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 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 Inferring Unobserved Events in Systems with Shared Resources and Queues

2022 ◽  
Vol 183 (3-4) ◽  
pp. 203-242
Author(s):  
Dirk Fahland ◽  
Vadim Denisov ◽  
Wil. M.P. van der Aalst
Keyword(s):  
Material Handling ◽  
Real Life ◽  
Process Models ◽  
Shared Resources ◽  
Compositional Model ◽  
Event Logs ◽  
Event Log ◽  
Baggage Handling ◽  
Process Behavior ◽  
And Performance

To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies in performance. However, due to a variety of reasons, real-life systems often record only a subset of all events taking place. To understand and analyze the behavior and performance of processes with shared resources, we aim to reconstruct bounds for timestamps of events in a case that must have happened but were not recorded by inference over events in other cases in the system. We formulate and solve the problem by systematically introducing multi-entity concepts in event logs and process models. We introduce a partial-order based model of a multi-entity event log and a corresponding compositional model for multi-entity processes. We define PQR-systems as a special class of multi-entity processes with shared resources and queues. We then study the problem of inferring from an incomplete event log unobserved events and their timestamps that are globally consistent with a PQR-system. We solve the problem by reconstructing unobserved traces of resources and queues according to the PQR-model and derive bounds for their timestamps using a linear program. While the problem is illustrated for material handling systems like baggage handling systems in airports, the approach can be applied to other settings where recording is incomplete. The ideas have been implemented in ProM and were evaluated using both synthetic and real-life event logs.

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.

scholarly journals Discovering Block–Structured Parallel Process Models from Causally Complete Event Logs

2016 ◽  
Vol 67 (2) ◽  
pp. 111-123 ◽  
Author(s):  
Julijana Lekić ◽  
Dragan Milićev
Keyword(s):  
Process Model ◽  
Business Processes ◽  
Process Models ◽  
Business Process Models ◽  
Modified Method ◽  
Event Logs ◽  
Event Log ◽  
Partial Correction ◽  
Modified Algorithm ◽  
Block Structured

Abstract α-algorithm is suitable to discover a large class of workflow (WF) nets based on the behaviour recorded in event logs, with the main limiting assumption that the event log is complete. Our research has been aimed at finding ways of discovering business process models based on examples of traces, ie, logs of workflow actions that do not meet the requirement of completeness. In this aim, we have modified the existing and introduced a new relation between activities recorded in the event log, which has led to a partial correction of the process models discovering technique, including the α-algorithm. We have also introduced the notion of causally complete logs, from which our modified algorithm can produce the same result as the α-algorithm from complete logs. The effect of these modifications on the efficiency of the process model discovering is mostly evident for business processes in which many activities can be performed in parallel. The application of the modified method for discovering block-structured models of parallel business processes is presented in this paper.

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 Mining of Event Logs: A Case Study Evaluating Internal Control Effectiveness

2019 ◽  
Vol 33 (3) ◽  
pp. 141-156 ◽  
Author(s):  
Tiffany Chiu ◽  
Mieke Jans
Keyword(s):  
Internal Control ◽  
Process Mining ◽  
Real Life ◽  
Potential Control ◽  
Control Effectiveness ◽  
Event Logs ◽  
Event Log ◽  
New Type ◽  
Potential Risks

SYNOPSISThis paper aims at adopting process mining to evaluate the effectiveness of internal control using a real-life event log. Specifically, the evaluation is based on the full population of an event log and it contains four analyses: (1) variant analysis that identifies standard and non-standard variants, (2) segregation of duties analysis that examines whether employees violate segregation of duties controls, (3) personnel analysis that investigates whether employees are involved in multiple potential control violations, and (4) timestamp analysis that detects time-related issues including weekend activities and lengthy process duration. Results from the case study indicate that process mining could assist auditors in identifying audit-relevant issues such as non-standard variants, weekend activities, and personnel who are involved in multiple violations. Process mining enables auditors to detect potential risks, ineffective internal controls, and inefficient processes. Therefore, process mining generates a new type of audit evidence and could revolutionize the current audit procedure.

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 Process Discovery of Business Processes Using Temporal Causal Relation

2019 ◽  
Vol 5 (2) ◽  
pp. 183
Author(s):  
Yutika Amelia Effendi ◽  
Nania Nuzulita
Keyword(s):  
Business Process ◽  
Business Processes ◽  
Process Mining ◽  
Causal Relation ◽  
Real Life ◽  
Process Models ◽  
Time Interval ◽  
Start Time ◽  
Process Discovery ◽  
Event Log

Background: Nowadays, enterprise computing manages business processes which has grown up rapidly. This situation triggers the production of a massive event log. One type of event log is double timestamp event log. The double timestamp has a start time and complete time of each activity executed in the business process. It also has a close relationship with temporal causal relation. The temporal causal relation is a pattern of event log that occurs from each activity performed in the process.Objective: In this paper, seven types of temporal causal relation between activities were presented as an extended version of relations used in the double timestamp event log. Since the event log was not always executed sequentially, therefore using temporal causal relation, the event log was divided into several small groups to determine the relations of activities and to mine the business process.Methods: In these experiments, the temporal causal relation based on time interval which were presented in Gantt chart also determined whether each case could be classified as sequential or parallel relations. Then to obtain the business process, each temporal causal relation was combined into one business process based on the timestamp of activity in the event log.Results: The experimental results, which were implemented in two real-life event logs, showed that using temporal causal relation and double timestamp event log could discover business process models.Conclusion: Considering the findings, this study concludes that business process models and their sequential and parallel AND, OR, XOR relations can be discovered by using temporal causal relation and double timestamp event log.Keywords:Business Process, Process Discovery, Process Mining, Temporal Causal Relation, Double Timestamp Event Log

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