomposed into modules. These modules are compact, and the state spaces of these modules are also compact enough to be exhaustively analyzed. The research contributions of this paper are the following: Firstly, an exhaustive literature study is done on Modular Petri Nets. Secondly, from the conclusions drawn from the literature study, a new Petri net is proposed that supports module composition with clearly defined syntax. Thirdly, the new Petri net is implemented in the software GPenSIM, which is crucial so that real-life discrete-event systems could be modeled, analyzed, and performance-optimized with GPenSIM.

scholarly journals Automated Repair of Process Models with Non-local Constraints Using State-Based Region Theory

2022 ◽  
Vol 183 (3-4) ◽  
pp. 293-317
Author(s):  
Anna Kalenkova ◽  
Josep Carmona ◽  
Artem Polyvyanyy ◽  
Marcello La Rosa
Keyword(s):  
Free Choice ◽  
State Of The Art ◽  
Real Life ◽  
Process Models ◽  
Choice Process ◽  
Process Discovery ◽  
Event Logs ◽  
Novel Approach ◽  
Non Local ◽  
Region Theory

State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs.

scholarly journals Detecting Complex Control-Flow Constructs for Choosing Process Discovery Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 1389-1393
Keyword(s):  
Business Process ◽  
Free Choice ◽  
Process Mining ◽  
Control Flow ◽  
Process Models ◽  
Event Data ◽  
Process Discovery ◽  
Event Logs ◽  
Complex Process ◽  
Mining Algorithms

Process models are the analytical illustration of an organization’s activity. They are very primordial to map out the current business process of an organization, build a baseline of process enhancement and construct future processes where the enhancements are incorporated. To achieve this, in the field of process mining, algorithms have been proposed to build process models using the information recorded in the event logs. However, for complex process configurations, these algorithms cannot correctly build complex process structures. These structures are invisible tasks, non-free choice constructs, and short loops. The ability of each discovery algorithm in discovering the process constructs is different. In this work, we propose a framework responsible of detecting from event logs the complex constructs existing in the data. By identifying the existing constructs, one can choose the process discovery techniques suitable for the event data in question. The proposed framework has been implemented in ProM as a plugin. The evaluation results demonstrate that the constructs can correctly be identified.

Modeling With Colored Petri Nets

2019 ◽  
pp. 378-404
Author(s):  
Dmitry A. Zaitsev ◽  
Tatiana R. Shmeleva
Keyword(s):  
Performance Evaluation ◽  
Petri Nets ◽  
Petri Net ◽  
Real Life ◽  
Colored Petri Nets ◽  
Correctness Proof ◽  
Model Driven ◽  
Cpn Tools ◽  
Complex Models ◽  
And Performance

Aviation and aerospace systems are complex and concurrent and require special tools for their specification, verification, and performance evaluation. The tool in demand should be easily integrated into the standard loop of model-driven development. Colored Petri nets represent a combination of a Petri net graph and a functional programming language ML that makes it powerful and convenient tool for specification of real-life system and solving both tasks: correctness proof i.e. verification and performance evaluation. This chapter studies basic and advanced features of CPN Tools – a powerful modeling system which uses graphical language of colored Petri nets. Starting with a concept of colored hierarhical timed Petri net, it goes through declaration of color sets and functions to peculiarities of hierarchical design of complex models and specification of timed characteristics. The authors accomplish the chapter with a real-life case study of performance evaluation for switched Ethernet network.

scholarly journals Event Log Preprocessing for Process Mining: A Review

2021 ◽  
Vol 11 (22) ◽  
pp. 10556
Author(s):  
Heidy M. Marin-Castro ◽  
Edgar Tello-Leal
Keyword(s):  
Business Process ◽  
Process Model ◽  
Process Mining ◽  
Data Cleaning ◽  
Data Preprocessing ◽  
Process Models ◽  
Event Data ◽  
Event Logs ◽  
Event Log ◽  
Preprocessing Technique

Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing reference model of the same process (conformance), and to detect issues in the executed process to improve (enhancement). An essential element in the three tasks of process mining (discovery, conformance, and enhancement) is data cleaning, used to reduce the complexity inherent to real-world event data, to be easily interpreted, manipulated, and processed in process mining tasks. Thus, new techniques and algorithms for event data preprocessing have been of interest in the research community in business process. In this paper, we conduct a systematic literature review and provide, for the first time, a survey of relevant approaches of event data preprocessing for business process mining tasks. The aim of this work is to construct a categorization of techniques or methods related to event data preprocessing and to identify relevant challenges around these techniques. We present a quantitative and qualitative analysis of the most popular techniques for event log preprocessing. We also study and present findings about how a preprocessing technique can improve a process mining task. We also discuss the emerging future challenges in the domain of data preprocessing, in the context of process mining. The results of this study reveal that the preprocessing techniques in process mining have demonstrated a high impact on the performance of the process mining tasks. The data cleaning requirements are dependent on the characteristics of the event logs (voluminous, a high variability in the set of traces size, changes in the duration of the activities. In this scenario, most of the surveyed works use more than a single preprocessing technique to improve the quality of the event log. Trace-clustering and trace/event level filtering resulted in being the most commonly used preprocessing techniques due to easy of implementation, and they adequately manage noise and incompleteness in the event logs.

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