東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Incremental process discovery

Record Type:	Electronic resources : Monograph/item
Title/Author:	Incremental process discovery/ by Daniel Schuster.
Author:	Schuster, Daniel.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xvi, 367 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Opening and fundamentals -- incremental process discovery -- facilitating interaction with event data -- realization and application -- closure.
Contained By:	Springer Nature eBook
Subject:	Business Process Management. -
Online resource:	https://doi.org/10.1007/978-3-031-80565-3
ISBN:	9783031805653

Incremental process discovery
Schuster, Daniel.

Incremental process discovery[electronic resource] /by Daniel Schuster. - Cham :Springer Nature Switzerland :2025. - xvi, 367 p. :ill. (some col.), digital ;24 cm. - Lecture notes in business information processing,5401865-1356 ;. - Lecture notes in business information processing ;540..

Opening and fundamentals -- incremental process discovery -- facilitating interaction with event data -- realization and application -- closure.

This book constitutes the revised version of the award-winning PhD dissertation written by the author at RWTH Aachen, Germany. It presents a framework for incremental process discovery that allows users to learn and refine process models from event data iteratively. Next to process discovery and event data handling, it also contributes to conformance checking, a further fundamental process mining task. Eventually, it presents Cortado, an open-source process mining software tool that implements the algorithms and techniques proposed in an integrated and comprehensive fashion. This part also includes a case study applying Cortado and, therefore, the various contributions of this thesis in a real-life scenario. In 2024, this PhD dissertation won the "Best Process Mining PhD Dissertation Award" by the IEEE Task Force for Process Mining, granted to outstanding PhD theses in this field.

ISBN: 9783031805653

Standard No.: 10.1007/978-3-031-80565-3doiSubjects--Topical Terms:

2134548
Business Process Management.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Incremental process discovery
LDR:02079nmm a2200337 a 4500 001 2409656
003 DE-He213
005 20250408035230.0
006 m d
007 cr nn 008maaau
008 260204s2025 sz s 0 eng d
020 $a 9783031805653 $q (electronic bk.)
020 $a 9783031805646 $q (paper)
024 7 $a 10.1007/978-3-031-80565-3 $2 doi
035 $a 978-3-031-80565-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a KJQ $2 bicssc
072 7 $a COM005030 $2 bisacsh
072 7 $a KJQ $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b S395 2025
100 1 $a Schuster, Daniel. $3 3782965
245 1 0 $a Incremental process discovery $h [electronic resource] / $c by Daniel Schuster.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xvi, 367 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in business information processing, $x 1865-1356 ; $v 540
505 0 $a Opening and fundamentals -- incremental process discovery -- facilitating interaction with event data -- realization and application -- closure.
520 $a This book constitutes the revised version of the award-winning PhD dissertation written by the author at RWTH Aachen, Germany. It presents a framework for incremental process discovery that allows users to learn and refine process models from event data iteratively. Next to process discovery and event data handling, it also contributes to conformance checking, a further fundamental process mining task. Eventually, it presents Cortado, an open-source process mining software tool that implements the algorithms and techniques proposed in an integrated and comprehensive fashion. This part also includes a case study applying Cortado and, therefore, the various contributions of this thesis in a real-life scenario. In 2024, this PhD dissertation won the "Best Process Mining PhD Dissertation Award" by the IEEE Task Force for Process Mining, granted to outstanding PhD theses in this field.
650 1 4 $a Business Process Management. $3 2134548
650 2 4 $a Computer Application in Administrative Data Processing. $3 3594379
650 0 $a Data mining. $3 562972
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in business information processing ; $v 540. $3 3782966
856 4 0 $u https://doi.org/10.1007/978-3-031-80565-3
950 $a Computer Science (SpringerNature-11645)