Nittel and Egenhofer receive $500,000 National Science Foundation research award

We are pleased to announce a recent National Science Foundation (NSF) award made to Drs. Silvia Nittel and Max Egenhofer, School of Computing and Information Science, University of Maine with a total amount of $500,000.00.

This project, entitled “III: Small: From Real-Time Sensor Data Streams to Continuous Field Data Models: Formal Foundations and Computational Challenges,” is under the direction of Silvia Nittel. The award has started Sep. 01, 2015.

Project Scope:

Massive sensor data streams are created from the automatic collection of sensor data in high frequency and in near real-time today. This project aims to advance the analytical potential of live-streamed data, historical data streams, and model simulations by creating an overarching representation in the form of the field data model with a set of operators that establish the field algebra.

A field is best explained as, for example, a magnetic field; the magnetic force can be determined for each point in a magnetic field and the field is therefore considered to be continuous. Similarly, environmental phenomena such as air pollution or flooding are considered to be continuous in space and time although they are sampled at limited, discrete time-space locations with sensors. This project develops the field algebra which is an intuitive, yet mathematically defined formalism to represent real-world phenomena as fields and to express analytical needs as canonical operations over fields. The field model represents phenomena as continuous entities again, and the implementation hides the fact that their spatio-temporal continuity is calculated on the-fly based on real-time measurements streams.

Extending sensor data streams to fields is transformative, as rarely a domain scientist is interested in the readings of individual sensors. Allowing scientists to work with high-level abstractions will significantly enhance their analytical tasks such as finding insights about changes, trends, or unexpected events happening in the real world. The project will integrate fields and data streams mathematically so that mappings between both are well-defined. The field data model is complemented by the development of an innovative computational framework for synthesizing and analyzing fields based on very large numbers of high throughput, real-time sensor data streams, and for creating continuous representations on-the-fly. This framework provides novel algorithms to assure that the field operators can absorb the throughput of very large numbers of sensor data streams, yet still compute complex analytical results in near real-time. This project will benefit our society by enabling us to react to situations such as extreme weather events, environmental disasters or chemical accidents immediately, and organize response effort based on accurate and timely information; this will help to protect the public interests better.

More information and results will be made available on the project website (