Mertens Steven Enabling Process Management for Loosely Framed Knowledge-Intensive Processes 9783030661922

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Автор: Mertens Steven
Название: Enabling Process Management for Loosely Framed Knowledge-Intensive Processes
ISBN: 9783030661922
Издательство: Springer
Классификация:

Математические методы анализа и информационные системы финансово-хозяйственной деятельности

Влияние ИТ на охрану здоровья и безопасность

ISBN-10: 303066192X
Обложка/Формат: Paperback
Страницы: 202
Вес: 0.31 кг.
Дата издания: 24.01.2021
Язык: English
Размер: 23.39 x 15.60 x 1.17 cm
Ссылка на Издательство: Link
Поставляется из: Германии
Описание:

1 Introduction.- 1.1 Terminology and Research Context.- 1.2 Problem Statement.- 1.3 Research Objectives.- 1.4 Research Methodology.- 1.5 Dissertation Structure.- 2 Towards a Decision-aware Declarative Process Modeling Language for Knowledge-intensive Processes.- 2.1 Introduction.- 2.2 Related Work.- 2.3 Research Methodology.- 2.4 Language Requirements.- 2.5 Designing the Perspectives.- 2.5.1 The arm fracture case.- 2.5.2 Declare as a foundation.- 2.5.3 Extending the functional and control-flow perspectives.- 2.5.4 Adding a notion of time-awareness to the constraint templates.- 2.5.5 Supporting the data perspective.- 2.5.6 Supporting the resource perspective.- 2.6 Integrating the Perspectives.- 2.6.1 Integrating the data perspective.- 2.6.2 Integrating the resource perspective.- 2.6.3 DeciClare.- 2.7 Evaluation.- 2.7.1 The evaluation set-up.- 2.7.2 Evaluating the perceived semantic quality.- 2.7.3 Evaluating the pragmatic quality.- 2.7.4 Evaluating the language-domain appropriateness.- 2.8 Positioning of DeciClare in the Research Domain.- 2.9 Conclusion.- 2.10 Limitations and Future Work.- 3 Discovering Loosely Framed Knowledge-intensive Processes using DeciClareMiner.- 3.1 Introduction.- 3.2 Related Work.- 3.3 Modeling Healthcare Processes: DeciClare.- 3.4 Research Methodology.- 3.5 Discovering Healthcare Process Models: DeciClareMiner.- 3.5.1 Phase 1: Mining decision-independent rules.- 3.5.1.1 Leveraged property.- 3.5.1.2 The iteration principle: optimizations.- 3.5.1.3 The join criteria.- 3.5.1.4 The join step.- 3.5.1.5 Running example.- 3.5.1.6 The output of the first phase.- 3.5.2 Phase 2: Mining decision-dependent rules.- 3.5.2.1 Fitness Function.- 3.5.2.2 Optimization.- 3.5.2.3 Running example.- 3.5.2.4 The output after the second phase.- 3.6 Evaluation.- 3.6.1 Dataset.- 3.6.2 Benchmark creation.- 3.6.3 Results.- 3.6.4 Discussion.- 3.7 Limitations.- 3.8 Conclusions and Directions for Future Research.- 4 Integrated Declarative Process and Decision Discovery of the Emergency Care Process.- 4.1 Introduction.- 4.2 Research Methodology.- 4.3 Discovering the Emergency Care Process with DeciClareMiner.- 4.3.1 Extraction.- 4.3.2 Data Preprocessing.- 4.3.3 Log Preparation.- 4.3.4 Mining a Process and Decision Model.- 4.3.5 Evaluation.- 4.4 Method Application: The Emergency Care Process.- 4.4.1 Extraction.- 4.4.2 Data Preprocessing.- 4.4.3 Log Preparation.- 4.4.4 Mining a Process and Decision Model.- 4.4.5 Evaluation.- 4.4.6 Discussion.- 4.5 Conclusion and Future Research.- 5 Operational Support for Loosely Framed Knowledge-intensive Processes.- 5.1 A Generic Framework for Flexible and Data-Aware Business Process Engines.- 5.1.1 Introduction.- 5.1.2 Research Methodology.- 5.1.3 Problem Statement and Solution Requirements.- 5.1.4 Data-Aware Declarative Process Enactment Framework.- 5.1.5 Demonstration.- 5.1.5.1 Process Definition: DeciClare.- 5.1.5.2 DeciClareEngine.- 5.1.5.3 Related Work.- 5.1.5.4 Conclusion and Future Research.- 5.2 Comparing Strategies to Generate Experience-based Clinical Process Recommendations that Leverage Similarity to Historic Data.- 5.2.1 Introduction.- 5.2.2 Related Work.- 5.2.3 Terminology and Formal Problem Definition.- 5.2.4 Research Methodology.- 5.2.5 Strategies.- 5.2.5.1 Pre-calculated similarity scorers.- 5.2.5.2 Positionless similarity scorers.- 5.2.5.3 Variable-position similarity scorers.- 5.2.5.4 Combinated scorers.- 5.2.6 Experiments.- 5.2.6.1 Experiment 1: comparing the general predictive power over different data sets.- 5.2.6.1.1 Experimental setup.- 5.2.6.1.2 Computational results.- 5.2.6.2 Experiment 2: the relation between predictive power and the size of H.- 5.2.6.2.1 Experimental setup.- 5.2.6.2.2 Computational results.- 5.2.7 Discussion.- 5.2.8 Conclusion and Future Research.- 6 Conclusion and Future Research.- 6.1 Research Results.- 6.2 Research Relevance and Implications.- 6.2.1 Implications for researchers.- 6.2.2 Implications for process stakeholders.- 6.2.3

Enabling Process Management for Loosely Framed Knowledge-Intensive Processes, Mertens Steven