Monitoring Constraints in Business Processes Using Object-Centric Constraint Graphs

• URL: http://arxiv.org/abs/2210.12080v1
• Date: Fri, 21 Oct 2022 16:11:29 GMT
• Title: Monitoring Constraints in Business Processes Using Object-Centric Constraint Graphs
• Authors: Gyunam Park and Wil. M. P. van der Aalst
• Abstract summary: Constraint monitoring aims to monitor the violation of constraints in business processes. Business processes are object-centric, i.e., multiple case notions (objects) exist. We propose an approach to monitoring constraints in object-centric business processes.
• Score: 0.5330240017302619
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Constraint monitoring aims to monitor the violation of constraints in business processes, e.g., an invoice should be cleared within 48 hours after the corresponding goods receipt, by analyzing event data. Existing techniques for constraint monitoring assume that a single case notion exists in a business process, e.g., a patient in a healthcare process, and each event is associated with the case notion. However, in reality, business processes are object-centric, i.e., multiple case notions (objects) exist, and an event may be associated with multiple objects. For instance, an Order-To-Cash (O2C) process involves order, item, delivery, etc., and they interact when executing an event, e.g., packing multiple items together for a delivery. The existing techniques produce misleading insights when applied to such object-centric business processes. In this work, we propose an approach to monitoring constraints in object-centric business processes. To this end, we introduce Object-Centric Constraint Graphs (OCCGs) to represent constraints that consider the interaction of objects. Next, we evaluate the constraints represented by OCCGs by analyzing Object-Centric Event Logs (OCELs) that store the interaction of different objects in events. We have implemented a web application to support the proposed approach and conducted two case studies using a real-life SAP ERP system.

Related papers

• Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networks [55.583478485027]
  This study proposes a novel framework for anomaly detection in business processes. We first reconstruct the process dependencies of the object-centric event logs as attributed graphs. We then employ a graph convolutional autoencoder architecture to detect anomalous events.
  arXiv  Detail & Related papers  (2024-02-14T14:17:56Z)
• Extracting Process-Aware Decision Models from Object-Centric Process Data [54.04724730771216]
  This paper proposes the first object-centric decision-mining algorithm called Integrated Object-centric Decision Discovery Algorithm (IODDA) IODDA is able to discover how a decision is structured as well as how a decision is made.
  arXiv  Detail & Related papers  (2024-01-26T13:27:35Z)
• Object-Centric Conformance Alignments with Synchronization (Extended Version) [57.76661079749309]
  We present a new formalism that combines the ability of object-centric Petri nets to capture one-to-many relations and the one of Petri nets with identifiers to compare and synchronize objects based on their identity. We propose a conformance checking approach for such nets based on an encoding in satisfiability modulo theories (SMT)
  arXiv  Detail & Related papers  (2023-12-13T21:53:32Z)
• Defining Cases and Variants for Object-Centric Event Data [0.36748639131154304]
  We introduce the case concept for object-centric process mining: process executions. We provide techniques to extract process executions. We show the most frequent object-centric variants of a real-life event log.
  arXiv  Detail & Related papers  (2022-08-05T15:33:03Z)
• Predictive Object-Centric Process Monitoring [10.219621548854343]
  This thesis shows that a prediction method utilizing Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM), and Sequence to Sequence models (Seq2seq) can be augmented with the rich data contained in OCEL. This thesis provides a web interface to predict the next sequence of activities from user input.
  arXiv  Detail & Related papers  (2022-07-20T16:30:47Z)
• ASP-Based Declarative Process Mining [4.060731229044571]
  We put forward Answer Set Programming (ASP) as a solution approach for three classical problems in Declarative Process Mining. We tackle them in their data-aware variant, i.e., by considering events that carry a payload (set of attribute-value pairs) The contributions of the work include an ASP encoding schema for the three problems, their solution, and experiments showing the feasibility of the approach.
  arXiv  Detail & Related papers  (2022-05-04T10:11:54Z)
• Object-centric Process Predictive Analytics [0.5161531917413706]
  Object-centric processes are implementations of a paradigm where an instance of one process is not executed in isolation but interacts with other instances of the same or other processes. This paper proposes an approach to incorporate the information about the object interactions into the predictive analytics.
  arXiv  Detail & Related papers  (2022-03-05T18:46:10Z)
• Inferring Unobserved Events in Systems With Shared Resources and Queues [0.8602553195689513]
  Real-life systems often record only a subset of all events taking place. To understand and analyze the behavior of processes with shared resources, we aim to reconstruct bounds for timestamps of events that must have happened but were not recorded. We use linear programming over entity traces to derive the timestamps of unobserved events in an efficient manner.
  arXiv  Detail & Related papers  (2021-02-27T09:34:01Z)
• Discovering Object-Centric Petri Nets [77.79845386439361]
  Techniques to discover Petri nets from event data assume precisely one case identifier per event. Case identifiers are used to correlate events, and the resulting discovered Petri net aims to describe the life-cycle of individual cases. This paper discusses a novel process discovery approach implemented in PM4Py.
  arXiv  Detail & Related papers  (2020-10-05T14:25:42Z)
• Efficient State Abstraction using Object-centered Predicates for Manipulation Planning [86.24148040040885]
  We propose an object-centered representation that permits characterizing a much wider set of possible changes in configuration spaces. Based on this representation, we define universal planning operators for picking and placing actions that permits generating plans with geometric and force consistency.
  arXiv  Detail & Related papers  (2020-07-16T10:52:53Z)
• Rethinking Object Detection in Retail Stores [55.359582952686175]
  We propose a new task, simultaneously object localization and counting, abbreviated as Locount. Locount requires algorithms to localize groups of objects of interest with the number of instances. We collect a large-scale object localization and counting dataset with rich annotations in retail stores.
  arXiv  Detail & Related papers  (2020-03-18T14:01:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.