Hadoop Summer Camp

Continuing with our ‘let’s learn something new’ summer camp from last year, this year we focus on learning Hadoop. We installed Virtual Box, and the Cloudera quickstart engine, and used the first tutorial as our “hello World” program option, with various forms of success. 8GB of RAM and 7GB of RAM associated with the VB is definitely a recommended start to not twiddle fingers for Cloudera to respond.


J.C. our instructor


This year we had to break out into the class room


Our camp crew: professors, grad students and 1 CS undergrad



Our paper “From Data Streams to Fields: Extending Stream Data Models with Field Data Types” accepted at GIScience 2016

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:Liang_Nittel_Hahmann_GIScience2016

Graduation 2016

Against all odds and weather forecasts, it turned out to be a sunny and warm day for commencement.  Spatial Information Science and Engineering had 4 PhD students to hood, 2 Master students, and Computer Science around 20 graduating undergraduate students, including Welles and Jennifer who did their capstone project with the Geosensor Networks lab.

Congratulations to everyone!


Real-time monitoring of rider’s pressure using a wireless sensor network

In the 2015/2016 academic year, Jennifer Hasting, a Computer Science Major at the University of Maine, developed a wireless saddle sensor network. An avid rider herself since childhood, she has been advancing to Dressage, a highly skilled form of riding. Applying correct pressure to the horse through the saddle is a key tool to ‘direct’ the horse for the different styles of riding.

For training purposes, it is helpful for the rider to get feedback on how they distribute pressure. The Amerika is a mechanical horse simulator that makes it easier for beginning and advanced students to learn proper, independent seat and sensitive hands techniques. Jennifer’s motivation was to replicate this feedback mechanism with an inexpensive sensor network that would be installed on a saddle, and used on a real horse.

Over the winter she built the pressure sensors herself, all 33 of them, programmed the Arduino node to collect the information and the Xbee radio to wirelessly send the information to a basestation connected to a laptop, that runs a real-time visualization of the pressure values. Beginning of April 2016 we got together at her parents house to try it out on her horse Kaya. The first proof of concept test. Besides the radio having difficulties to send the information to the base station when the distance became larger than ca. 5 meters, the overall system worked very well.

On May 6 2016, Jennifer successfully presented her project in the Capstone class.

For more information, please contact Jennifer Hastings at (jennifer . hastings @ maine . edu).

Jennifer checking the live sensor network.

Jennifer checking the live sensor network.



33 sensors and one extended bread board

33 sensors and one extended bread board


Ready for a test run

Ready for a test run

All installed and ready to go

All installed and ready to go



Testing with a wired link provides real-time feedback

Testing with a wired link provides real-time feedback

User interface

User interface


SIE558 Real-time sensor data streams final projects

Here are some impressions from the final project presentations of the SIE 558 Real-time Sensor Data Stream course. — The projects included

  • An Arduinos and raspberry pi-based sensor network to send  automatic alerts when the dog would open the fridge and check the trash can
  • data analysis in the Damariscotto river and
  • data analysis of fertilizer run-off in the Mississippi delta
  • an Arduino-based  heart rate monitoring system, and
  • a Arduino-based house plant monitoring system








Congratulations, Xueying!

Today, Xueying Gu, successfully presented his MS project. Congratulations!Screen Shot 2015-12-15 at 12.11.26 PM

Screen Shot 2015-12-15 at 12.12.05 PM

Xueying Gu’s MS project presentation

Master Project Presentation — Dec 15 2015, 11am SIE Library


Xueying Gu

Project Advisor: Dr. Silvia Nittel
Co-Advisor: Dr. Torsten Hahmann

Understanding the behavior of people in a building can be useful to save energy in buildings and improve efficiency of employees. To study the behavior of people in office buildings, sensors can be used to collect raw data about people’s presence in rooms and their movement through buildings. However, to understand higher-level behavioral patterns, we first developed an application ontology, written in Common Logic, that describes this information of interest at a higher level of abstraction. For example, which characteristics constitute a meeting? How are they related to movement data? Following, we translate the defined ontological concepts into MySQL scripts and Java algorithms to ‘mine’ the low-level sensor data stored in a relational database system. With the programs, we can retrieve some important information about offices such as: how frequently are offices and shares spaces utilized? How many meetings take place on average during a week? A number of use cases demonstrate the capabilities of the system.

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