| 000 | 11163nam a22004693i 4500 | ||
|---|---|---|---|
| 001 | EBC6938742 | ||
| 003 | MiAaPQ | ||
| 005 | 20220623112416.0 | ||
| 006 | m o d | | ||
| 007 | cr cnu|||||||| | ||
| 008 | 220617s2022 xx o ||||0 eng d | ||
| 020 |
_a9783030985813 _q(electronic bk.) |
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| 020 | _z9783030985806 | ||
| 035 | _a(MiAaPQ)EBC6938742 | ||
| 035 | _a(Au-PeEL)EBL6938742 | ||
| 035 | _a(OCoLC)1308508232 | ||
| 040 |
_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ |
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| 050 | 4 | _aQA76.9.D343 | |
| 100 | 1 | _aMunoz-Gama, Jorge. | |
| 245 | 1 | 0 |
_aProcess Mining Workshops : _bICPM 2021 International Workshops, Eindhoven, the Netherlands, October 31 - November 4, 2021, Revised Selected Papers. |
| 264 | 1 |
_aCham : _bSpringer International Publishing AG, _c2022. |
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| 264 | 4 | _c�2022. | |
| 300 | _a1 online resource (419 pages) | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 490 | 1 |
_aLecture Notes in Business Information Processing Ser. ; _vv.433 |
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| 505 | 0 | _aIntro -- Preface -- Organization -- Contents -- XES 2.0 Workshop and Survey -- Rethinking the Input for Process Mining: Insights from the XES Survey and Workshop -- 1 Introduction -- 2 XES Standard: A Brief Overview -- 3 Survey Design and Insights -- 4 Adding Context: Reflections from the XES 2.0 Workshop -- 5 Conclusion -- References -- EdbA 2021: 2nd International Workshop on Event Data and Behavioral Analytics -- Second International Workshop on Event Data and Behavioral Analytics (EdbA'21) -- Organization -- Workshop Chairs -- Program Committee -- Probability Estimation of Uncertain Process Trace Realizations -- 1 Introduction -- 2 Related Work -- 3 Running Example -- 4 Preliminaries -- 5 Method -- 6 Validation of Probability Estimates -- 7 Conclusion -- References -- Visualizing Trace Variants from Partially Ordered Event Data -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Visualizing Trace Variants -- 4.1 Approach -- 4.2 Formal Guarantees -- 4.3 Limitations -- 4.4 Implementation -- 5 Evaluation -- 6 Conclusion -- References -- Analyzing Multi-level BOM-Structured Event Data -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methods -- 4.1 Analysis Methodology -- 4.2 M2BOM-Structured Assembly Processes -- 5 Case Study -- 6 Conclusion -- References -- Linac: A Smart Environment Simulator of Human Activities -- 1 Introduction -- 2 Existing Solutions -- 3 Proposed Simulation Solution -- 3.1 Configuration of the Smart Environment -- 3.2 Configuration of the Agents' Behavior - AIL Language -- 3.3 Simulation Execution -- 3.4 Clock Simulation -- 3.5 MQTT Output -- 4 Implementation -- 5 Evaluation -- 5.1 Configuration -- 5.2 Results -- 6 Conclusions and Future Works -- References -- Root Cause Analysis in Process Mining with Probabilistic Temporal Logic -- 1 Introduction -- 2 Related Work -- 3 The AITIA-PM Algorithm. | |
| 505 | 8 | _a3.1 Background -- 3.2 Algorithmic Procedure -- 4 Demonstration -- 5 Conclusion -- References -- xPM: A Framework for Process Mining with Exogenous Data -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 A Framework for Process Mining with Exogenous Data -- 4.1 Linking -- 4.2 Slicing -- 4.3 Transformation -- 4.4 Discovery -- 4.5 Enhancing -- 5 Evaluation -- 5.1 Procedure -- 5.2 Quality Measures -- 5.3 Event Logs and Exogenous Data -- 5.4 Results and Discussion -- 6 Conclusion -- References -- A Bridging Model for Process Mining and IoT -- 1 Introduction -- 2 Background -- 2.1 IoT Ontologies -- 2.2 Business Process Context Modelling -- 3 Conceptual Ambiguity in IoT and PM -- 3.1 IoT Data -- 3.2 Context in PM vs Context in IoT -- 3.3 Process Event vs IoT Event -- 4 Connecting IoT and Process Mining: A Conceptual Model -- 5 Use Case Validation -- 6 Related Work -- 7 Conclusion -- References -- ML4PM 2021: 2nd International Workshop in Leveraging Machine Learning for Process Mining -- 2nd International Workshop in Leveraging Machine Learning for Process Mining (ML4PM 2021) -- Organization -- Workshop Chairs -- Program Committee -- Additional Reviewers -- Exploiting Instance Graphs and Graph Neural Networks for Next Activity Prediction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Building Instance Graphs -- 3.2 Data Preprocessing -- 3.3 Deep Graph Convolutional Neural Network -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusions and Future Works -- References -- Can Deep Neural Networks Learn Process Model Structure? An Assessment Framework and Analysis -- 1 Introduction -- 2 Related Work -- 3 A Framework for Assessing the Generalisation Capacity of RNNs -- 3.1 The Resampling Procedure -- 3.2 Metrics -- 4 Experimental Evaluation -- 4.1 Process Models -- 4.2 Hyperparameter Search -- 4.3 Results -- 5 Discussion. | |
| 505 | 8 | _a6 Conclusion and Future Work -- References -- Remaining Time Prediction for Processes with Inter-case Dynamics -- 1 Introduction -- 2 Preliminaries and Related Work -- 2.1 Related Work -- 2.2 RTM Background -- 2.3 Performance Spectrum with Error Progression -- 3 Approach -- 3.1 Detecting Uncertain Segments -- 3.2 Identifying Inter-case Dynamics in Uncertain Segments -- 3.3 Inter-case Feature Creation -- 3.4 Predicting the Next Segment -- 3.5 Predicting Waiting Time -- 4 Evaluation -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusion -- References -- Event Log Sampling for Predictive Monitoring -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Proposed Sampling Methods -- 5 Evaluation -- 5.1 Event Logs -- 5.2 Implementation -- 5.3 Evaluation Setting -- 5.4 Experimental Results -- 6 Discussion -- 7 Conclusion -- References -- Active Anomaly Detection for Key Item Selection in Process Auditing -- 1 Introduction -- 2 Related Work -- 2.1 Anomaly Detection -- 2.2 Active Anomaly Detection -- 2.3 Trace Visualisation -- 3 Active Selection Approach -- 3.1 Step One: Encode Process Data -- 3.2 Step Two: Assign Anomaly Score -- 3.3 Step Three: Actively Label Exceptions -- 4 Evaluation -- 4.1 Step One: Encode Process Data -- 4.2 Step Two: Assign Anomaly Score -- 4.3 Step Three: Actively Label Exceptions -- 4.4 Performance Results -- 5 Discussion -- 5.1 Cycle One -- 5.2 Cycle Two -- 5.3 Cycle Three -- 6 Limitations -- 7 Conclusion and Future Work -- References -- Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach -- 1 Introduction -- 2 Background and Related Work -- 2.1 Predictive Process Monitoring -- 2.2 Prescriptive Process Monitoring -- 2.3 Causal Inference -- 3 Approach -- 3.1 Log Preprocessing -- 3.2 Predictive Model -- 3.3 Causal Model -- 3.4 Resource Allocator -- 4 Evaluation -- 4.1 Dataset. | |
| 505 | 8 | _a4.2 Experiment Setup -- 4.3 Results -- 4.4 Threats to Validity -- 5 Conclusion -- References -- Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring -- 1 Introduction -- 2 Preliminaries -- 3 Explainability in OOPPM -- 3.1 Explainability Through Interpretability and Faithfulness -- 3.2 Logit Leaf Model -- 3.3 Generalized Logistic Rule Model -- 4 Experimental Evaluation -- 4.1 Benchmark Models -- 4.2 Event Logs -- 4.3 Implementation -- 4.4 Quantitative Metrics Results -- 5 Conclusion -- References -- SA4PM 2021: 2nd International Workshop on Streaming Analytics for Process Mining -- 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) -- Organization -- Workshop Chairs -- Program Committee -- Online Prediction of Aggregated Retailer Consumer Behaviour -- 1 Introduction -- 2 Framework -- 2.1 Features -- 2.2 Clustering -- 2.3 Training -- 2.4 Predicting -- 3 Experimental Evaluation -- 3.1 Experimental Setup -- 3.2 Results -- 4 Related Work -- 5 Conclusion and Future Work -- References -- PErrCas: Process Error Cascade Mining in Trace Streams -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Online Cascade Mining -- 4.1 Outlier Segment-Level Events -- 4.2 Error Cascade Construction -- 4.3 Cascade Patterns -- 5 Evaluation -- 5.1 Synthetic Data -- 5.2 Travel Reimbursement Process -- 6 Conclusion -- References -- Continuous Performance Evaluation for Business Process Outcome Monitoring -- 1 Introduction -- 2 Related Work -- 3 Continuous Prediction Evaluation Framework -- 4 Performance Evaluation Methods -- 4.1 Evaluating Performance Using a Local Timeline -- 4.2 Real-Time Model Performance -- 5 Experimental Analysis and Results -- 6 Conclusions -- References -- PQMI 2021: 6th International Workshop on Process Querying, Manipulation, and Intelligence. | |
| 505 | 8 | _a6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI 2021) -- Organization -- Workshop Organizers -- Program Committee -- An Event Data Extraction Approach from SAP ERP for Process Mining -- 1 Introduction -- 2 Background -- 2.1 Object-Centric Event Logs -- 2.2 SAP: Entities and Relationships -- 3 Extracting Event Data from SAP ERP: Approach -- 3.1 Building Graphs of Relations -- 3.2 Extracting Object-Centric Event Logs -- 4 Extracting Event Data from SAP ERP: Tool -- 5 Assessment -- 5.1 Building a Graph of Relations -- 5.2 Extracting Object-Centric Event Logs -- 6 Related Work -- 7 Conclusion -- References -- Towards a Natural Language Conversational Interface for Process Mining -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Pre-processing and Tagging -- 3.2 Semantic Parsing -- 3.3 PM Tool Interface Mapping -- 4 Sample Questions -- 5 Proof of Concept -- 6 Conclusions and Future Work -- References -- On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization -- 1 Introduction -- 2 Related Work -- 2.1 Generalization Metric -- 2.2 Adversarial System Variant Approximation -- 3 Notations -- 4 Problem Statement -- 5 Experimental Setup -- 5.1 Sampling Parameter -- 5.2 Variant Log Size -- 5.3 Biased Variant Logs -- 6 Results -- 6.1 Sampling Parameter Results -- 6.2 Variant Log Size Results -- 6.3 Biased Variant Log Results -- 7 Conclusion -- References -- PODS4H 2021: 4th International Workshop on Process-Oriented Data Science for Healthcare -- Fourth International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) -- Organization -- Workshop Chairs -- Program Committee -- Verifying Guideline Compliance in Clinical Treatment Using Multi-perspective Conformance Checking: A Case Study -- 1 Introduction -- 2 Background. | |
| 505 | 8 | _a3 Research Method. | |
| 588 | _aDescription based on publisher supplied metadata and other sources. | ||
| 590 | _aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. | ||
| 655 | 4 | _aElectronic books. | |
| 700 | 1 | _aLu, Xixi. | |
| 776 | 0 | 8 |
_iPrint version: _aMunoz-Gama, Jorge _tProcess Mining Workshops _dCham : Springer International Publishing AG,c2022 _z9783030985806 |
| 797 | 2 | _aProQuest (Firm) | |
| 856 | 4 | 0 |
_uhttps://ebookcentral.proquest.com/lib/ostimteknik/detail.action?docID=6938742 _zClick to View |
| 999 |
_c17444 _d17444 |
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