Acquisition and understanding of process knowledge using problem solving methods:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[Amsterdam]
IOS Press AKA
[2010]
|
| Schriftenreihe: | Studies on the Semantic Web
v. 007 |
| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 Volltext Inhaltsverzeichnis |
| Beschreibung: | Print version record |
| Beschreibung: | 1 online resource (x, 144 pages) illustrations |
| ISBN: | 1306284767 1607506009 1614993416 3898386392 9781306284769 9781607506003 9781614993414 9783898386395 |
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| 245 | 1 | 0 | |a Acquisition and understanding of process knowledge using problem solving methods |c José Manuel Gómez-Pérez |
| 264 | 1 | |a [Amsterdam] |b IOS Press |b AKA |c [2010] | |
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| 490 | 0 | |a Studies on the Semantic Web |v v. 007 | |
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| 505 | 8 | |a The development of knowledge-based systems is usually approached through the combined skills of knowledge engineers (KEs) and subject matter experts (SMEs). One of the most critical steps in this activity aims at transferring knowledge from SMEs to formal, machine-readable representations, which allow systems to reason with such knowledge. However, this is a costly and error prone task. Alleviating the knowledge acquisition bottleneck requires enabling SMEs with the means to produce the desired knowledge representations without the help of KEs. This is especially difficult application domains uncovers that process knowledge is one of the most freqent knowledge types, whose complexity requires specific means to enable SMEs to represent processes in a computational form. Additionally, such complexity and the increasigly large amount of data that process executions generate in knowledge-intensive domains, like Biology or Astronomy, requires analytical means with high abstraction capabilities to support SMEs in the analysis of such processes. This book presents methods and tools that enable SMEs to acquire process knowledge from the domains, formally represent such knowledge, reason about it, and understand process executions by analyzing their provenance. We describe the utilization of Problem Solving Methods as the main knowledge artifacts for process acquisition and analysis in two innovative ways. First, as formalizations of the reasoning strategies needed for processes and, second, as high-level, domain-independent, and reusable abstractions of process knowledge to provide SMEs with interpretati-ons of process executions--P.4 of cover | |
| 505 | 8 | |a Title Page; List of figures; List of Tables; Contents; Introduction; State of the Art; The Knowledge Acquisition Bottleneck; From Mining to Modelling: The Knowledge Level; Ontologies and Problem Solving Methods in the Knowledge Acquisition Modelling Paradigm; Knowledge Acquisition by Knowledge Engineers; Knowledge Acquisition by Subject Matter Experts; Process Knowledge and Subject Matter Experts; The Process Knowledge Lifecycle; Conclusions; Work Objectives; Goals and Open Research Problems; Contributions to the State of the Art; Work Assumptions, Hypotheses, and Restrictions | |
| 505 | 8 | |a Acquisition of Process Knowledge by SMEsIntroduction; Knowledge Acquisition and Formulation by SMEs in the Halo Project; Knowledge Types in Scientific Disciplines; Domain Analysis; A Comprehensive Set of Knowledge Types in Scientific Disciplines; The Process Metamodel; Process Entities in the Process Metamodel; Problem Solving Methods for the Acquisition of Process Knowledge; A PSM Modelling Framework for Processes; A Method to Build a PSM Library of Process Knowledge; A PSM Library for the Acquisition of Process Knowledge; Enabling SMEs to Formulate Process Knowledge | |
| 505 | 8 | |a The DarkMatter Process EditorRelated Work; Representing and Reasoning with SME-authored Process Knowledge; A Formalism for Representing and Reasoning with Process Knowledge; F-logic as Process Representation and Reasoning Language; The Process Frame; Code Generation for Process Knowledge; Synthesis of precedence rules for data flow management; Code Synthesis for Iterative Actions; Soundness and Completeness of Process Models; Optimization of the Synthesized Process Code; Reasoning with Process Models; Analysis of Process Executions by SMEs; Towards Knowledge Provenance in Process Analysis | |
| 505 | 8 | |a Problem Solving Methods for the Analysis of Process ExecutionsA Knowledgeoriented Provenance Environment; An Algorithm for Process Analysis Using PSMs; Evaluation; Evaluation of the DarkMatter Process Component for Acquisition of Process Knowledge by SMEs; Evaluation Syllabus; Distribution of the Formulated Processes across the Evaluation Syllabus; Utilization of the PSM Library and Process Metamodel; Usage Experience of the SMEs with the Process Editor; Performance Evaluation of the Process Component; Evaluation of KOPE for the Analysis of Process Executions by SMEs; Evaluation Settings | |
| 505 | 8 | |a Evaluation MetricsEvaluation Results; Evaluation Conclusions; Conclusions and Future Research; Conclusions; Future Research Problems; REFERENCES; Appendix. Sample F-logic Code for a Process Model | |
| 650 | 7 | |a COMPUTERS / General |2 bisacsh | |
| 650 | 7 | |a Knowledge acquisition (Expert systems) |2 fast | |
| 650 | 7 | |a Problem solving / Data processing |2 fast | |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Knowledge acquisition (Expert systems) | |
| 650 | 4 | |a Problem solving |x Data processing | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Gómez-Pérez, José Manuel |t Acquisition and understanding of process knowledge using problem solving methods |
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Datensatz im Suchindex
| _version_ | 1804175389449256960 |
|---|---|
| adam_text | CONTENTS
LIST OF FIGURES VI
LIST OF TABLES VIII
1. INTRODUCTION 1
2. STATE OF THE ART 5
2.1. THE KNOWLEDGE ACQUISITION BOTTLENECK 5
2.2. FROM MINING TO MODELLING: THE KNOWLEDGE LEVEL 5
2.3. ONTOLOGIES AND PROBLEM SOLVING METHODS IN THE KNOWLEDGE ACQUISITION
MODELLING
PARADIGM 7
2.4. KNOWLEDGE ACQUISITION BY KNOWLEDGE ENGINEERS 8
2.5. KNOWLEDGE ACQUISITION BY SUBJECT MATTER EXPERTS 9
2.6. PROCESS KNOWLEDGE AND SUBJECT MATTER EXPERTS 1 ]
2.7. THE PROCESS KNOWLEDGE LIFECYCLE 14
2.8. CONCLUSIONS IS
3. WORK OBJECTIVES 17
3.1. GOALS AND OPEN RESEARCH PROBLEMS 17
3.2. CONTRIBUTIONS TO THE STATE OF THE ART 19
3.3. WORK ASSUMPTIONS, HYPOTHESES, AND RESTRICTIONS 20
4. ACQUISITION OF PROCESS KNOWLEDGE BY SMES 24
4.1. INTRODUCTION 24
4.1.1. KNOWLEDGE ACQUISITION AND FORMULATION BY SMES IN THE HALO PROJECT
26
4.2. KNOWLEDGE TYPES IN SCIENTIFIC DISCIPLINES 27
4.2.1. DOMAIN ANALYSIS 28
4.2.2. A COMPREHENSIVE SET OF KNOWLEDGE TYPES IN SCIENTIFIC DISCIPLINES
30
4.3. THE PROCESS METAMODEL 32
4.3.1. PROCESS ENTITIES IN THE PROCESS METAMODEL 33
4.4. PROBLEM SOLVING METHODS FOR THE ACQUISITION OF PROCESS KNOWLEDGE 35
4.4.1. APSM MODELLING FRAMEWORK FOR PROCESSES 36
4.4.2. A METHOD TO BUILD A PSM LIBRARY OF PROCESS KNOWLEDGE 39
4.4.3. A PSM LIBRARY FOR THE ACQUISITION OF PROCESS KNOWLEDGE 41
4.5. ENABLING SMES TO FORMULATE PROCESS KNOWLEDGE 54
4.5.1. THE DARKMATTER PROCESS EDITOR 55
4.6. RELATED WORK 59
5. REPRESENTING AND REASONING WITH SME-AUTHORED PROCESS KNOWLEDGE 61
5.1. A FORMALISM FOR REPRESENTING AND REASONING WITH PROCESS KNOWLEDGE
61
5.2. F-LOGIC AS PROCESS REPRESENTATION AND REASONING LANGUAGE 65
5.3. THE PROCESS FRAME 67
5.4. CODE GENERATION FOR PROCESS KNOWLEDGE 69
SYNTHESIS OF PRECEDENCE RULES FOR DATA FLOW MANAGEMENT 75
5.5. CODE SYNTHESIS FOR ITERATIVE ACTIONS 76
5.6. SOUNDNESS AND COMPLETENESS OF PROCESS MODELS 79
5.7. OPTIMIZATION OF THE SYNTHESIZED PROCESS CODE 81
5.8. REASONING WITH PROCESS MODELS 83
6. ANALYSIS OF PROCESS EXECUTIONS BY SMES 89
6.1. TOWARDS KNOWLEDGE PROVENANCE IN PROCESS ANALYSIS 89
6.2. PROBLEM SOLVING METHODS FOR THE ANALYSIS OF PROCESS EXECUTIONS 92
6.3. A KNOWLEDGE-ORIENTED PROVENANCE ENVIRONMENT 96
6.4. AN ALGORITHM FOR PROCESS ANALYSIS USING PSMS 99
7. EVALUATION 103
BIBLIOGRAFISCHE INFORMATIONEN
HTTP://D-NB.INFO/1004897529
DIGITALISIERT DURCH
7.1. EVALUATION OF THE DARKMATTER PROCESS COMPONENT FOR ACQUISITION OF
PROCESS
KNOWLEDGE BY SMES 103
7.1.1. EVALUATION SYLLABUS 103
7.1.2. DISTRIBUTION OF THE FORMULATED PROCESSES ACROSS THE EVALUATION
SYLLABUS 105
7.1.3. UTILIZATION OF THE PSM LIBRARY AND PROCESS METAMODEL 107
7.1.4. USAGE EXPERIENCE OF THE SMES WITH THE PROCESS EDITOR 110
7.1.5. PERFORMANCE EVALUATION OF THE PROCESS COMPONENT 113
7.2. EVALUATION OF KOPE FOR THE ANALYSIS OF PROCESS EXECUTIONS BY SMES
114
7.2.1. EVALUATION SETTINGS 115
7.2.2. EVALUATION METRICS 117
7.2.3. EVALUATION RESULTS 119
7.3. EVALUATION CONCLUSIONS 121
8. CONCLUSIONS AND FUTURE RESEARCH 127
8.1. CONCLUSIONS 127
8.2. FUTURE RESEARCH PROBLEMS 129
REFERENCES 133
APPENDIX. SAMPLE F-LOGIC CODE FOR A PROCESS MODEL 142
|
| any_adam_object | 1 |
| author | Gómez-Pérez, José Manuel |
| author_facet | Gómez-Pérez, José Manuel |
| author_role | aut |
| author_sort | Gómez-Pérez, José Manuel |
| author_variant | j m g p jmg jmgp |
| building | Verbundindex |
| bvnumber | BV043032297 |
| collection | ZDB-4-EBA |
| contents | The development of knowledge-based systems is usually approached through the combined skills of knowledge engineers (KEs) and subject matter experts (SMEs). One of the most critical steps in this activity aims at transferring knowledge from SMEs to formal, machine-readable representations, which allow systems to reason with such knowledge. However, this is a costly and error prone task. Alleviating the knowledge acquisition bottleneck requires enabling SMEs with the means to produce the desired knowledge representations without the help of KEs. This is especially difficult application domains uncovers that process knowledge is one of the most freqent knowledge types, whose complexity requires specific means to enable SMEs to represent processes in a computational form. Additionally, such complexity and the increasigly large amount of data that process executions generate in knowledge-intensive domains, like Biology or Astronomy, requires analytical means with high abstraction capabilities to support SMEs in the analysis of such processes. This book presents methods and tools that enable SMEs to acquire process knowledge from the domains, formally represent such knowledge, reason about it, and understand process executions by analyzing their provenance. We describe the utilization of Problem Solving Methods as the main knowledge artifacts for process acquisition and analysis in two innovative ways. First, as formalizations of the reasoning strategies needed for processes and, second, as high-level, domain-independent, and reusable abstractions of process knowledge to provide SMEs with interpretati-ons of process executions--P.4 of cover Title Page; List of figures; List of Tables; Contents; Introduction; State of the Art; The Knowledge Acquisition Bottleneck; From Mining to Modelling: The Knowledge Level; Ontologies and Problem Solving Methods in the Knowledge Acquisition Modelling Paradigm; Knowledge Acquisition by Knowledge Engineers; Knowledge Acquisition by Subject Matter Experts; Process Knowledge and Subject Matter Experts; The Process Knowledge Lifecycle; Conclusions; Work Objectives; Goals and Open Research Problems; Contributions to the State of the Art; Work Assumptions, Hypotheses, and Restrictions Acquisition of Process Knowledge by SMEsIntroduction; Knowledge Acquisition and Formulation by SMEs in the Halo Project; Knowledge Types in Scientific Disciplines; Domain Analysis; A Comprehensive Set of Knowledge Types in Scientific Disciplines; The Process Metamodel; Process Entities in the Process Metamodel; Problem Solving Methods for the Acquisition of Process Knowledge; A PSM Modelling Framework for Processes; A Method to Build a PSM Library of Process Knowledge; A PSM Library for the Acquisition of Process Knowledge; Enabling SMEs to Formulate Process Knowledge The DarkMatter Process EditorRelated Work; Representing and Reasoning with SME-authored Process Knowledge; A Formalism for Representing and Reasoning with Process Knowledge; F-logic as Process Representation and Reasoning Language; The Process Frame; Code Generation for Process Knowledge; Synthesis of precedence rules for data flow management; Code Synthesis for Iterative Actions; Soundness and Completeness of Process Models; Optimization of the Synthesized Process Code; Reasoning with Process Models; Analysis of Process Executions by SMEs; Towards Knowledge Provenance in Process Analysis Problem Solving Methods for the Analysis of Process ExecutionsA Knowledgeoriented Provenance Environment; An Algorithm for Process Analysis Using PSMs; Evaluation; Evaluation of the DarkMatter Process Component for Acquisition of Process Knowledge by SMEs; Evaluation Syllabus; Distribution of the Formulated Processes across the Evaluation Syllabus; Utilization of the PSM Library and Process Metamodel; Usage Experience of the SMEs with the Process Editor; Performance Evaluation of the Process Component; Evaluation of KOPE for the Analysis of Process Executions by SMEs; Evaluation Settings Evaluation MetricsEvaluation Results; Evaluation Conclusions; Conclusions and Future Research; Conclusions; Future Research Problems; REFERENCES; Appendix. Sample F-logic Code for a Process Model |
| ctrlnum | (OCoLC)868068573 (DE-599)BVBBV043032297 |
| dewey-full | 006.3/31 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/31 |
| dewey-search | 006.3/31 |
| dewey-sort | 16.3 231 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV043032297 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:15:31Z |
| institution | BVB |
| isbn | 1306284767 1607506009 1614993416 3898386392 9781306284769 9781607506003 9781614993414 9783898386395 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028456948 |
| oclc_num | 868068573 |
| open_access_boolean | |
| owner | DE-1046 DE-1047 |
| owner_facet | DE-1046 DE-1047 |
| physical | 1 online resource (x, 144 pages) illustrations |
| psigel | ZDB-4-EBA ZDB-4-EBA FAW_PDA_EBA |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | IOS Press AKA |
| record_format | marc |
| series2 | Studies on the Semantic Web |
| spelling | Gómez-Pérez, José Manuel Verfasser aut Acquisition and understanding of process knowledge using problem solving methods José Manuel Gómez-Pérez [Amsterdam] IOS Press AKA [2010] © 2010 1 online resource (x, 144 pages) illustrations txt rdacontent c rdamedia cr rdacarrier Studies on the Semantic Web v. 007 Print version record The development of knowledge-based systems is usually approached through the combined skills of knowledge engineers (KEs) and subject matter experts (SMEs). One of the most critical steps in this activity aims at transferring knowledge from SMEs to formal, machine-readable representations, which allow systems to reason with such knowledge. However, this is a costly and error prone task. Alleviating the knowledge acquisition bottleneck requires enabling SMEs with the means to produce the desired knowledge representations without the help of KEs. This is especially difficult application domains uncovers that process knowledge is one of the most freqent knowledge types, whose complexity requires specific means to enable SMEs to represent processes in a computational form. Additionally, such complexity and the increasigly large amount of data that process executions generate in knowledge-intensive domains, like Biology or Astronomy, requires analytical means with high abstraction capabilities to support SMEs in the analysis of such processes. This book presents methods and tools that enable SMEs to acquire process knowledge from the domains, formally represent such knowledge, reason about it, and understand process executions by analyzing their provenance. We describe the utilization of Problem Solving Methods as the main knowledge artifacts for process acquisition and analysis in two innovative ways. First, as formalizations of the reasoning strategies needed for processes and, second, as high-level, domain-independent, and reusable abstractions of process knowledge to provide SMEs with interpretati-ons of process executions--P.4 of cover Title Page; List of figures; List of Tables; Contents; Introduction; State of the Art; The Knowledge Acquisition Bottleneck; From Mining to Modelling: The Knowledge Level; Ontologies and Problem Solving Methods in the Knowledge Acquisition Modelling Paradigm; Knowledge Acquisition by Knowledge Engineers; Knowledge Acquisition by Subject Matter Experts; Process Knowledge and Subject Matter Experts; The Process Knowledge Lifecycle; Conclusions; Work Objectives; Goals and Open Research Problems; Contributions to the State of the Art; Work Assumptions, Hypotheses, and Restrictions Acquisition of Process Knowledge by SMEsIntroduction; Knowledge Acquisition and Formulation by SMEs in the Halo Project; Knowledge Types in Scientific Disciplines; Domain Analysis; A Comprehensive Set of Knowledge Types in Scientific Disciplines; The Process Metamodel; Process Entities in the Process Metamodel; Problem Solving Methods for the Acquisition of Process Knowledge; A PSM Modelling Framework for Processes; A Method to Build a PSM Library of Process Knowledge; A PSM Library for the Acquisition of Process Knowledge; Enabling SMEs to Formulate Process Knowledge The DarkMatter Process EditorRelated Work; Representing and Reasoning with SME-authored Process Knowledge; A Formalism for Representing and Reasoning with Process Knowledge; F-logic as Process Representation and Reasoning Language; The Process Frame; Code Generation for Process Knowledge; Synthesis of precedence rules for data flow management; Code Synthesis for Iterative Actions; Soundness and Completeness of Process Models; Optimization of the Synthesized Process Code; Reasoning with Process Models; Analysis of Process Executions by SMEs; Towards Knowledge Provenance in Process Analysis Problem Solving Methods for the Analysis of Process ExecutionsA Knowledgeoriented Provenance Environment; An Algorithm for Process Analysis Using PSMs; Evaluation; Evaluation of the DarkMatter Process Component for Acquisition of Process Knowledge by SMEs; Evaluation Syllabus; Distribution of the Formulated Processes across the Evaluation Syllabus; Utilization of the PSM Library and Process Metamodel; Usage Experience of the SMEs with the Process Editor; Performance Evaluation of the Process Component; Evaluation of KOPE for the Analysis of Process Executions by SMEs; Evaluation Settings Evaluation MetricsEvaluation Results; Evaluation Conclusions; Conclusions and Future Research; Conclusions; Future Research Problems; REFERENCES; Appendix. Sample F-logic Code for a Process Model COMPUTERS / General bisacsh Knowledge acquisition (Expert systems) fast Problem solving / Data processing fast Datenverarbeitung Knowledge acquisition (Expert systems) Problem solving Data processing Erscheint auch als Druck-Ausgabe Gómez-Pérez, José Manuel Acquisition and understanding of process knowledge using problem solving methods http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=683316 Aggregator Volltext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028456948&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Gómez-Pérez, José Manuel Acquisition and understanding of process knowledge using problem solving methods The development of knowledge-based systems is usually approached through the combined skills of knowledge engineers (KEs) and subject matter experts (SMEs). One of the most critical steps in this activity aims at transferring knowledge from SMEs to formal, machine-readable representations, which allow systems to reason with such knowledge. However, this is a costly and error prone task. Alleviating the knowledge acquisition bottleneck requires enabling SMEs with the means to produce the desired knowledge representations without the help of KEs. This is especially difficult application domains uncovers that process knowledge is one of the most freqent knowledge types, whose complexity requires specific means to enable SMEs to represent processes in a computational form. Additionally, such complexity and the increasigly large amount of data that process executions generate in knowledge-intensive domains, like Biology or Astronomy, requires analytical means with high abstraction capabilities to support SMEs in the analysis of such processes. This book presents methods and tools that enable SMEs to acquire process knowledge from the domains, formally represent such knowledge, reason about it, and understand process executions by analyzing their provenance. We describe the utilization of Problem Solving Methods as the main knowledge artifacts for process acquisition and analysis in two innovative ways. First, as formalizations of the reasoning strategies needed for processes and, second, as high-level, domain-independent, and reusable abstractions of process knowledge to provide SMEs with interpretati-ons of process executions--P.4 of cover Title Page; List of figures; List of Tables; Contents; Introduction; State of the Art; The Knowledge Acquisition Bottleneck; From Mining to Modelling: The Knowledge Level; Ontologies and Problem Solving Methods in the Knowledge Acquisition Modelling Paradigm; Knowledge Acquisition by Knowledge Engineers; Knowledge Acquisition by Subject Matter Experts; Process Knowledge and Subject Matter Experts; The Process Knowledge Lifecycle; Conclusions; Work Objectives; Goals and Open Research Problems; Contributions to the State of the Art; Work Assumptions, Hypotheses, and Restrictions Acquisition of Process Knowledge by SMEsIntroduction; Knowledge Acquisition and Formulation by SMEs in the Halo Project; Knowledge Types in Scientific Disciplines; Domain Analysis; A Comprehensive Set of Knowledge Types in Scientific Disciplines; The Process Metamodel; Process Entities in the Process Metamodel; Problem Solving Methods for the Acquisition of Process Knowledge; A PSM Modelling Framework for Processes; A Method to Build a PSM Library of Process Knowledge; A PSM Library for the Acquisition of Process Knowledge; Enabling SMEs to Formulate Process Knowledge The DarkMatter Process EditorRelated Work; Representing and Reasoning with SME-authored Process Knowledge; A Formalism for Representing and Reasoning with Process Knowledge; F-logic as Process Representation and Reasoning Language; The Process Frame; Code Generation for Process Knowledge; Synthesis of precedence rules for data flow management; Code Synthesis for Iterative Actions; Soundness and Completeness of Process Models; Optimization of the Synthesized Process Code; Reasoning with Process Models; Analysis of Process Executions by SMEs; Towards Knowledge Provenance in Process Analysis Problem Solving Methods for the Analysis of Process ExecutionsA Knowledgeoriented Provenance Environment; An Algorithm for Process Analysis Using PSMs; Evaluation; Evaluation of the DarkMatter Process Component for Acquisition of Process Knowledge by SMEs; Evaluation Syllabus; Distribution of the Formulated Processes across the Evaluation Syllabus; Utilization of the PSM Library and Process Metamodel; Usage Experience of the SMEs with the Process Editor; Performance Evaluation of the Process Component; Evaluation of KOPE for the Analysis of Process Executions by SMEs; Evaluation Settings Evaluation MetricsEvaluation Results; Evaluation Conclusions; Conclusions and Future Research; Conclusions; Future Research Problems; REFERENCES; Appendix. Sample F-logic Code for a Process Model COMPUTERS / General bisacsh Knowledge acquisition (Expert systems) fast Problem solving / Data processing fast Datenverarbeitung Knowledge acquisition (Expert systems) Problem solving Data processing |
| title | Acquisition and understanding of process knowledge using problem solving methods |
| title_auth | Acquisition and understanding of process knowledge using problem solving methods |
| title_exact_search | Acquisition and understanding of process knowledge using problem solving methods |
| title_full | Acquisition and understanding of process knowledge using problem solving methods José Manuel Gómez-Pérez |
| title_fullStr | Acquisition and understanding of process knowledge using problem solving methods José Manuel Gómez-Pérez |
| title_full_unstemmed | Acquisition and understanding of process knowledge using problem solving methods José Manuel Gómez-Pérez |
| title_short | Acquisition and understanding of process knowledge using problem solving methods |
| title_sort | acquisition and understanding of process knowledge using problem solving methods |
| topic | COMPUTERS / General bisacsh Knowledge acquisition (Expert systems) fast Problem solving / Data processing fast Datenverarbeitung Knowledge acquisition (Expert systems) Problem solving Data processing |
| topic_facet | COMPUTERS / General Knowledge acquisition (Expert systems) Problem solving / Data processing Datenverarbeitung Problem solving Data processing |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=683316 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028456948&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT gomezperezjosemanuel acquisitionandunderstandingofprocessknowledgeusingproblemsolvingmethods |