Label Hierarchy Inference in Property Graph Databases PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Label Hierarchy Inference in Property Graph Databases PDF full book. Access full book title Label Hierarchy Inference in Property Graph Databases by Fabian Klopfer. Download full books in PDF and EPUB format.
Author: Fabian Klopfer
Publisher: GRIN Verlag
ISBN: 3346504808
Category : Computers
Languages : en
Pages : 74
Get Book Here
Book Description
Bachelor Thesis from the year 2020 in the subject Computer Science - Miscellaneous, grade: 1.1, University of Constance, language: English, abstract: A lot of data contains implicit hierarchical structures, e.g. type hierarchies. The property graph model – among others employed in some graph databases – provides no tools to capture those internally. In this thesis we derive such hierarchies automatically. First a survey is conducted to find the most promising approaches that cluster a data set hierarchically. In the next step various features and vectors thereof are experimented with to extend the methodology to graphs, capturing the structure as well as possible. We found that there is not one specific feature vector that works well for all data sets and forms of representation in a graph, but rather needs to be constructed adaptive, depending on the way data is modelled. Finally, some extensions of a specific algorithm that was used during experimentation – namely Cobweb – are discussed as well as the use case of cardinality estimation in property graph databases, leveraging the hierarchy as an associative multi-level histogram.
Author: Fabian Klopfer
Publisher: GRIN Verlag
ISBN: 3346504808
Category : Computers
Languages : en
Pages : 74
Get Book Here
Book Description
Bachelor Thesis from the year 2020 in the subject Computer Science - Miscellaneous, grade: 1.1, University of Constance, language: English, abstract: A lot of data contains implicit hierarchical structures, e.g. type hierarchies. The property graph model – among others employed in some graph databases – provides no tools to capture those internally. In this thesis we derive such hierarchies automatically. First a survey is conducted to find the most promising approaches that cluster a data set hierarchically. In the next step various features and vectors thereof are experimented with to extend the methodology to graphs, capturing the structure as well as possible. We found that there is not one specific feature vector that works well for all data sets and forms of representation in a graph, but rather needs to be constructed adaptive, depending on the way data is modelled. Finally, some extensions of a specific algorithm that was used during experimentation – namely Cobweb – are discussed as well as the use case of cardinality estimation in property graph databases, leveraging the hierarchy as an associative multi-level histogram.
Author: Ian Robinson
Publisher: "O'Reilly Media, Inc."
ISBN: 1491930861
Category : Computers
Languages : en
Pages : 238
Get Book Here
Book Description
Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems. This second edition includes new code samples and diagrams, using the latest Neo4j syntax, as well as information on new functionality. Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution. Model data with the Cypher query language and property graph model Learn best practices and common pitfalls when modeling with graphs Plan and implement a graph database solution in test-driven fashion Explore real-world examples to learn how and why organizations use a graph database Understand common patterns and components of graph database architecture Use analytical techniques and algorithms to mine graph database information
Author: Carlos Ordonez
Publisher: Springer
ISBN: 3319985396
Category : Computers
Languages : en
Pages : 401
Get Book Here
Book Description
This book constitutes the refereed proceedings of the 20th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2018, held in Regensburg, Germany, in September 2018. The 13 revised full papers and 17 short papers presented were carefully reviewed and selected from 76 submissions. The papers are organized in the following topical sections: Graph analytics; case studies; classification and clustering; pre-processing; sequences; cloud and database systems; and data mining.
Author: Ian Robinson
Publisher: "O'Reilly Media, Inc."
ISBN: 1491930845
Category : Computers
Languages : en
Pages : 238
Get Book Here
Book Description
Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems. This second edition includes new code samples and diagrams, using the latest Neo4j syntax, as well as information on new functionality. Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution. Model data with the Cypher query language and property graph model Learn best practices and common pitfalls when modeling with graphs Plan and implement a graph database solution in test-driven fashion Explore real-world examples to learn how and why organizations use a graph database Understand common patterns and components of graph database architecture Use analytical techniques and algorithms to mine graph database information
Author: George Fletcher
Publisher: Springer
ISBN: 3319961934
Category : Computers
Languages : en
Pages : 186
Get Book Here
Book Description
This book presents a comprehensive overview of fundamental issues and recent advances in graph data management. Its aim is to provide beginning researchers in the area of graph data management, or in fields that require graph data management, an overview of the latest developments in this area, both in applied and in fundamental subdomains. The topics covered range from a general introduction to graph data management, to more specialized topics like graph visualization, flexible queries of graph data, parallel processing, and benchmarking. The book will help researchers put their work in perspective and show them which types of tools, techniques and technologies are available, which ones could best suit their needs, and where there are still open issues and future research directions. The chapters are contributed by leading experts in the relevant areas, presenting a coherent overview of the state of the art in the field. Readers should have a basic knowledge of data management techniques as they are taught in computer science MSc programs.
Author: William L. William L. Hamilton
Publisher: Springer Nature
ISBN: 3031015886
Category : Computers
Languages : en
Pages : 141
Get Book Here
Book Description
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Author: Aidan Hogan
Publisher: Morgan & Claypool Publishers
ISBN: 1636392369
Category : Computers
Languages : en
Pages : 257
Get Book Here
Book Description
This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.
Author: Tareq Abedrabbo
Publisher: Simon and Schuster
ISBN: 1638351996
Category : Computers
Languages : en
Pages : 441
Get Book Here
Book Description
Summary Neo4j in Action is a comprehensive guide to Neo4j, aimed at application developers and software architects. Using hands-on examples, you'll learn to model graph domains naturally with Neo4j graph structures. The book explores the full power of native Java APIs for graph data manipulation and querying. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Much of the data today is highly connected—from social networks to supply chains to software dependency management—and more connections are continually being uncovered. Neo4j is an ideal graph database tool for highly connected data. It is mature, production-ready, and unique in enabling developers to simply and efficiently model and query connected data. About the Book Neo4j in Action is a comprehensive guide to designing, implementing, and querying graph data using Neo4j. Using hands-on examples, you'll learn to model graph domains naturally with Neo4j graph structures. The book explores the full power of native Java APIs for graph data manipulation and querying. It also covers Cypher, Neo4j's graph query language. Along the way, you'll learn how to integrate Neo4j into your domain-driven app using Spring Data Neo4j, as well as how to use Neo4j in standalone server or embedded modes. Knowledge of Java basics is required. No prior experience with graph data or Neo4j is assumed. What's Inside Graph database patterns How to model data in social networks How to use Neo4j in your Java applications How to configure and set up Neo4j About the Authors Aleksa Vukotic is an architect specializing in graph data models. Nicki Watt, Dominic Fox, Tareq Abedrabbo, and Jonas Partner work at OpenCredo, a Neo Technology partner, and have been involved in many projects using Neo4j. Table of Contents PART 1 INTRODUCTION TO NEO4J A case for a Neo4j database Data modeling in Neo4j Starting development with Neo4j The power of traversals Indexing the data PART 2 APPLICATION DEVELOPMENT WITH NEO4J Cypher: Neo4j query language Transactions Traversals in depth Spring Data Neo4j PART 3 NEO4J IN PRODUCTION Neo4j: embedded versus server mode
Author: Angela Bonifati
Publisher: Springer Nature
ISBN: 3031018648
Category : Computers
Languages : en
Pages : 166
Get Book Here
Book Description
Graph data modeling and querying arises in many practical application domains such as social and biological networks where the primary focus is on concepts and their relationships and the rich patterns in these complex webs of interconnectivity. In this book, we present a concise unified view on the basic challenges which arise over the complete life cycle of formulating and processing queries on graph databases. To that purpose, we present all major concepts relevant to this life cycle, formulated in terms of a common and unifying ground: the property graph data model—the pre-dominant data model adopted by modern graph database systems. We aim especially to give a coherent and in-depth perspective on current graph querying and an outlook for future developments. Our presentation is self-contained, covering the relevant topics from: graph data models, graph query languages and graph query specification, graph constraints, and graph query processing. We conclude by indicating major open research challenges towards the next generation of graph data management systems.
Author: Ian Robinson
Publisher: "O'Reilly Media, Inc."
ISBN: 1449356222
Category : Computers
Languages : en
Pages : 161
Get Book Here
Book Description
Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems. Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution. Model data with the Cypher query language and property graph model Learn best practices and common pitfalls when modeling with graphs Plan and implement a graph database solution in test-driven fashion Explore real-world examples to learn how and why organizations use a graph database Understand common patterns and components of graph database architecture Use analytical techniques and algorithms to mine graph database information
