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How Much Event Data Is Enough? A Statistical Framework for Process Discovery · Study

WorkHow Much Event Data Is Enough? A Statistical Framework for Process Discovery

Arik Senderovich2018

Work2018

How Much Event Data Is Enough? A Statistical Framework for Process Discovery

Arik Senderovich · Avigdor Gal · Lars Grunske · Matthias Weidlich

39 citations·0/0 files stored·Lecture notes in computer science

No readable copy stored yet — this record is metadata only.

Business Process Modeling and Analysis Data Mining Algorithms and Applications Robotic Process Automation Applications

Abstract

No abstract was published for this record.

Citations in this library

Stored only when both papers are in the corpus — 39 citations exist upstream.

References

2004Workflow mining: discovering process models from event logs 2016Process Mining 2012Process Mining Manifesto 2016Process Mining: Data Science in Action 2012Replaying history on process models for conformance checking and performance analysis 2013Discovering Block-Structured Process Models from Event Logs - A Constructive Approach 2003Rediscovering workflow models from event-based data using little thumb 2014Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour

Cited by

2019Estimating Process Conformance by Trace Sampling and Result Approximation 2020Conformance Checking Approximation Using Subset Selection and Edit Distance 2020Sampling and approximation techniques for efficient process conformance checking 2019The Impact of Event Log Subset Selection on the Performance of Process Discovery Algorithms 2021The impact of biased sampling of event logs on the performance of process discovery 2020Data Analytic Approaches for Mining Process Improvement—Machinery Utilization Use Case

Details

Year2018

Typeother

Published2018-01-01

VenueLecture notes in computer science

ISSN-L0302-9743

Languageen

Citations39

Files0 public / 0

ShelfBusiness Process Modeling and Analysis

Identifiers

doi10.1007/978-3-319-91563-0_15

openalex_workW2804517486

Links

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