Process Comparison Using Object-Centric Process Cubes
- URL: http://arxiv.org/abs/2103.07184v1
- Date: Fri, 12 Mar 2021 10:08:28 GMT
- Title: Process Comparison Using Object-Centric Process Cubes
- Authors: Anahita Farhang Ghahfarokhi, Alessandro Berti, Wil M.P. van der Aalst
- Abstract summary: In real-life business processes, different behaviors exist that make the overall process too complex to interpret.
Process comparison is a branch of process mining that isolates different behaviors of the process from each other by using process cubes.
We propose a process cube framework, which supports process cube operations such as slice and dice on object-centric event logs.
- Score: 69.68068088508505
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Process mining provides ways to analyze business processes. Common process
mining techniques consider the process as a whole. However, in real-life
business processes, different behaviors exist that make the overall process too
complex to interpret. Process comparison is a branch of process mining that
isolates different behaviors of the process from each other by using process
cubes. Process cubes organize event data using different dimensions. Each cell
contains a set of events that can be used as an input to apply process mining
techniques. Existing work on process cubes assume single case notions. However,
in real processes, several case notions (e.g., order, item, package, etc.) are
intertwined. Object-centric process mining is a new branch of process mining
addressing multiple case notions in a process. To make a bridge between
object-centric process mining and process comparison, we propose a process cube
framework, which supports process cube operations such as slice and dice on
object-centric event logs. To facilitate the comparison, the framework is
integrated with several object-centric process discovery approaches.
Related papers
- Navigating Process Mining: A Case study using pm4py [0.0]
We present a comprehensive analysis of road traffic fine management processes using the pm4py library in Python.
Through filtering and statistical analysis, we uncover key patterns and variations in the process executions.
We visualize the discovered models to understand the workflow structures and dependencies within the process.
arXiv Detail & Related papers (2024-09-17T15:48:46Z) - 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) - Analyzing An After-Sales Service Process Using Object-Centric Process
Mining: A Case Study [0.1433758865948252]
This paper focuses on the emerging domain of object-centric process mining.
Through an in-depth case study of Borusan Cat's after-sales service process, this study emphasizes the capability of object-centric process mining.
arXiv Detail & Related papers (2023-10-16T08:34:41Z) - Detecting Surprising Situations in Event Data [0.45119235878273]
In existing work, it is usually assumed that the set of problematic process instances in which an undesirable outcome occurs is known prior or is easily detectable.
We formulate finding the process enhancement areas as a context-sensitive anomaly/outlier detection problem.
We aim to characterize those situations where process performance/outcome is significantly different from what was expected.
arXiv Detail & Related papers (2022-08-29T11:33:58Z) - Clustering Object-Centric Event Logs [0.36748639131154304]
We propose a clustering-based approach to cluster similar objects in OCELs to simplify the obtained process models.
Our approach reduces the complexity of the process models and generates coherent subsets of objects which help the end-users gain insights into the process.
arXiv Detail & Related papers (2022-07-26T09:16:39Z) - Removing Operational Friction Using Process Mining: Challenges Provided
by the Internet of Production (IoP) [0.0]
Event data generated by today's operational processes provides opportunities and challenges for process mining.
Process mining is used to create "digital shadows" to improve a wide variety of operational processes.
We aim to develop valuable "digital shadows" that can be used to remove operational friction.
arXiv Detail & Related papers (2021-07-27T20:04:25Z) - CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT
(Extended Version) [62.96267257163426]
We introduce the CoCoMoT (Computing Conformance Modulo Theories) framework.
First, we show how SAT-based encodings studied in the pure control-flow setting can be lifted to our data-aware case.
Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering.
arXiv Detail & Related papers (2021-03-18T20:22:50Z) - Analogous Process Structure Induction for Sub-event Sequence Prediction [111.10887596684276]
We propose an Analogous Process Structure Induction APSI framework to predict the whole sub-event sequence of previously unseen processes.
As our experiments and analysis indicate, APSI supports the generation of meaningful sub-event sequences for unseen processes and can help predict missing events.
arXiv Detail & Related papers (2020-10-16T17:35:40Z) - "What Are You Trying to Do?" Semantic Typing of Event Processes [94.3499255880101]
This paper studies a new cognitively motivated semantic typing task, multi-axis event process typing.
We develop a large dataset containing over 60k event processes, featuring ultra fine-grained typing on both the action and object type axes.
We propose a hybrid learning framework, P2GT, which addresses the challenging typing problem with indirect supervision from glosses1and a joint learning-to-rank framework.
arXiv Detail & Related papers (2020-10-13T22:37:29Z) - 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)
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.