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USING BIG DATA AND OPEN
SOURCE FOR SMART CITY
PLANNING
GUY HADASH, PAULA TA-SHMA
BIG DATA-IOT
The IoT market will grow to
$1.7 trillion in 2020 (IDC)
By 2020 the number of networked devices will
be 30 billion (IDC), more than 4 times the entire
global population
2005 2012 2017
WE DON’T KNOW HOW TO USE IT
So much data, that we don’t know how to make sense of it
‱ We archive as much data as we can
‱ In order to get value from that data we need to process it
‱ Not as easy as it seems
Cultivate Resilient Smart Objects
for Sustainable City Applications
EU funded project for developing smart-city infrastructure
USE CASE – MADRID TRANSPORTATION
Many sensors, a lot of data
MONITORING TRAFFIC IN
MADRID TODAY
Uses only current data
in the old-fashioned way
WHAT WE DID
We analysed data from 300 sensors on a specific
highway.
We developed a POC for Madrid Council in order
to show how to learn from the historical data, in
order to optimize the way it handles online data.
We created dashboard which
handles some of the sensors.
It monitors the traffic speed and
intensity (vehicles per hour).
Using results of an ML algorithm
applied to the historical data, the
system decides if anything is wrong
and reports it using the dashboard.
OUR SOLUTION
HOW WE DID IT
SWIFT
MLLib & SQL
Secor
Swift
Object Store
Freeboard Dashboard
Secor MQTT Broker
Data
Sources
Apache
Spark
Node-RED
Secor
CEP
CEP
Message
Bus
Data
Storage
Data
Transformation
Data
Analytics
Data
Visualization
Freeboard Dashboard
Object StorageMessage Hub
10
THE FLOW & BLUEMIX
POSSIBLE FUTURE PLANS
‱ Calculate “bottle necks” using historical data, and use that data when planning new
roads
‱ Predict the correlation of traffic with other data sources (such as weather data)
‱ Help Madrid Council make the processed historical data available through an
external API
‱ Re-route public transportation automatically when special events happens
‱ Etc


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Using big data and open source for smart city planning

  • 1. USING BIG DATA AND OPEN SOURCE FOR SMART CITY PLANNING GUY HADASH, PAULA TA-SHMA
  • 2. BIG DATA-IOT The IoT market will grow to $1.7 trillion in 2020 (IDC) By 2020 the number of networked devices will be 30 billion (IDC), more than 4 times the entire global population 2005 2012 2017
  • 3. WE DON’T KNOW HOW TO USE IT So much data, that we don’t know how to make sense of it ‱ We archive as much data as we can ‱ In order to get value from that data we need to process it ‱ Not as easy as it seems
  • 4. Cultivate Resilient Smart Objects for Sustainable City Applications EU funded project for developing smart-city infrastructure
  • 5. USE CASE – MADRID TRANSPORTATION Many sensors, a lot of data
  • 6. MONITORING TRAFFIC IN MADRID TODAY Uses only current data in the old-fashioned way
  • 7. WHAT WE DID We analysed data from 300 sensors on a specific highway. We developed a POC for Madrid Council in order to show how to learn from the historical data, in order to optimize the way it handles online data.
  • 8. We created dashboard which handles some of the sensors. It monitors the traffic speed and intensity (vehicles per hour). Using results of an ML algorithm applied to the historical data, the system decides if anything is wrong and reports it using the dashboard. OUR SOLUTION
  • 9. HOW WE DID IT SWIFT MLLib & SQL Secor
  • 10. Swift Object Store Freeboard Dashboard Secor MQTT Broker Data Sources Apache Spark Node-RED Secor CEP CEP Message Bus Data Storage Data Transformation Data Analytics Data Visualization Freeboard Dashboard Object StorageMessage Hub 10 THE FLOW & BLUEMIX
  • 11. POSSIBLE FUTURE PLANS ‱ Calculate “bottle necks” using historical data, and use that data when planning new roads ‱ Predict the correlation of traffic with other data sources (such as weather data) ‱ Help Madrid Council make the processed historical data available through an external API ‱ Re-route public transportation automatically when special events happens ‱ Etc


Hinweis der Redaktion

  1. Responding very slow, requires a lot of man hours.
  2. Each sensor sends data each 5 minutes
  3. k-means clustering where k=2 in different parts of the day
  4. Creators Node-RED - IBM Parquet - Twitter and Cloudera Secor – Pinterest Kafka – Linkedin Spark - University of California, Berkeley OpenStack - Rackspace Cloud & NASA
  5. We extended elastic search, secor, Apache Spark, Apache Kafka, freeboard