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xi | |
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xiii | |
Foreword |
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xv | |
Acknowledgments |
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xvii | |
Part I Introduction |
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3 | (22) |
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The Requirement Analysis Gap Revisited |
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3 | (2) |
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5 | (1) |
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The Knowledge Based Approaches |
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6 | (2) |
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8 | (10) |
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Introducing the Domain Knowledge |
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8 | (2) |
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The Knowledge based Formal Approach |
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10 | (1) |
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Ontology based Domain Analysis |
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11 | (1) |
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A Three-Layer Structure of Requirement Elicitation |
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11 | (3) |
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14 | (2) |
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Automatic Generation of Software Architecture |
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16 | (1) |
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Automation, Interaction and Evolution |
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17 | (1) |
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18 | (1) |
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The Eagle Projects and the PROMIS Tools |
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18 | (4) |
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18 | (1) |
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18 | (2) |
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20 | (1) |
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20 | (2) |
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22 | (3) |
Part II Domain Analysis and Domain Modeling |
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Ontology-Oriented Domain Analysis: The Foundation |
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25 | (48) |
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Domain Analysis and Domain Engineering |
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25 | (4) |
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DADL: Ontology-Oriented External Domain Knowledge Representation |
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29 | (7) |
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The Features of the Domain |
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30 | (1) |
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30 | (1) |
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31 | (1) |
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32 | (3) |
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35 | (1) |
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Ontology as Formal Knowledge Representation |
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36 | (2) |
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Can Object-Oriented Paradigm Express the Domain Knowledge? |
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36 | (1) |
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37 | (1) |
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A General Framework of Information Ontology |
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38 | (3) |
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A Mathematical Model for Ontology |
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41 | (3) |
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The Architecture of Knowledge Models |
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44 | (7) |
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The Design Principle of DOKB |
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44 | (2) |
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Family of Knowledge Models |
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46 | (5) |
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ONONET: The Internal Domain Model Representation |
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51 | (2) |
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INFORM: A Framework of Ontologies and Objects for Information System Modeling |
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53 | (11) |
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Basic Object Types in INFORM |
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53 | (2) |
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Organizing the Basic Entities with Relations |
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55 | (1) |
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The Basic Relation Types in INFORM |
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56 | (2) |
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The Basic Ontologies in INFORM |
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58 | (4) |
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62 | (2) |
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SHOP: A Domain Model of Shopping Centers |
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64 | (4) |
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Historical Remarks on Ontology like Domain Knowledge Representation |
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68 | (5) |
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Ontology-Oriented Domain Analysis: The Dynamics |
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73 | (50) |
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A Theory of Domain Classification |
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74 | (18) |
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Need for Domain Classification |
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74 | (1) |
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Enterprise Constructs and Repertory Grids |
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74 | (4) |
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Building Up the Repertory Grids |
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78 | (3) |
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Classifying the Enterprises |
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81 | (10) |
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Classifying the Attributes |
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91 | (1) |
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Build Virtual Domain Models: A Genetic Approach |
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92 | (12) |
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SONONET and Well-Formed Domain Models |
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104 | (11) |
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Interactive Operation for Constructing Domain Models |
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115 | (8) |
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116 | (1) |
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Tools for Constructing Domain Models |
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117 | (1) |
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118 | (5) |
Part III The Knowledge based Software Development |
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Automating the Requirement Analysis |
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123 | (44) |
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The Pseudo-Natural Language BIDL |
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123 | (7) |
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123 | (1) |
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124 | (6) |
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Formalizing the Pseudo-Natural Languages |
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130 | (4) |
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130 | (3) |
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Parsing Pseudo-Natural Language Texts based on Relational Grammars |
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133 | (1) |
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Pseudo-Natural Language for Pre-Requirement Analysis |
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134 | (3) |
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Requirement Acquisition from Texts |
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134 | (1) |
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The Pre-Requirement Analysis and its Automation |
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135 | (1) |
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Requirement Acquisition from Pseudo-Natural Language Texts: First Step of OORA |
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136 | (1) |
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IS-net: Transformational Semantics of BIDL |
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137 | (5) |
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Need for a Semantic Network Representation |
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137 | (1) |
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Syntax and Semantics of IS-net |
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138 | (4) |
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Interactive Knowledge Acquirer and Its Automation |
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142 | (12) |
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INKAI: The PROMIS Knowledge Acquirer |
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142 | (9) |
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Automated Construction of Interactive Knowledge Acquisition Interface |
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151 | (3) |
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Historical Remarks on the Pseudo-Natural Language Understanding PNLU |
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154 | (6) |
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Motivation for Introducing Pseudo-Natural Languages |
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154 | (1) |
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155 | (3) |
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First Experiences in PNLU |
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158 | (1) |
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Application of PNLU Techniques to Information Systems Modeling |
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158 | (1) |
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An Assessment of the PNLU Approach |
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159 | (1) |
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Historical Remarks on Semantic Network Representation |
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160 | (3) |
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160 | (2) |
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162 | (1) |
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Historical Remarks on Knowledge Acquirers |
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163 | (4) |
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OORA: Ontology Oriented Requirement Analysis |
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167 | (34) |
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On Executable Specification |
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167 | (1) |
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The Object-Oriented Analysis Revisited |
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168 | (3) |
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Ontology Recognition and Clustering |
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171 | (7) |
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The Procedure of Ontology Recognition and Clustering |
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171 | (4) |
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175 | (3) |
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Semantic Integrity of OORA |
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178 | (8) |
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What do We Mean by Semantic Integrity? |
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178 | (3) |
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The Small and the Grand BIDL |
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181 | (1) |
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Scalability of the Target Information System |
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182 | (1) |
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Completeness of the Target Information System |
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183 | (1) |
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Consistency of the Target Information System |
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184 | (1) |
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Normality of the Target Information System |
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185 | (1) |
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Intelligence of the Target Information System |
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186 | (1) |
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User Independent And User Dependent Models |
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186 | (15) |
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Why User Dependent Models? |
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186 | (1) |
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Strategy Library and User Model |
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187 | (2) |
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189 | (1) |
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189 | (4) |
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The Case based UDM Generator |
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193 | (2) |
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195 | (6) |
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Planning Software Architecture |
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201 | (28) |
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Issues on Software Architecture and Architecture Description Languages |
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201 | (8) |
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Motivation of Studying Software Architecture |
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201 | (2) |
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Different Software Architecture |
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203 | (4) |
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Architecture Description Languages |
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207 | (2) |
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The Architecture Description Language NEWCOM |
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209 | (8) |
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209 | (1) |
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210 | (3) |
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213 | (2) |
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215 | (1) |
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A Comparison of NEWCOM with Other Architecture Implementation Languages |
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216 | (1) |
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Planning the Client Server Architecture |
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217 | (7) |
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224 | (5) |
Part IV The Virtual Enterprise |
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Intelligent Information Service |
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229 | (40) |
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Motivation and Approaches |
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229 | (2) |
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A Technical Basis: Processing the Fuzzy Information |
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231 | (3) |
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The Architecture of PRINSE Data Warehouses |
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234 | (3) |
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Hierarchical and Typed Model of Data Warehouse |
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234 | (3) |
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Fuzzy Information Retrieval in Pseudo-Natural Language |
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237 | (7) |
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Natural Style Query Language NQL |
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237 | (2) |
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Query Language Interpreters |
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239 | (1) |
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240 | (3) |
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243 | (1) |
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Data Warehouse Builder WARDER |
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243 | (1) |
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Acquisition and Application of Temporal Knowledge |
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244 | (8) |
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244 | (1) |
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TKDL: A Language For Describing the Temporal Knowledge |
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245 | (3) |
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TKCM: A Compiler for Integrating the Temporal Knowledge |
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248 | (4) |
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Tendency Detection from Temporal Data |
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252 | (15) |
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Data Mining and Knowledge Discovery |
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252 | (1) |
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Learning Fuzzy Decision Trees |
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253 | (3) |
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Learning Fuzzy Decision Trees from Sequential and Incomplete Data |
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256 | (11) |
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Other Functional Agents of PRINSE |
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267 | (2) |
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Agents as Tendency Detector |
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267 | (1) |
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Agents as Exception Handlers |
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268 | (1) |
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268 | (1) |
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Software Reuse and System Evolution |
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269 | (42) |
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Software Evolution versus Software Reuse |
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269 | (3) |
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269 | (1) |
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Component Based and Knowledge Based Software Reuse |
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270 | (1) |
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271 | (1) |
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Horizontal System Evolution |
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272 | (10) |
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A General Schema of Software Reuse and Software Evolution in PROMIS |
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272 | (2) |
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Program Evolution at BIDL Level |
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274 | (6) |
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Program Evolution at NEWCOM Level |
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280 | (2) |
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Vertical Software Evolution |
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282 | (13) |
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Software Process as a Third Dimension of Software Evolution |
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282 | (2) |
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Evolution of Software Process in PROMIS |
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284 | (1) |
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Ontology as Software Process |
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285 | (4) |
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Executable Software Process Ontology |
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289 | (6) |
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295 | (5) |
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Meaning and Goals of Database Transformation |
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295 | (2) |
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297 | (3) |
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300 | (11) |
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Tool Evolution at BIDL Level |
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300 | (4) |
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Tool Evolution at Semantic Network Representation Level |
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304 | (1) |
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Evolution at Knowledge Base Level |
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305 | (6) |
Part V A Summary |
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311 | (1) |
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Combining Formal Methods with Knowledge Based Ones |
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311 | (2) |
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Ontology as Unified Representation Paradigm for Different Approaches |
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313 | (1) |
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313 | (1) |
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314 | (1) |
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The Jackson Development Method |
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314 | (2) |
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Entity Relationship Data Models |
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316 | (1) |
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Knowledge Based Fast Prototyping and a New Software Life Cycle |
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317 | (5) |
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Pseudo-Natural Language versus Natural Like Languages |
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322 | (2) |
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Pseudo-Natural Language versus Pseudo Code |
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324 | (2) |
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Pseudo-Natural Language versus Limited Natural Language |
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326 | (3) |
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329 | (1) |
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Knowledge Engineers versus Software Engineers |
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329 | (1) |
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Knowledge Industry versus Software Industry |
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330 | |