With the rise of location-aware IoT devices, there is an increased desire to process and manage the stationary and moving trajectory data generated by these real-time sensors. There has been a corresponding evolution of distributed database and compute technology to handle the increasing data load. Here we describe challenges in managing this kind of data and survey the technologies that address those challenges. We evaluate two distributed database technologies against a simulated high volume sensor data source and discuss the performance of predominant moving trajectory access patterns.

Big Data has driven the need for datastores that can scale horizontally leading to the development of many different NoSQL database implementations, each with different persistence and query philosophies. Spatio-temporal data such as location data is one of the largest types of data being collected today. We describe a novel spatio-temporal index structure that leverages the horizontal scalability of NoSQL databases to achieve performant query and transformation semantics. We present performance characteristics gathered from testing with Accumulo.

Abstract One benefit of a computational Grid is the ability to run high‐performance applications over distributed resources simply and securely. We demonstrated this benefit with an experiment in which we studied the protein‐folding process with the CHARMM molecular simulation package over a Grid managed by Legion, a Grid operating system. High‐performance applications can take advantage of Grid resources if the Grid operating system provides both low‐level functionality as well as high‐level services. We describe the nature of services provided by Legion for high‐performance applications. Our experiences indicate that human factors continue to play a crucial role in the configuration of Grid resources, underlying resources can be problematic, Grid services must tolerate underlying problems or inform the user, and high‐level services must continue to evolve to meet user requirements. Our experiment not only helped a scientist perform an important study, but also showed the viability of an integrated approach such as Legion's for managing a Grid. Copyright © 2004 John Wiley & Sons, Ltd.

One benefit of a computational grid is the ability to run high-performance applications over distributed resources simply and securely. We demonstrate this benefit with an experiment in which we studied the protein folding process with the CHARMM molecular simulation package over a grid managed by Legion, a grid operating system. High-performance applications can take advantage of grid resources if the grid operating system provides both low level functionality as well as high-level services. We describe the nature of services provided by Legion for high-performance applications. Our experiences indicate that human factors continue to play a crucial role in the configuration of grid resources, underlying resources can be problematic, grid services must tolerate underlying problems or inform the user, and high-level services must continue to evolve to meet user requirements. Our experiment not only helped a scientist perform an important study, but also showed the viability of an integrated approach such as Legion's for managing a grid.