Towards The Continuous Spatio-Temporal Field Model for Sensor Data Streams

Today, with the availability of inexpensive, wireless enabled sensor nodes, we encounter a massive amount of geo-referenced sensor streams, which are collected continuously, spatially dense, and in real-time. Continuous geographic phenomena such as pollen distribution, extreme weather events, a toxic chemical leak or radioactive fallout now can be observed live and needs to be analyzed in real-time. However, the high volume of continuous sensor data streams pushes the capabilities of traditional sensor data management beyond their limits. Over the last decade, data stream engines (DSE) have been introduced as data management technology, which provide real-time query support for applications with very high throughput rates. However, users are better supported if they would be able to interact with higher-level abstractions of the real-world phenomena, rather than analyzing observations based on individual measurement streams. Dealing with individual streams requires that users need to write code that not only copes with the real-time nature of streams but also that fact that the streams need to be integrated and analyzed, continuously, which is a non-trivial task. This dissertation introduces the Stream Field Data Model, a DSE data model extension that is based on the concept of a field to represent continuous phenomena over space and time and is formally integrated with the relational and relational-based stream models. Using the high-level abstraction of fields provides an easy-to-use, flexible, mathematically defined and concise data model support for both sensor data streams as well as continuous phenomena. Furthermore, a Stream Query Language for the Field Stream Data Model is proposed with a novel set of stream query operators specifically for spatio-temporal fields. The approach is to lift relational operator to fields, and the semantics of this set of operators are discussed and formalized. The feasibility of extending DSE for visualizing fields in near real-time based on 100,000 of streams has been investigated. This dissertation proposes and evaluates different strategies to optimize a pipelined stream operator framework to achieve near real-time spatial interpolation throughput, considering the memory footprint, runtime efficiency and interpolation quality