All That Glitters Is Not Gold: Towards Process Discovery Techniques with Guarantees

• URL: http://arxiv.org/abs/2012.12764v1
• Date: Wed, 23 Dec 2020 16:08:47 GMT
• Title: All That Glitters Is Not Gold: Towards Process Discovery Techniques with Guarantees
• Authors: Jan Martijn E. M. van der Werf, Artem Polyvyanyy, Bart R. van Wensveen, Matthieu Brinkhuis and Hajo A. Reijers
• Abstract summary: The better the quality of the event data, the better the quality of the model that is discovered. We demonstrate this by using a range of quality measures for both event data and discovered process models. This paper is a call to the community of IS engineers to complement their process discovery algorithms with properties that relate qualities of their inputs to those of their outputs.
• Score: 1.3299507495084417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The aim of a process discovery algorithm is to construct from event data a process model that describes the underlying, real-world process well. Intuitively, the better the quality of the event data, the better the quality of the model that is discovered. However, existing process discovery algorithms do not guarantee this relationship. We demonstrate this by using a range of quality measures for both event data and discovered process models. This paper is a call to the community of IS engineers to complement their process discovery algorithms with properties that relate qualities of their inputs to those of their outputs. To this end, we distinguish four incremental stages for the development of such algorithms, along with concrete guidelines for the formulation of relevant properties and experimental validation. We will also use these stages to reflect on the state of the art, which shows the need to move forward in our thinking about algorithmic process discovery.

Related papers

• The WHY in Business Processes: Discovery of Causal Execution Dependencies [2.0811729303868005]
  Unraveling causal relationships among the execution of process activities is a crucial element in predicting the consequences of process interventions. This work offers a systematic approach to the unveiling of the causal business process by leveraging an existing causal discovery algorithm over activity timing. Our methodology searches for such discrepancies between the two models in the context of three causal patterns, and derives a new view in which these inconsistencies are annotated over the mined process model.
  arXiv  Detail & Related papers  (2023-10-23T14:23:15Z)
• Discovering Hierarchical Process Models: an Approach Based on Events Clustering [0.0]
  We present an algorithm for discovering hierarchical process models represented as two-level workflow nets. Unlike existing solutions, our algorithm does not impose restrictions on the process control flow and allows for iteration.
  arXiv  Detail & Related papers  (2023-03-12T11:05:40Z)
• A Monitoring and Discovery Approach for Declarative Processes Based on Streams [0.0]
  We present a discovery algorithm that extracts declarative processes as Dynamic Condition Response (DCR) graphs from event streams. Streams are monitored to generate temporal representations of the process, later processed to generate declarative models.
  arXiv  Detail & Related papers  (2022-08-10T14:25:35Z)
• Human Activity Recognition using Attribute-Based Neural Networks and Context Information [61.67246055629366]
  We consider human activity recognition (HAR) from wearable sensor data in manual-work processes. We show how context information can be integrated systematically into a deep neural network-based HAR system. We empirically show that our proposed architecture increases HAR performance, compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-28T06:08:25Z)
• Process discovery on deviant traces and other stranger things [6.974048370610024]
  We focus on declarative processes and embrace the less-popular view of process discovery as a binary supervised learning task. We deepen how the valuable information brought by both these two sets can be extracted and formalised into a model that is "optimal" according to user-defined goals.
  arXiv  Detail & Related papers  (2021-09-30T06:58:34Z)
• Knowledge-Aware Procedural Text Understanding with Multi-Stage Training [110.93934567725826]
  We focus on the task of procedural text understanding, which aims to comprehend such documents and track entities' states and locations during a process. Two challenges, the difficulty of commonsense reasoning and data insufficiency, still remain unsolved. We propose a novel KnOwledge-Aware proceduraL text understAnding (KOALA) model, which effectively leverages multiple forms of external knowledge.
  arXiv  Detail & Related papers  (2020-09-28T10:28:40Z)
• Information Theoretic Meta Learning with Gaussian Processes [74.54485310507336]
  We formulate meta learning using information theoretic concepts; namely, mutual information and the information bottleneck. By making use of variational approximations to the mutual information, we derive a general and tractable framework for meta learning.
  arXiv  Detail & Related papers  (2020-09-07T16:47:30Z)
• Process Discovery for Structured Program Synthesis [70.29027202357385]
  A core task in process mining is process discovery which aims to learn an accurate process model from event log data. In this paper, we propose to use (block-) structured programs directly as target process models. We develop a novel bottom-up agglomerative approach to the discovery of such structured program process models.
  arXiv  Detail & Related papers  (2020-08-13T10:33:10Z)
• Causal Feature Selection for Algorithmic Fairness [61.767399505764736]
  We consider fairness in the integration component of data management. We propose an approach to identify a sub-collection of features that ensure the fairness of the dataset.
  arXiv  Detail & Related papers  (2020-06-10T20:20:10Z)
• Active Model Estimation in Markov Decision Processes [108.46146218973189]
  We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision process (MDP) We show that our Markov-based algorithm outperforms both our original algorithm and the maximum entropy algorithm in the small sample regime.
  arXiv  Detail & Related papers  (2020-03-06T16:17:24Z)

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.