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Graph Data Management

Book Description

Graph Data Management: Techniques and Applications is a central reference source for different data management techniques for graph data structures and their application. This book discusses graphs for modeling complex structured and schemaless data from the Semantic Web, social networks, protein networks, chemical compounds, and multimedia databases and offers essential research for academics working in the interdisciplinary domains of databases, data mining, and multimedia technology.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
  5. Foreword
  6. Preface
  7. Acknowledgment
  8. Section 1: Basic Challenges of Data Management in Graph Databases
    1. Chapter 1: Graph Representation
      1. Abstract
      2. Introduction
      3. CLASSICAL structures for representing graphs
      4. COMPRESSED ADJACENCY LISTS
      5. Improving the data locality of a graph
      6. Representing graphs via Resource Description Framework (RDF)
      7. Libraries implementing graphs
    2. Chapter 2: The Graph Traversal Pattern
      1. ABSTRACT
      2. INTRODUCTION
      3. THE REALIZATION OF GRAPHS
      4. GRAPH TRAVERSALS
      5. CONCLUSION
    3. Chapter 3: Data, Storage and Index Models for Graph Databases
      1. Abstract
      2. INTRODUCTION
      3. Data Models for Graph Databases
      4. Structure Indexes for graph databases
      5. Conclusion
    4. Chapter 4: An Overview of Graph Indexing and Querying Techniques
      1. ABSTRACT
      2. INTRODUCTION
      3. PRELIMINARIES
      4. SUPERGRAPH QUERY PROCESSING
      5. GRAPH SIMILARITY QUERIES
      6. GRAPH QUERY LANGUAGES
      7. DISCUSSION AND CONCLUSION
    5. Chapter 5: Efficient Techniques for Graph Searching and Biological Network Mining
      1. ABSTRACT
      2. INTRODUCTION
      3. SEARCHING IN A SINGLE GRAPH
      4. INDEXING AND MINING ON DATABASES OF GRAPHS
      5. LARGE GRAPH ANALYSIS IN BIOLOGY
      6. SUMMARY AND THE FUTURE
    6. Chapter 6: A Survey of Relational Approaches for Graph Pattern Matching over Large Graphs
      1. Abstract
      2. INTRODUCTION
      3. A Join Based Framework for Graph pattern Matching
      4. Different Join approaches
      5. Top-k graph pattern Matching
      6. Future research directions
      7. Summary
    7. Chapter 7: Labelling-Scheme-Based Subgraph Query Processing on Graph Data
      1. Abstract
      2. INTRODUCTION
      3. Background
      4. Labelling-based subgraph query processing1
      5. FUTURE RESEARCH DIRECTIONS
      6. Conclusion
      7. Appendix
  9. Section 2: Advanced Querying and Mining Aspects of Graph Databases
    1. Chapter 8: G-Hash
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. RELATED WORK
      5. FAST GRAPH SIMILARITY SEARCH WITH HASH FUNCTIONS
      6. APPLICATION OF SIMILARITY SEARCH IN CHEMICAL DATABASES
      7. CONCLUSION AND FUTURE WORK
    2. Chapter 9: TEDI
      1. Abstract
      2. INTRODUCTION
      3. Our Contributions
      4. Background
      5. Graph Indexing using tree decomposition
      6. Algorithms and Complexity Results
      7. Experimental Results
      8. FUTURE RESEARCH DIRECTIONS
      9. Conclusion
    3. Chapter 10: Graph Mining Techniques
      1. Abstract
      2. INTRODUCTION
      3. Background
      4. Graph Generators
      5. Graph Mining: The Problem of Distinguishing Real Networks from Synthetic Ones
      6. ShatterPlots
      7. Observations
      8. Scalability
      9. Conclusion
      10. FUTURE RESEARCH DIRECTIONS
    4. Chapter 11: Matrix Decomposition-Based Dimensionality Reduction on Graph Data
      1. Abstract
      2. INTRODUCTION
      3. RELATED RESEARCH
      4. Computational Experiments
      5. CONCLUSION
      6. Appendix
    5. Chapter 12: Clustering Vertices in Weighted Graphs
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MINIMUM SPANNING TREE ALGORITHM
      5. GRAPH ALGORITHMS FOR CLUSTERING
      6. PROPOSED GRAPH CLUSTERING ALGORITHMS
      7. MST-Sim: Proposed Variants of MST Clustering
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
    6. Chapter 13: Large Scale Graph Mining with MapReduce
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. TRIANGLE COUNTING
      5. EIGENTRIANGLE: Spectral Counting of Triangles
      6. IMPLEMENTATION DETAILS
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
    7. Chapter 14: Graph Representation and Anonymization in Large Survey Rating Data
      1. ABSTRACT
      2. INTRODUCTION
      3. MOTIVATION
      4. CONTRIBUTIONS
      5. RELATED WORK
      6. (K, ε)-ANONYMITY
      7. SURVEY RATING DATA ANONYMIZATION
      8. Distortion Metrics
      9. GRAPHICAL REPRESENTATION
      10. GRAPH MODIFICATION
      11. EXPERIMENTS
      12. DATA SETS
      13. EFFICIENCY
      14. DATA UTILITY
      15. STATISTICAL PROPERTIES
      16. CONCLUSION AND FUTURE WORK
  10. Section 3: Graph Database Applications in Various Domains
    1. Chapter 15: Querying RDF Data
      1. ABSTRACT
      2. INTRODUCTION
      3. RDF TERMINOLOGY
      4. QUERY LANGUAGES FOR THE SEMANTIC WEB
      5. THE SPARQL QUERY LANGUAGE
      6. Work on SPARQL
      7. SPARQL EXTENSIONS
      8. COMPARISON BETWEEN DIFFERENT QUERY LANGUAGES
      9. CONCLUSION
    2. Chapter 16: On the Efficiency of Querying and Storing RDF Documents
      1. Abstract
      2. INTRODUCTION
      3. BACKGROUND
      4. RDF BASED STORAGE AND QUERY EVALUATION
      5. Experimental Study
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
    3. Chapter 17: Graph Applications in Chemoinformatics and Structural Bioinformatics
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. NOTATION
      5. USE OF ALGORITHMS FROM GRAPH THEORY IN CHEMOINFORMATICS
      6. USE OF ALGORITHMS FROM GRAPH THEORY IN STRUCTURAL BIOINFORMATICS
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
    4. Chapter 18: Business Process Graphs
      1. ABSTRACT
      2. INTRODUCTION
      3. Background
      4. Similarity Search and Matching
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    5. Chapter 19: A Graph-Based Approach for Semantic Process Model Discovery
      1. ABSTRACT
      2. INTRODUCTION
      3. MOTIVATING SCENARIO
      4. RELATED WORKS
      5. OVERVIEW OF OWL-S ONTOLOGY
      6. A GRAPH-BASED APPROACH FOR PROCESS MODEL SIMILARITY EVALUATION
      7. Similarity Metrics
      8. Comparison Rules
      9. GRANULARITY LEVEL COMPARISON
      10. CONCLUSION
    6. Chapter 20: Shortest Path in Transportation Network and Weighted Subdivisions
      1. ABSTRACT
      2. Introduction
  11. About the Contributors
  12. Index