Название: Graph Databases: Applications on Social Media Analytics and Smart Cities
Автор: Christos Tjortjis
Издательство: CRC Press
Год: 2024
Страниц: 191
Язык: английский
Формат: pdf (true)
Размер: 12.8 MB

With social media producing such huge amounts of data, the importance of gathering this rich data, often called "the digital gold rush", processing it and retrieving information is vital. This practical book combines various state-of-the-art tools, technologies and techniques to help us understand Social Media Analytics, Data Mining and Graph Databases, and how to better utilize their potential. Graph Applications on Social Media Analytics and Smart Cities reviews social media analytics with examples using real-world data. It describes data mining tools for optimal information retrieval; how to crawl and mine data from Twitter; and the advantages of Graph Databases. The book is meant for students, academicians, developers and simple general users involved with Data Science and Graph Databases to understand the notions, concepts, techniques, and tools necessary to extract data from social media, which will aid in better information retrieval, management and prediction.

The contents of the book guide the interested reader into a tour starting with a detailed comparison of relational SQL Databases with NoSQL and Graph Databases, reviewing their popularity, with a focus on Neo4j.

Chapter 1 details the characteristics and reviews the pros and cons of relational and NoSQL databases assessing and explaining the increasing popularity of the latter, in particular when it comes to Neo4j. The chapter includes a categorization of NoSQL Database Management Systems (DBMS) into i) Column, ii) Document, iii) Key-value, iv) Graph and v) TimeSeries. Neo4j Use Cases and related scientific research are detailed, and the chapter concludes with an insightful discussion. It is essential reading for any reader who is not familiar with the related concepts before engaging with the following chapters.

Chapter 2 reviews the literature for graph databases and software libraries suitable for performing common social network analytic tasks. It proposes a taxonomy of graph database approaches for social network analytics based on the available algorithms and the provided means of storing, importing, exporting, and querying data, as well as the ability to deal with big social graphs, and the corresponding CPU and memory usage. Various graph technologies are evaluated by experiments related to the link prediction problem on datasets of diverse sizes.

Chapter 3 introduces novel capabilities for knowledge extraction by surveying Neo4j usage for social media. It highlights the importance of transitioning from SQL to NoSQL databases and proposes a categorization of Neo4j use cases in Social Media.
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Finally, Chapter 8 concludes with an interesting Graph-Based Data Model for Digital Health Applications in the context of smart cities.