Precision and Fitness in Object-Centric Process Mining

• URL: http://arxiv.org/abs/2110.05375v1
• Date: Wed, 6 Oct 2021 15:49:56 GMT
• Title: Precision and Fitness in Object-Centric Process Mining
• Authors: Jan Niklas Adams and Wil M.P. van der Aalst
• Abstract summary: We introduce a notion for the precision and fitness of an object-centric Petri net with respect to an object-centric event log. Our notions are an appropriate way to generalize quality measures to the object-centric setting.
• Score: 1.14219428942199
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional process mining considers only one single case notion and discovers and analyzes models based on this. However, a single case notion is often not a realistic assumption in practice. Multiple case notions might interact and influence each other in a process. Object-centric process mining introduces the techniques and concepts to handle multiple case notions. So far, such event logs have been standardized and novel process model discovery techniques were proposed. However, notions for evaluating the quality of a model are missing. These are necessary to enable future research on improving object-centric discovery and providing an objective evaluation of model quality. In this paper, we introduce a notion for the precision and fitness of an object-centric Petri net with respect to an object-centric event log. We give a formal definition and accompany this with an example. Furthermore, we provide an algorithm to calculate these quality measures. We discuss our precision and fitness notion based on an event log with different models. Our precision and fitness notions are an appropriate way to generalize quality measures to the object-centric setting since we are able to consider multiple case notions, their dependencies and their interactions.

Related papers

• When is an Embedding Model More Promising than Another? [33.540506562970776]
  Embedders play a central role in machine learning, projecting any object into numerical representations that can be leveraged to perform various downstream tasks. The evaluation of embedding models typically depends on domain-specific empirical approaches. We present a unified approach to evaluate embedders, drawing upon the concepts of sufficiency and informativeness.
  arXiv  Detail & Related papers  (2024-06-11T18:13:46Z)
• Causal Estimation of Memorisation Profiles [58.20086589761273]
  Understanding memorisation in language models has practical and societal implications. Memorisation is the causal effect of training with an instance on the model's ability to predict that instance. This paper proposes a new, principled, and efficient method to estimate memorisation based on the difference-in-differences design from econometrics.
  arXiv  Detail & Related papers  (2024-06-06T17:59:09Z)
• 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)
• Evaluating Representations with Readout Model Switching [18.475866691786695]
  In this paper, we propose to use the Minimum Description Length (MDL) principle to devise an evaluation metric. We design a hybrid discrete and continuous-valued model space for the readout models and employ a switching strategy to combine their predictions. The proposed metric can be efficiently computed with an online method and we present results for pre-trained vision encoders of various architectures.
  arXiv  Detail & Related papers  (2023-02-19T14:08:01Z)
• Interacting Hand-Object Pose Estimation via Dense Mutual Attention [97.26400229871888]
  3D hand-object pose estimation is the key to the success of many computer vision applications. We propose a novel dense mutual attention mechanism that is able to model fine-grained dependencies between the hand and the object. Our method is able to produce physically plausible poses with high quality and real-time inference speed.
  arXiv  Detail & Related papers  (2022-11-16T10:01:33Z)
• OPerA: Object-Centric Performance Analysis [0.4014524824655105]
  We propose a novel approach to performance analysis by using object-centric Petri nets as formal representations of business processes. The proposed approach correctly computes existing performance metrics, while supporting the derivation of newly-introduced object-centric performance metrics. We have implemented the approach as a web application and conducted a case study based on a real-life loan application process.
  arXiv  Detail & Related papers  (2022-04-22T12:23:06Z)
• Towards Self-Supervised Category-Level Object Pose and Size Estimation [121.28537953301951]
  This work presents a self-supervised framework for category-level object pose and size estimation from a single depth image. We leverage the geometric consistency residing in point clouds of the same shape for self-supervision.
  arXiv  Detail & Related papers  (2022-03-06T06:02:30Z)
• 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)
• Translational Concept Embedding for Generalized Compositional Zero-shot Learning [73.60639796305415]
  Generalized compositional zero-shot learning means to learn composed concepts of attribute-object pairs in a zero-shot fashion. This paper introduces a new approach, termed translational concept embedding, to solve these two difficulties in a unified framework.
  arXiv  Detail & Related papers  (2021-12-20T21:27:51Z)
• Attentional Prototype Inference for Few-Shot Segmentation [128.45753577331422]
  We propose attentional prototype inference (API), a probabilistic latent variable framework for few-shot segmentation. We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution. We conduct extensive experiments on four benchmarks, where our proposal obtains at least competitive and often better performance than state-of-the-art prototype-based methods.
  arXiv  Detail & Related papers  (2021-05-14T06:58:44Z)

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.