1. Environmental Sensing Monitoring and Analyzing Water Temperatures
Mark Caruso, Joseph A. Hassan, Leanne Denise Keeley,, Sheetal Vasant Nikam, AJ Zada
Team One

2. Introduction
During the spring of 2017 a team of Computer Science students at Pace University developed a temperature monitoring device using a microcontroller with attached sensors to collect air and water temperature data from Choate Pond.
The collected data was sent over a Wi-Fi connection and stored in a database.
In the fall of 2017 another team of Pace CS students began integrating with the previously developed DBMS. The goal was to build a web-based tool that would offer the user options on how they’d like to visualize, and analyze the data. It offered line, histogram, and bar chart visualizations.
Standing on the shoulders of the above work, this study focused on data analyses and presenting the data in manner appropriate to its domain.
Introduction

3. Goals
Challenges
Expand the existing data-set by simulating a situation where data has been collected over several years.
Use a set of metrics on the data that is appropriate for its domain.
Expose the data, through a web application, in its raw and analyzed form.
Manage “Big Data”
Provide Meaning through analysis and Visualization
Develop Software that can allow for features not yet imagined
Goals and Challenges

4. Data Analyses Data Visualization Big What?
A component of a larger System
The intersection between...
IoT
Data Analyses
Data Visualization
Big What?
Environmental Science
Web Application
The Big Ideas

5. Big Data Data Analytics Environmental Science Water Temp Velocity
1. Monitor long-term changes
in climate
Velocity
Volume
Water Temp
2. Predict biological and chemical activity in bodies of water
Big Data
3. judge the
overall health
of an ecosystem
Data Analytics
Variety
Time Series
CSV
Veracity
Raw Data
Temp / Time
JSON
DTR= Tmax – Tmin / month
Storage is More cost effective
than paper
DD = (Tmax –Tmin)/2
Research – Summary Big Data and Data Analytics

6. App Environment & Ecosystem
CSS
HTML5
Client / Server
Dev Tools
In The Cloud JS
MVC
PaaS
9 – Request Data & Connect to DB
5 - Return a Resource
Bluemix
ExpressJS
10 - Query DB & Respond
NoSQL
System Architecture is done
Node + Express
JQuery

7. DV - “A Picture is worth a thousand words”
“Data visualization is the presentation of data in a pictorial or graphical format it enables decision makers to be able to see analytics represented visually so they can grasp difficult concepts or identify new patterns.”
Research – Data Visualization

8. D.V. Tools D3 Scalable Vector Graphics Google Charts Charts.js Raster
Library
Chart Types
Interactivity
Events
Customization Expressivity
Rendering Method
D3.js
Data-Driven-Documents
No charts out-of-the box
Defined by developer on selected elements using JS event handling functions
All aspects are customizable by developer using library methods, CSS, and pure JavaScript
JavaScript, CSS & HTM5/SVG
Google Charts API
12 core chart types plus 16 more
Out-of-box tooltips with additional event handling through JS event handling functions
Ready to use out-of-the-box. Customizations available through an options object. Some specific to each chart, others generic
JavaScript, HTML5/SVG, VML for older IE browsers such as IE8
Charts.js
9 core chart types
out-of-the-box tooltips and standard JS events passed into an options object as a string[]
Customization available on major chart components such as colors, fonts, layout, legend, and ToolTips
JavaScript HTML5/ Canvas
D.V. Tools
HTML5 / SVG D3
Scalable Vector Graphics
Data-Driven-Documents
Google Charts
HTML5 <canvas>
Charts.js
Raster

9. Water Monitoring Analyzer
The Web Application
Water Monitoring Analyzer

10. Next Steps
Conclusion
Possible to build a single-paged web-based tool that provides an easy to use solution for visualizing and analyzing large data sets.
Add additional metrics to the exisiting set.
Test the application while receiving real-time data.
Reinforces the notion that when it comes to gaining insight into big data, data visualization is the way to go
Add a feature to view similar data from various locations.
Support the case for offering the device as well as a visualization tool to educators interested in exploring “The Internet of Things” with their students.
Compare and combine the data received from the device with other relevant data-sets such as Geographical Information System.