IEEE CS IT Professional Special Issue: Graph Databases

The management and analysis of huge volumes of unstructured data, connected through pervasive and diverse cross-links, increasingly permeates most applications, calling for new perspectives and approaches. The traditional, widely used relational database management system (RDBMS), which is most successful in process-stable and transaction-rich environments, faces limitations in dealing with fuzzy data and agile processes. Nonrelational or Not Only SQL (NoSQL) databases, one of the most significant developments to affect the industry since RDBMSs, accommodate mountains of unstructured data, offer greater flexibility in design and implementation, and support less stable processes.

Graph databases, a specialized NoSQL database, bring a novel dimension to traditional data management and are a timely addition to an organization’s data arsenal. Dealing with defined nodes and their multiple relationships (labeled edges), the graph database is far more conducive to our ever-more interconnected world and meets the needs of the growing network science field. Equipped with new capabilities, graph databases are attracting considerable interest among researchers and industry, and open up new opportunities.

This issue of IT Professional will explore several aspects of emerging graph databases and outline their applications and ongoing developments. We solicit articles on graph databases that discuss recent advances, research insights, novel applications, practical experiences and challenges, and future prospects. Topics of interest include, but are not limited to, the following:

  • Evolution of graph databases
  • Graph data structures
  • Graph database models
  • Graph query-programming languages
  • Smart data integration—integration of relational, graph, and cloud databases
  • Interoperability with other NoSQL databases
  • Visualization of complex networks through graph databases
  • Graph analytics of big data
  • Performance modeling, benchmarking, and scalability
  • Modeling strategies, especially for dealing with messy, inconsistent, and unstructured data
  • Standards and APIs to enable effective graph database content exchange
  • Applications (business, medical, logistics, cybersecurity, fraud detection, manufacturing, test and evaluation, human resources, network analysis)
  • Role in the Internet of Things and real-time sensor analytics
  • Network management using a graph database
  • Case studies and practical experiences
  • Security risks and governance of graph database applications and data
  • Technical, social, and organizational challenges
  • Graph database trends and future prospects

Important Dates

Submission deadline 1 April 2017
Publication  November/December 2017


Feature articles should be no longer than 4,200 words with no more than 20 references (with tables and figures counting as 300 words). Illustrations are welcome. For author guidelines, including sample articles, please see

Submit your article at


For more information, please contact the guest editors: