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基本説明
Describes a new way of building global agreements (semantic interoperability) based only on decentraized, self-organizing interactions.
Full Description
Peer-to-peer systems are evolving with new information-system architectures, leading to the idea that the principles of decentralization and self-organization will offer new approaches in informatics, especially for systems that scale with the number of users or for which central authorities do not prevail. This book describes a new way of building global agreements (semantic interoperability) based only on decentralized, self-organizing interactions.
Contents
PrefaceChapter 1 Introduction1.1 On Syntax, Semantics and Syntactic Semantics1.2 Emergent Semanticsin Distributed Information Systems1.3 Scope of the Research1.4 What this Book is not about1.5 Outline1.6 ContributionsChapter 2 On Integrating Datain the Internet Era2.1 Federated Databases2.2 XML, RDF and the Semantic WebChapter 3 Peer-to-Peer Information Management3.1 From unstructured to structured P2P Systems3.2 Peer Data ManagementChapter 4 Semantic Gossiping4.1 On Uncertain Schema Mappingsin Decentralized Settings4.1.1 Mapping Completeness4.1.2 Mapping Soundness4.2 The Model4.2.1 The Data Model4.2.2 The Network Model4.3 Overview4.4 Syntactic Similarity4.5 Semantic Similarity4.5.1 Cycle Analysis4.5.2 Result Analysis4.6 Gossiping Algorithm4.7 Case Study4.8 Related Work4.9 ConclusionsChapter 5 Self-Repairing Semantic Networks 5.1 Experimental setup5.2 Cycle Analysis5.3 Result Analysis5.4 Combined Analysis5.5 Related Work5.6 ConclusionsChapter 6 Probabilistic Message Passing 6.1 Introduction6.2 Problem Definition6.2.1 An Introductory Example6.3 Modeling PDMSs as Factor-Graphs6.3.1 A Quick Reminder on Factor-Graphsand Message Passing Schemes6.3.2 On Factor-Graphsin Undirected PDMSs6.3.3 On Factor-Graphs in Directed PDMSswith Containment Mappings6.4 Embedded Message Passing6.4.1 On Feedback Variablesin PDMS Factor-Graphs6.4.2 On Cycles in PDMS Factor-Graphs6.4.3 Embedded Message Passing Schedules6.4.4 Prior Belief Updates6.4.5 Introductory Example Revisited6.5 Performance Evaluation6.5.1 Performance Analyses6.5.2 Performance Evaluationon Random PDMS Networks6.5.3 Applying Message Passingon Real-World Schemas6.6 ConclusionsChapter 7 Analyzing Semantic Interoperability in the Large7.1 Introduction7.2 The Model7.2.1 The Peer-to-Peer Model7.2.2 The Peer-to-Schema Model7.2.3 The Schema-to-Schema Model7.3 Semantic Interoperability In the Large7.3.1 Semantic Connectivity7.4 A Necessary Conditionfor Semantic Interoperability7.4.1 Undirected Model7.4.2 Directed Model7.5 Semantic Component Size7.6 Weighted Graphs7.6.1 Connectivity Indicator7.6.2 Giant Component Size7.7 Semantic Interoperabilityin a Bioinformatic Database Network7.7.1 The Sequence Retrieval System (SRS) 7.7.2 Graph analysis of an SRS repository7.7.3 Applying the Heuristicsto the SRS Graph7.7.4 Generating a Graph witha given Power-Law Degree Distribution7.8 Use Case Scenarios7.9 ConclusionsChapter 8 GridVine:Building Internet-ScaleSemantic Overlay Networks 8.1 Introduction8.2 Overview of our Approach8.2.1 Data Independence8.2.2 Decentralized Semantics8.3 The P-Grid P2P System8.4 Semantic Support8.4.1 Metadata Storage8.4.2 Schema Definition And Storage8.5 Resolving Queries in GridVine8.5.1 Resolving Atomic Queries8.5.2 Resolving Conjunctive Queries8.6 Semantic Interoperability8.6.1 Schema Inheritance8.6.2 Semantic Gossiping8.7 Implementation8.7.1 Architectural Overview8.7.2 Querying8.7.3 Query Reformulation8.7.4 Experimental Evaluation8.8 Related Work8.9 ConclusionsChapter 9 PicShark: Sharing Semi-Structured Annotationsin the Large 9.1 Introduction.9.2 Sharing Semi-Structured Metadata9.2.1 On Semi-Structured Metadata9.2.2 On the Difficultyof Sharing Semi-Structured Metadata9.2.3 Opportunities for ReducingMetadata Scarcity Collaboratively9.3 Formal Model9.3.1 Metadata Entropy9.4 Recontextualizing Semi-Structured Metadata9.4.1 Exporting Local Metadatathrough Data Indexing9.4.2 Dealing with Metadata Incompletenessthrough Intra-Community MetadataImputation9.4.3 Dealing with Metadata Heterogeneitythrough Pairwise Schema Mappings9.4.4 Dealing with Metadata Incompletenessthrough Inter-Community MetadataPropagation9.4.5 Possible Answers and User Feedback9.5 PicShark: Sharing Annotated Picturesin the Large9.5.1 Information Extraction in PicShark9.5.2 Performance Evaluation9.6 Related Work9.7 ConclusionsChapter 10 idMesh: Graph-Based Disambiguationof Online Identities10.1 Introduction10.2 Contributions and Outline10.3 Related Work10.4 Problem Definition10.5 idMesh Constructs10.6 Making Sense of It10.6.1 An Introductory Example10.6.2 Deriving a Factor-Graphto Retrieve Equivalent Identities10.6.3 Deriving a Factor-Graphto Retrieve Up-to-date Identities10.6.4 Query Answering10.7 System Perspective10.7.1 Architectural Overview10.7.2 Distributed Probabilistic Inference10.8 Performance Evaluation10.8.1 Performance of the Inference Network10.8.2 Scale-Up10.9 ConclusionsChapter 11 Conclusions List of Frequently Used Symbols and Abbreviations Bibliography
