Our paper “From Data Streams to Fields: Extending Stream Data Models with Field Data Types” by Qinghan Liang, Silvia Nittel and Torsten Hahmann was accepted in the full paper track at GIScience 2016.
With ubiquitous live sensors and sensor networks, increasingly large numbers of individual sensors are deployed in physical space. Sensor data streams are a fundamentally novel mechanism to create and deliver observations to infor- mation systems, enabling us to represent spatio-temporal continuous phenomena such as radiation accidents, pollen distributions, or toxic plumes almost as instan- taneously as they happen in the real world. While data stream engines (DSE) are available to process high-throughput updates, DSE support for phenomena that are continuous in both space and time is not available. This places the burden of handling any tasks related to the integration of potentially very large sets of con- current sensor streams into higher-level abstractions on the user. In this paper, we propose a formal extension to stream data model languages based on the concept of fields to support high-level abstractions of continuous ST phenomena that are known to the DSE, and therefore, can be supported through queries and process- ing optimization. The proposed field data types are formalized in a data model language independent way using second order signatures. We formalize both the set of supported field types are as well as the embedding into stream data model languages.
Full paper can be found here.