1st Edition
Graph Databases Applications on Social Media Analytics and Smart Cities
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 Databases: 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.
From Relational to NoSQL Databases - Comparison and Popularity. Graph Databases and the Neo4j Use Cases
Dimitrios Rousidis, and Paraskevas Koukaras
A Comparative Survey of Graph Databases and Software for Social Network Analytics: The Link Prediction Perspective
Nikos Kanakaris, Dimitrios Michail, and Iraklis Varlamis
A Survey on Neo4j Use Cases in Social Media: Exposing New Capabilities for Knowledge Extraction
Paraskevas Koukaras
Combining and Working with Multiple Social Networks on a Single Graph
Ahmet Anil MÜNGEN
Child Influencers on YouTube: From Collection to Overlapping Community Detection
Maximilian Paul Kißgen, Joachim Allgaier, and Ralf Klamma
Managing Smart City Linked Data with Graph Databases: An Integrative Literature Review
Anestis Kousis
Graph Databases in Smart City Applications - Using Neo4j and Machine Learning for Energy Load Forecasting
Aristeidis Mystakidis
A Graph-Based Data Model for Digital Health Applications
Jero Schäfer and Lena Wiese
Biography
Christos Tjortjis is an Associate Professor in Knowledge Discovery and Software Engineering systems. He is Dean of the School of Science and Technology at the International Hellenic University, and the Programme Director for the MSc in Data Science, the MSc in ICT Systems and the MSc in Smart Cities and Communities courses. He holds a Deng (Hons) in Computer Engineering and Informatics (5-year studies) from the Department of Computer Engineering & Informatics at the University of Patras, and a BSc (Hons) in Law (4-year studies) from the Department of Law at the Democritus University of Thrace, in Greece. He also holds an MPhil in Computation from UMIST and a PhD in Informatics from the University of Manchester, UK. His research focus is on data mining and analytics. He has published over 100 papers in international refereed journals and conferences. He leads the Data Mining and Analytics research group (DaMA). He is Associate Editor for the IET Smart Cities Journal, and Editorial Review Board Member for the International Journal of Information Retrieval Research.
