Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Monitoring and analyzing network traffic usage patterns is vital for managing IP Networks. An important problem is to provide network managers with information about changes in traffic, informing them about "what's new". Specifically,  focus on the challenge of finding significantly large differences in traffic: over time, beten interfaces and beten routers.  introduce the idea of a deltoid: an item that has a large difference, whether the difference is absolute, relative or variational.  present novel algorithms for finding the most significant deltoids in high-speed traffic data, and prove that they use small space, very small time per update, and are guaranteed to find significant deltoids with pre-specified accuracy. In experimental evaluation with real network traffic, our algorithms perform ll and recover almost all deltoids. This is the first work to provide solutions capable of working over the data with one pass, at network traffic speeds.
['Graham Cormode', 'S. Muthukrishnan']
I am writing a research paper on a idea What's new: finding significant differences in network data streams
[{"from": "human", "value": "I am writing a research paper on a idea What's new: finding significant differences in network data streams"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Monitoring and analyzing network traffic usage patterns is vital for managing IP Networks. An important problem is to provide network managers with information about changes in traffic, informing them about \"what's new\". Specifically,  focus on the challenge of finding significantly large differences in traffic: over time, beten interfaces and beten routers.  introduce the idea of a deltoid: an item that has a large difference, whether the difference is absolute, relative or variational.  present novel algorithms for finding the most significant deltoids in high-speed traffic data, and prove that they use small space, very small time per update, and are guaranteed to find significant deltoids with pre-specified accuracy. In experimental evaluation with real network traffic, our algorithms perform ll and recover almost all deltoids. This is the first work to provide solutions capable of working over the data with one pass, at network traffic speeds."}]