How can we analyze and predict high velocity data streams accurately? How can we discover interesting patterns in real time? How can we detect anomalies as quickly as possible? These questions are highly related to data stream mining, and are receiving growing interests especially in recent days. Streaming data is very common in the real world: IoT data, sensor data from cars, environment sensing data, co-purchasing products in e-commerce sites, financial transaction, messages in social networks, click streams in the web, network traffic, etc. One important characteristic of this kind of data is that it is generated continuously at a very high speed. As a result, a stream mining stack requires to satisfy the following desiderata.
In this project, we design and develop Swift Stream Miner, a fast and efficient stream mining software stack to analyze a high velocity data stream. Especially, our focus is twofold.
Our Swift Stream Miner will run on various platforms, including standalone environment and distributed platforms.
Swift Stream Miner will be used for various applications including: