From Streams To Fields (NSF)

III: Small: From Real-Time Sensor Data Streams to Continuous Data Fields Models: Formal Foundations and Computational Challenges

This work is funded through the National Science Foundation (NSF Award Number: 1527504) (2015-2020)

Principal Investigator:

Silvia Nittel

Co-Principal Investigator:

Max Egenhofer

Academic Institution:

School of Computing and Information Science, University of Maine

Summary:

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 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 measurement 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 efforts based on accurate and timely information; this will help to protect the public interests better.

The research in this project develops a formal foundation for sensor data streams by abstracting them as geographic fields, and a scalable computational framework that computes field operators on massive numbers of sensor data streams in near real-time.

In this research,

  • the field algebra, with a recursive definition of fields and a set of field operators, are formalized.
  • The field algebra and data streams are formally integrated on the level of their mathematical foundations.
  • The formal field algebra is implemented as a data type hierarchy and integrated with stream data models.
  • At the same time, a computational framework is developed that extends data stream engines with computational components to estimate spatio-temporal fields based on recursive or transposed field definitions, and the evaluation of complex predicates over fields, which lays the foundation for co-analyzing live and historic fields.

Project-Related Publications:

Extending Stream Data Models with Fields:

  1. Iranga Subasinghe, Silvia Nittel, Michael Cressey, Prashanta Bajracharya, Melissa Landon, Mapping Natural Disaster in Real-time Using Citizen Update Streams, International Journal of Geographic Information Systems, Special Issue: Real-time GIS and Smart Cities. July 2019, Pages 393-421, https://doi.org/10.1080/13658816.2019.1639185.
  2. J.C. Whittier, Towards an efficient, scalable stream query
    operator framework for representing and analyzing continuous fields, Ph.D. dissertation, University of Maine, August 2018.
  3. Q. Liang, S. Nittel, J.C. Whittier, S. De Bruin, Real-time Spatial Inverse Distance Weighting for Streaming Sensor Data, Journal Transactions of GIS, Vol. 22(5), pp 1179-1204, 2018.
  4. J.C. Whittier, S. Nittel, I. Subasinghe, “Real-Time Earthquake Monitoring with Spatio-Temporal Fields”, in Proc. 2nd International Symposium on “Spatiotemporal Computing”, Harvard, MA, Aug 7-9 2017.
  5. Q. Liang, S. Nittel, and T. Hahmann,
    From Data Streams to Fields: Extending Stream Data Models with Field Data Types,
    9th International Conference on Geographic Information Science (GIScience), Springer LNCS, Montreal, Canada, September 2016.
  6. J. Lewis and M. Egenhofer (2016). Point Partitions: A Qualitative Representation for Region-Based Spatial Scenes in R^2, Geographic Information Science.  9th International Conference on Geographic Information Science (GIScience), Springer LNCS, Montreal, Canada, September 2016.
  7. M. Egenhofer, K. Clarke, S. Gao, T. Quesnot, W.R. Franklin, M. Yuan, and D. Coleman (2015). Contributions of GIScience over the Past Twenty Years. Advancing Geographic Information Science: The Past and Next Twenty Years  H.~Onsrud and W.~Kuhn.  GSDI Association Press.  Needham, MA.
  8. S. Nittel, L. Bodum, K. Clarke, M. Gould, P. Raposo, J. Sharma, and M. Vasardani (2015). Emerging Technological Trends likely to Affect GIScience in the Next 20 Years. Advancing Geographic Information Science: The Past and Next Twenty Years  H.~Onsrud and W.~Kuhn.

Implementations and Demos:

Presentations:

  • Michael Cressey, Spatio-Temporal Fields over Real-time Sensor Streams Using Spark, M.Sc. project presentation, November 2019.
  • J.C. Whittier, Towards an efficient, scalable stream query operator framework for representing and analyzing continuous fields, public Ph.D. defense, April 27, 2018, University of Maine.
  • J.C. Whittier, S. Nittel, I. Subasinghe, Real-Time Earthquake Monitoring with Spatio-Temporal Fields,  at 2nd International Symposium on “Spatiotemporal Computing”, Harvard, MA, Aug 7, 2017.
  • Qinghan Liang,  From Data Streams to Fields: Extending Stream Data Models with Field Data Types, 9th International Conference on Geographic Information Science (GIScience), Montreal, Canada, September 2016.

Teaching:

  • Dr. Nittel has integrated the implementation of Spatio-temporal fields with Spark in her SIE558 Real-time Sensor Data Streams class and provided hands-on experience of using fields to process Spatio-temporal streams with Spark to a graduate class (Fall 2019).
  • Dr. Nittel is integrating the architecture and use of Spark in her current class SIE555 Spatial Database Systems as an hands-on example of future database technology.

Outreach:

Students supported:

  • J.C. Whittier (2015-2018, graduated with Ph.D.; Advisor: Silvia Nittel). Dissertation “Towards an efficient, scalable stream query operator framework for representing and analyzing continuous fields”
  • Iranga Subashinghe (Ph.D. program, since January 2018, Advisor: Silvia Nittel): works on citizen update streams and ST-streams support in Spark
  • Dilrukshi Abeyrathne (Ph.D., since September 2018, Ph.D. advisor Silvia Nittel, Project Advisor: Dr. Egenhofer): working with Dr. Egenhofer on defining on field types using Haskell
  • Michael Cressey (2017-2019, graduated with an M.Sc. in Spatial Information Science and Engineering, Advisor: Silvia Nittel): Topic: Spatio-Temporal Fields over Real-time Sensor Streams Using Spark

 

Last Update: Jan 27, 2020