Dr. Pouria Amirian
Computer Science Department
Maynooth Strategic Research in Advanced Geotechnologies, National University of Ireland
UK

Biography
Dr. Pouria Amirian is a GIS/Computer Science lecturer, researcher and developer in National University of Ireland Maynooth. He holds M.Sc and Ph.D in GIS. Pouria has extensive experience in the design and development of various kinds of small-scale to enterprise-distributed service-oriented (geospatial) information systems. His most recent research projects are in the area of Geospatial Big Data and NoSQL databases.

Abstract
Challenges and Opportunities of Using NoSQL Databases for Managing Geospatial Big Data

We live in the age of Big Data. Big Data can be defined simply as huge volume of rapidly changing data from different sources and in different structures. Most of research about Big Data is focused on the major challenges in storage, analysis and visualization of Big Data. However when the Big Data has geospatial components (like geographical coordinates, street addresses, IP addresses) there are also another set of unique challenges in storage, analysis and visualization. The unique challenges are partly because of the special nature of geospatial data and the relationships among geospatial objects (like directional, distance and topological relationships) and partly because the special nature of analysis on the geospatial data. In this paper the data in the context of Big Data which has at least one geospatial component and that component is the core to the storage, analysis and visualization, is called Geospatial Big Data. Most datasets in Big Data context has the geospatial component (like street address) but the component doesn’t have any influence on the nature of storage, analysis and visualization of Big Data. In other words, when the geospatial component in Big Data is the core part of the data in its storage, analysis and visualization, it is called Geospatial Big Data. Traditionally, Relational Database Management Systems (RDBMS) were used to manage and to some extent analyze the geospatial data. Nowadays these systems can be used in many scenarios but there are some situations when using these systems may not provide the required efficiency and effectiveness. More specifically when the geospatial data has high volume, high frequency of change (in both data content and data structure) and variety of structures (Geospatial Big Data), the conventional data storage systems cannot provide needed efficiency in online systems in terms of performance, scalability and availability. In these situations, NoSQL solutions can provide the efficiency necessary for applications using geospatial data. This paper provides an overview of the characteristics of Geospatial Big Data, possible solutions for managing and processing them. Then the paper provides an overview of the major types of NoSQL solutions, their advantages and disadvantages and the challenges they present in managing Geospatial Big Data.