Hybrid Process Mining: Inference & Evaluation Across Imperative & Declarative Approaches

Research output: Book/ReportPh.D. thesis

Standard

Hybrid Process Mining : Inference & Evaluation Across Imperative & Declarative Approaches. / Back, Christoffer Olling.

Department of Computer Science, Faculty of Science, University of Copenhagen, 2021. 238 p.

Research output: Book/ReportPh.D. thesis

Harvard

Back, CO 2021, Hybrid Process Mining: Inference & Evaluation Across Imperative & Declarative Approaches. Department of Computer Science, Faculty of Science, University of Copenhagen.

APA

Back, C. O. (2021). Hybrid Process Mining: Inference & Evaluation Across Imperative & Declarative Approaches. Department of Computer Science, Faculty of Science, University of Copenhagen.

Vancouver

Back CO. Hybrid Process Mining: Inference & Evaluation Across Imperative & Declarative Approaches. Department of Computer Science, Faculty of Science, University of Copenhagen, 2021. 238 p.

Author

Back, Christoffer Olling. / Hybrid Process Mining : Inference & Evaluation Across Imperative & Declarative Approaches. Department of Computer Science, Faculty of Science, University of Copenhagen, 2021. 238 p.

Bibtex

@phdthesis{322d33f3b2394b23ad530dac26b8bf7a,
title = "Hybrid Process Mining: Inference & Evaluation Across Imperative & Declarative Approaches",
abstract = "Modern enterprise systems - and IT systems generally - generate enormous amounts of data related to the execution of workflows and other processes. We can harness that data using process mining techniques, and related machine learning algorithms, to provide insight into real-world process execution, and develop predictive models for process enhancement, risk mitigation and anomaly detection. This thesis explores approaches to learning two contrasting classes of models, namely imperative and declarative models, and hybrids of the two. Roughly speaking, the former captures processes as explicit flows or procedures, whereas the latter captures the most essential constraints, rules or properties of a process and is often encoded in a logic resembling natural language. The aim is to harness the strengths of both to learn accurate and interpretable models. With a strong focus on one-to-one empirical comparisons across the modeling paradigms, the project aims to ground hybrid process mining on a solid statistical basis and frame it as a classical inference task.",
author = "Back, {Christoffer Olling}",
year = "2021",
language = "English",
publisher = "Department of Computer Science, Faculty of Science, University of Copenhagen",

}

RIS

TY - BOOK

T1 - Hybrid Process Mining

T2 - Inference & Evaluation Across Imperative & Declarative Approaches

AU - Back, Christoffer Olling

PY - 2021

Y1 - 2021

N2 - Modern enterprise systems - and IT systems generally - generate enormous amounts of data related to the execution of workflows and other processes. We can harness that data using process mining techniques, and related machine learning algorithms, to provide insight into real-world process execution, and develop predictive models for process enhancement, risk mitigation and anomaly detection. This thesis explores approaches to learning two contrasting classes of models, namely imperative and declarative models, and hybrids of the two. Roughly speaking, the former captures processes as explicit flows or procedures, whereas the latter captures the most essential constraints, rules or properties of a process and is often encoded in a logic resembling natural language. The aim is to harness the strengths of both to learn accurate and interpretable models. With a strong focus on one-to-one empirical comparisons across the modeling paradigms, the project aims to ground hybrid process mining on a solid statistical basis and frame it as a classical inference task.

AB - Modern enterprise systems - and IT systems generally - generate enormous amounts of data related to the execution of workflows and other processes. We can harness that data using process mining techniques, and related machine learning algorithms, to provide insight into real-world process execution, and develop predictive models for process enhancement, risk mitigation and anomaly detection. This thesis explores approaches to learning two contrasting classes of models, namely imperative and declarative models, and hybrids of the two. Roughly speaking, the former captures processes as explicit flows or procedures, whereas the latter captures the most essential constraints, rules or properties of a process and is often encoded in a logic resembling natural language. The aim is to harness the strengths of both to learn accurate and interpretable models. With a strong focus on one-to-one empirical comparisons across the modeling paradigms, the project aims to ground hybrid process mining on a solid statistical basis and frame it as a classical inference task.

M3 - Ph.D. thesis

BT - Hybrid Process Mining

PB - Department of Computer Science, Faculty of Science, University of Copenhagen

ER -

ID: 272724847