Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   study a new problem in temporal pattern mining: discovering frequent arrangements of temporal intervals.  assume that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine arrangements of event intervals that appear frequently in the database. There are many applications where these type of patterns can be useful, including data network, scientific, and financial applications. Efficient methods to find frequent arrangements of temporal intervals using both breadth first and depth first search techniques are described. The performance of the proposed algorithms is evaluated and compared with other approaches on real datasets (American sign language streams and network data) and large synthetic datasets.
['Panagiotis Papapetrou', 'George Kollios', 'Stan Sclaroff', 'Dimitrios Gunopulos']
I am writing a research paper on a idea Discovering frequent arrangements of temporal intervals
[{"from": "human", "value": "I am writing a research paper on a idea Discovering frequent arrangements of temporal intervals"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   study a new problem in temporal pattern mining: discovering frequent arrangements of temporal intervals.  assume that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine arrangements of event intervals that appear frequently in the database. There are many applications where these type of patterns can be useful, including data network, scientific, and financial applications. Efficient methods to find frequent arrangements of temporal intervals using both breadth first and depth first search techniques are described. The performance of the proposed algorithms is evaluated and compared with other approaches on real datasets (American sign language streams and network data) and large synthetic datasets."}]