PhD defense by Qinghan Liang, May 5th 2015

Qinghan will defend his dissertations on May 5 2015, 1pm in 336 Boardman Hall, University of Maine.

Title: 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 (DSEs) 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 DSEs 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, run-time efficiency and interpolation quality.

Mobile Mapping with DroneDeploy

Today, DroneDeploy, a start-up poised to make farms and other businesses significantly more efficient, launches its mobile app and announced it will be compatible with one of the world’s most popular drones.


“With DroneDeploy a drone can be easily told to fly on autopilot over a farmer’s fields. Shortly after the drone lands a farmer will already be able to review the maps and data gathered by the drone. With NDVI data, a farmer can what portions of his fields are healthy, and what portions aren’t.

He can then download a geotagged image out of DroneDeploy and upload that into his usual farming software to apply a dose of fertilizer to only the area of his field that is in need.”  The software steers the drone, maps a path, stitches the collected data into a map, and visualizes the new image on a tablet.

See more in this article.


Real-time sensing, streaming and analysis, 1 step closer.

Wireless soil moisture sensor network: testing phase

During the winter, we have put together a 5-node soil moisture sensor network with arduino boards, raspberry pis, solar panels and soil moisture sensors to monitor region based soil moisture distribution. Currently, we are in beta testing indoors, and plan to deploy outdoors once the ground is thawed (likely mid/end April 2015).


Arduino soil moisture sensor network

Silvia Nittel on Editorial Board of new journal “Open Geospatial Data, Software and Standards”

Silvia Nittel is on the editorial board of the new open access journal “Open Geospatial Data, Software and Standards” by Springer.

Open Geospatial Data, Software and Standards provides an advanced forum for the science and technology of open data, crowdsourced information, and sensor web through the publication of reviews and regular research papers. The journal publishes articles that address issues related, but not limited to, the analysis and processing of open geo-data, standardization and interoperability of open geo-data and services, as well as applications based on open geo-data. The journal is also meant to be a space for theories, methods and applications related to crowdsourcing, volunteered geographic information, as well as Sensor Web and related topics.

Holiday outing and graduation

And another semester is coming to a close…. Some students graduated and are moving on, most of us are just ready for a break, and so we had a holiday social at Blaze on Monday with whoever of the faculty and graduate students could make it.


Not everyone likes drones

Our paper “Evaluating Predicates over Dynamic Fields” has been accepted to the 5th International SIGSPATIAL Workshop on Geostreaming

Technological advances have created an unprecedented availability of inexpensive sensors able to stream environmental data in real-time for which we still seek appropriate data management technology that can keep up with this onslaught of sampling in previously unavailable spatial and temporal density.

In previous work we have shown that DSEs can be extended to generate smooth representations of continuous spatio-temporal fields sampled by up to 250K sensors on-the-fly in near real-time, creating a new representation every second.

In this paper we have investigated a spatio-temporal stream operator framework that efficiently executes predicate operators over spatio-temporal fields. We introduced a definition of predicates over dynamic fields, analyzed requirements for stream query evaluation and presented several pipelined stream based query operators algorithms. The work is based on the assumption that it is more efficient to find ‘seeds’ of regions that are part of the predicate result and expand them into the complete predicate result regions instead of interpolating the entire continuous phenomenon first, and filtering all cells based on the predicate condition. We investigated different seed expansion algorithms (Breadth First and Scanline region growing, and tile expansion) as well as exploring the impact of using the knowledge of the previous window query result. Our analysis and performance results show that both region growing algorithms perform best for all data set sizes and characteristics; tile-based approaches are efficient for tiles sizes 4×4 and 8×8. History-aware tile expansion performs better if the phenomenon changes slowly (as expected). Future work will include investigating adaptive query evaluation using the different algorithms based on the changing phenomenon characteristics.



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