9 Nov 2015
Submission for Numenta Challenge 2015 http://devpost.com/software/air-traffic-anomaly-detector
Who has never suffer any flight’s delay? Could we use the causes of these issues to avoid bad flights?
ATAD try to answer some of these questions.
We believe that many flights are delayed by meteorological incidents, but there are others reasons of anomalies such as the plane or the airport or the route.
We also are interested in known if a plane changes its typical flight path in order to detect possible security breach.
This first stage was focused on analyzing flights between LA and NY, assessing the flight path and to detect abnormal movements in flight in relation to its geo-position and heading.
We got two different features.
1) The first allows us to view the history of flights and find anomalous points detected by NUPIC
2) But we also, we want to detect if any current flight is having an anomaly, so we can inform the airline about this situation. To do this, we have created a map having real time data, it’ll notify the user if it find any anomaly.
We know that there are thousands of flights above our heads everyday. One problem we noticed is that scalability for this kind of system is crutial. Because of this we built HTM-MOCLU.
By definition HTM-MOCLU is short for Hierarchical Temporal Memory Models Cluster. Htm-Moclu provides a platform similar to HtmEngine for htm.java applications, and has the ability to scale horizontally using multiple servers.
Then we built our app on top of Moclu, that way we can add hardware on demand to deal with tons of data for realtime flights.
On the UI side we are using AngularJS that integrates with Lift 3 and Comet actors, a google maps angular implementation called angular-google-maps and D3 charts.
The backend is built using 2 different web servers.
One of them is used to connect to the models cluster, get and push new flights data coming from external sources also it saves results into MongoDB.
The second web server is used to serve this data to the client. Both web servers can be scaled horizontally.
A lot of value can be delivered by ATAD in a short-term future, such as:
* Give the possibility to a person who is about to buy a ticket to select a flight not only based on costs and benefits but also for the possibility of having a fault, making the travel experience better.
* Find which airports have more anomalies than others, in order to provide this information for later use by airlines or passagers.
* Report on the number of anomalies by flight to assess their personal onboard.
* Add weather information to understand it impact and to help plan situations where airports are closed to prevent early collapse secondary airport facilities like hotels.
* Analyze routes and airports can enable airlines to enhance their routes management in order to reduce costs generated by anomalies.
We believe this information is useful for all the stakeholders, but primarily for the passenger, who will receive a better travel experience.
https://github.com/antidata/ATAD
https://github.com/antidata/htm-moclu
https://github.com/numenta/htm.java
Who has never suffer any flight’s delay? Could we use the causes of these issues to avoid bad flights?
ATAD try to answer some of these questions.
We believe that many flights are delayed by meteorological incidents, but there are others reasons of anomalies such as the plane or the airport or the route.
We also are interested in known if a plane changes its typical flight path in order to detect possible security breach.
This first stage was focused on analyzing flights between LA and NY, assessing the flight path and to detect abnormal movements in flight in relation to its geo-position and heading.
We got two different features.
1) The first allows us to view the history of flights and find anomalous points detected by NUPIC
2) But we also, we want to detect if any current flight is having an anomaly, so we can inform the airline about this situation. To do this, we have created a map having real time data, it’ll notify the user if it find any anomaly.
We know that there are thousands of flights above our heads everyday. One problem we noticed is that scalability for this kind of system is crutial. Because of this we built HTM-MOCLU.
By definition HTM-MOCLU is short for Hierarchical Temporal Memory Models Cluster. Htm-Moclu provides a platform similar to HtmEngine for htm.java applications, and has the ability to scale horizontally using multiple servers.
Then we built our app on top of Moclu, that way we can add hardware on demand to deal with tons of data for realtime flights.
On the UI side we are using AngularJS that integrates with Lift 3 and Comet actors, a google maps angular implementation called angular-google-maps and D3 charts.
The backend is built using 2 different web servers.
One of them is used to connect to the models cluster, get and push new flights data coming from external sources also it saves results into MongoDB.
The second web server is used to serve this data to the client. Both web servers can be scaled horizontally.
A lot of value can be delivered by ATAD in a short-term future, such as:
* Give the possibility to a person who is about to buy a ticket to select a flight not only based on costs and benefits but also for the possibility of having a fault, making the travel experience better.
* Find which airports have more anomalies than others, in order to provide this information for later use by airlines or passagers.
* Report on the number of anomalies by flight to assess their personal onboard.
* Add weather information to understand it impact and to help plan situations where airports are closed to prevent early collapse secondary airport facilities like hotels.
* Analyze routes and airports can enable airlines to enhance their routes management in order to reduce costs generated by anomalies.
We believe this information is useful for all the stakeholders, but primarily for the passenger, who will receive a better travel experience.
https://github.com/antidata/ATAD
https://github.com/antidata/htm-moclu
https://github.com/numenta/htm.java
- 1 participant
- 6 minutes
9 Nov 2015
This is the Cortical.io submission for the Numenta HTM Challenge 2015. It's an intelligent Twitter monitoring service that detects semantic anomalies - meaning that it uncovers unusual changes in topics in an individual's Twitter feeds. It has been configured to monitor the Twitter accounts of several top US Presidential candidates so users can investigate what they post are about and use the power of the HTM and Cortical's Retina API to learn more about their elected officials.
More information at:
http://devpost.com/software/news-reader-0wkjli
http://www.cortical.io/
http://numenta.com/
More information at:
http://devpost.com/software/news-reader-0wkjli
http://www.cortical.io/
http://numenta.com/
- 1 participant
- 5 minutes
8 Nov 2015
My submission for the Numenta Challenge. My application uses HTM models to detect anomalous traffic flows in Adelaide, Australia. The idea is that anomalous traffic indicates an incident.
Github here: https://github.com/JonnoFTW/htm-models-adelaide
Github here: https://github.com/JonnoFTW/htm-models-adelaide
- 1 participant
- 7 minutes
