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出版商:
Springer
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出版日期:
2026-04-24
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售價:
$7,360
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貴賓價:
9.5 折
$6,992
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語言:
英文
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頁數:
174
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裝訂:
Quality Paper - also called trade paper
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ISBN:
3032172772
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ISBN-13:
9783032172778
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相關分類:
Data-mining
商品描述
This book provides a comprehensive, structured, and accessible resource that covers both foundational aspects and advanced topics of predictive process monitoring (PPM). It introduces the key building blocks of PPM from preliminary notions and core libraries to bucketing and encoding strategies, learning methods, and validation techniques. At the same time, the book extends its reach to advanced themes such as neuro-symbolic PPM, explainability, multi-modal predictive monitoring, and prescriptive approaches. This dual scope makes it both an introductory text and a reference work for advanced study. The presentation is organized in seven chapters. Chapter 1 introduces the reader to the field, including its preliminaries and a helicopter view of PPM. Next, chapter 2 presents the tools and libraries that support implementation. Chapters 3 and 4 then delve into core data preparation aspects: prefix generation, bucketing, and encoding techniques. Chapter 5 discusses learning approaches, while Chapter 6 focuses on validation and testing. Finally, Chapter 7 highlights advanced topics that represent the current frontier of the field. Each chapter is enriched with exercises to facilitate learning and with notes to provide further reading. This book mainly aims at graduate students and researchers in computer science, information systems, and data science who wish to gain a deep understanding of PPM. It is also designed for educators, who will find the structured exposition, exercises, and references suitable for designing and teaching courses on process mining and predictive analytics. Eventually, practitioners and professionals in industry will benefit from the guidance on applying PPM techniques to optimize and innovate their organizational processes.
商品描述(中文翻譯)
本書提供了一個全面、結構化且易於理解的資源，涵蓋了預測過程監控（PPM）的基礎面向和進階主題。它介紹了PPM的關鍵構建塊，從初步概念和核心庫到分桶和編碼策略、學習方法以及驗證技術。同時，本書也擴展到進階主題，如神經符號PPM、可解釋性、多模態預測監控和規範性方法。這種雙重範疇使其既是入門教材，也是進階研究的參考書。
本書的內容組織在七個章節中。第一章向讀者介紹該領域，包括其基礎知識和PPM的全景概述。接下來，第二章介紹支持實現的工具和庫。第三章和第四章深入探討核心數據準備方面：前綴生成、分桶和編碼技術。第五章討論學習方法，而第六章則專注於驗證和測試。最後，第七章強調代表該領域當前前沿的進階主題。每個章節都附有練習題以促進學習，並附有註解以提供進一步閱讀的參考。
本書主要針對希望深入了解PPM的計算機科學、資訊系統和數據科學的研究生和研究人員。同時，它也為教育工作者設計，結構化的闡述、練習題和參考資料適合用於設計和教授過程挖掘和預測分析的課程。最終，業界的從業者和專業人士將受益於有關應用PPM技術以優化和創新其組織流程的指導。
作者簡介
Chiara Di Francescomarino is an Associate Professor at the Information Engineering and Computer Science Department of the University of Trento. Her main research interests are in the field of Business Process Management, with a particular focus on Process Mining. She has worked extensively on Predictive and Prescriptive Process Monitoring based on historical execution traces, as well as on techniques for explaining process predictions and supporting simulation-based decision making. Ivan Donadello is an Assistant Professor at the Faculty of Engineering of the Free University of Bozen-Bolzano. His research focuses on Predictive Process Monitoring within the framework of Neuro-Symbolic Artificial Intelligence. He is the main architect of Declare4Py, an open-source Python library for declarative process mining and serves as associate editor for the Logic and Reasoning in Artificial Intelligence section of Frontiers in Artificial Intelligence. He also heads the Machine Learning course at the Free University of Bozen-Bolzano and regularly supervises theses on Neuro-Symbolic techniques applied to Predictive Process Monitoring. Fabrizio Maria Maggi is a Full Professor at the Faculty of Engineering of the Free University of Bozen-Bolzano. He has a strong background in the fields of Business Process Management and Artificial Intelligence. He is a pioneer in developing techniques that integrate Machine Learning to extract hidden insights from execution logs, having initiated the Predictive Process Monitoring research line that later has become one of the pillars of process analysis in Business Process Management. He has also contributed to some of the first works on explainable Predictive Process Monitoring and Prescriptive Process Monitoring. Recently, his research has focused on developing techniques that combine Predictive Process Monitoring with Neuro-Symbolic Artificial Intelligence.
作者簡介(中文翻譯)
Chiara Di Francescomarino 是特倫托大學資訊工程與計算機科學系的副教授。她的主要研究興趣在於商業流程管理（Business Process Management），特別專注於流程挖掘（Process Mining）。她在基於歷史執行痕跡的預測性和處方性流程監控方面有廣泛的研究，並且致力於解釋流程預測和支持基於模擬的決策制定的技術。
Ivan Donadello 是博岑-博爾扎諾自由大學工程學院的助理教授。他的研究專注於神經符號人工智慧（Neuro-Symbolic Artificial Intelligence）框架下的預測性流程監控。他是 Declare4Py 的主要架構師，這是一個用於聲明式流程挖掘的開源 Python 庫，並擔任《人工智慧前沿》（Frontiers in Artificial Intelligence）中邏輯與推理在人工智慧部分的副編輯。他還負責博岑-博爾扎諾自由大學的機器學習課程，並定期指導關於應用於預測性流程監控的神經符號技術的論文。
Fabrizio Maria Maggi 是博岑-博爾扎諾自由大學工程學院的正教授。他在商業流程管理和人工智慧領域擁有堅實的背景。他是將機器學習整合以從執行日誌中提取隱藏見解的技術的先驅，並啟動了預測性流程監控的研究方向，該方向後來成為商業流程管理中流程分析的支柱之一。他還對一些關於可解釋的預測性流程監控和處方性流程監控的早期研究做出了貢獻。最近，他的研究專注於開發將預測性流程監控與神經符號人工智慧相結合的技術。
