1. Real Time Event Processing Using Distributed Machine Learning in Urban Environments Nikos Stefanos Kostagiolas Computer Science Student at National Kapodistrian University of Athens George Papadimitriou Electrical and Computer Engineering Student at National Technical University of Athens

2. The problem In the modern world, people seek to automatically tune many aspects of their lives, making the use of Machine Learning (ML) algorithms become an essential part of our everyday needs. One such aspect, which can be applied to ensure our everyday safety with minimal casualties combining unconstrained communication with precision and robustness, is Anomaly Detection in Real Time Events. So, what is the idea behind trusting algorithms to schedule our safety in everyday events, instead of having human labour to take the case ?

3. The idea We’re going to start with pointing out three things that really matter by choosing Machine Learning algorithms to monitor our safety : Fast response in emergencies (natural disasters, fires, bomb alerts, road accidents, assaults, etc.) Robustness and precision of the system Efficiency and scalability from simple pilot implementation towards smart cities So what’s the exact idea ? Having real data from the cities, we would like to perform real time event processing and recognise what’s happening in the city. Our ultimate goal is to provide an open system, that provides fast and accurate insights to the local authorities and alerts instantly the citizens about emergencies, that have happened a few moments ago or will happen in the near future. The key point is that we want to achieve this functionality in Real Time !

4. Harnessing New Technologies In order to implement our idea, we are putting to task new technologies in the very popular fields of IoT, Distributed Computing, Big Data and Machine Learning. More specifically, to support our idea and make our vision possible we: Create low power embedded devices, that capture and transmit image, sound and environmental parameters wirelessly (via WiFi or 3G) Take advantage of the existing infrastructure (security/traffic cams) Use Apache Storm for real time analysis Use Apache Spark to run our distributed Machine Learning algorithms Use Apache Kafka to support the real time alerting system Use Machine Learning algorithms (Support Vector Machines, Neural Networks, Graphical Models)

5. The Importance of Machine Learning But what is machine learning and why it really matters ? Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. Learning from previous computations produces reliability, continuously better results and more secure decisions. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful and affordable data storage mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results - even on a very large scale. The result ? High-value predictions that can guide better decisions and smart actions in real time without human intervention (self-driving Google car, online recommendation offers, fraud detection, customer feedback analysis).

6. The proposed system

7. Example Use Case 1: Prevent Terrorist Attacks Imagine the following situation: A terrorist attack takes place in an urban area, where a face recognition system captures the physical appearance of the culprits. The system alerts the police about the incident and automatically tries to follow the terrorist team, track any possible suspects or search for them analyzing the behaviour of the nearby citizens. The location and time of the incident are also stored to facilitate future evaluation of the correlated data gathered by all possible sources nearby the event.

8. Example Use Case 2 Imagine now another case: Smoke starts coming out of an apartment that’s on fire, in a large inhabited area with many wooden rowhouses nearby. This can result in a rapidly spreading fire with many casualties. Fortunately, the detectors in our system have already acquired visual and sensor data about the incident and decide that, due to the presence of smoke and temperature increase, a fire is about to break out. The system sends an immediate report to the closest fire department where the situation is evaluated and a quick response gets underway, guaranteeing efficiency and minimal casualties.

9. Example Use Case 3 Now a third scenario comes to mind: A huge car crash takes place in an urban highway, where immediate action must take place to facilitate both the victims and the unconstrained traffic continuation. The system now analyzes the visual and acoustic data received from the place of the car crash and decides to notify the closest police station about the incident and call for the appropriate medical services. The police arrives shortly to inspect and evaluate the situation of the car crash. Healthcare is administered to the victims by the medical service and the car remains are cleared for the highway to be functional again.

10. Thank You