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Graph Databases in Action

Graph Databases in Action teaches readers everything they need to know to begin building and running applications powered by graph;

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Graph Databases

Discover how graph databases can help you manage and query highly connected data. With this practical book, you'll learn how to design and;

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Graph Algorithms And Applications 3

, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering;

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Graph Algorithms And Applications 5

, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering;

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Querying Graphs

concise unified view on the basic challenges which arise over the complete life cycle of formulating and processing queries on graph databases. To;

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Advances in Databases and Information Systems

; subsequence matching and streaming data; OLAP; graph databases; spatial data management; parallel and distributed data processing; query optimization;

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The Practitioner's Guide to Graph Data Applying Graph Thinking and Graph Technologies to Solve Complex Problems

analysts how to solve complex problems with graph databases. You'll explore templates for building with graph technology, along with examples that;

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Graph Algorithms And Applications 4

, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering;

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International Symposium on Computer and Information Sciences

networks, databases, evolutionary computation, graph theory, image processing, multimedia technology, software engineering, and software;

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Data Mining In Time Series Databases

Adding the time dimension to real-world databases produces Time Series Databases (TSDB) and introduces new aspects and difficulties to data;

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Data Analytics

This book constitutes the refereed conference proceedings of the 31st British International Conference on Databases, BICOD 2017 - formerly;

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Biological Network Analysis

Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene;

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NoSQL Data Models

needed. This book presents recent research works, covering the following basic aspects: semantic data management, graph databases, and big data;

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Model and Data Engineering

, concurrent systems, machine learning, swarm optimization, query processing, semantic web, graph databases, formal methods, model-driven engineering;

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Web and Big Data

were carefully reviewed and selected from 184 submissions. The papers are organized around the following topics: Graph Mining; Data Mining;

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Database and Expert Systems Applications: 29th International Conference, Dexa 2018, Regensburg, Germany, September 3-6, 2018, Proceedings, Part I

networks; temporal and spatial databases; and graph data and road networks. The papers of the second volume discuss a range of the following topics;

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Web and Big Data

; Knowledge Graph; Machine Learning; Recommender Systems; Storage, Indexing and Physical Database Design; Spatial, Temporal and Multimedia Databases;

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Web and Big Data

; Knowledge Graph; Machine Learning; Recommender Systems; Storage, Indexing and Physical Database Design; Spatial, Temporal and Multimedia Databases;

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PySpark SQL Recipes

Carry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides;

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Network Security Through Data Analysis

security data Store data traffic with relational databases, graph databases, Redis, and Hadoop Use SiLK, the R language, and other tools for;

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Web and Big Data

submissions. The papers are organized around the following topics: Big Data Analytics; Graph Data and Social Networks; Knowledge Graph; Recommender;

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Web and Big Data

submissions. The papers are organized around the following topics: Big Data Analytics; Graph Data and Social Networks; Knowledge Graph; Recommender;

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Visualizing Graph Data

visualization tools AUDIENCE A basic understanding of RDBMS systems is assumed. No previous knowledge of graph databases;

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Model and Data Engineering

, semantic web, graph databases, formal methods, model-driven engineering, blockchain, cyber physical systems, IoT applications, and smart systems;

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SQL NoSQL Databases

Data Modeling and Consistency Chapter-length treatment is afforded Data Modeling in both relational and graph databases, including;

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Combinatorial Algorithms

, Complexity Theory (Structural and Computational), Computational Biology, Databases (Security, Compression and Information Retrieval), Decompositions;

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Keyword Search in Databases

It has become highly desirable to provide users with flexible ways to query/search information over databases as simple as keyword search;

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