Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a length-independent feature representation of sets of strings based on Bloom filters called BACR for similarity search in databases. Further,  show how a Z-curve-based discretization of geospatial trajectories can be used in order to search for similar trajectories in large databases. Additionally to the already-known estimation of the size of the union and the intersection of sets from Bloom filters,  propose a way to calculate an upper bound for the intersection and a lor bound for the union of sets. Consequently,  show that the Jaccard distance and many other similarity measures allow for a lor bound. This makes exact similarity search on large databases of this type feasible. Finally,  show that the Jaccard distance is incompatible with the union of sets and replace the Jaccard distance appropriately in a way such that even collections of sets of strings can be represented with a single BACR feature vector at least for similarity search applications. The algorithms are thoroughly evaluated and motivated by real-world examples.
['Martin Werner']
I am writing a research paper on a idea BACR: set similarities with lower bounds and application to spatial trajectories
[{"from": "human", "value": "I am writing a research paper on a idea BACR: set similarities with lower bounds and application to spatial trajectories"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a length-independent feature representation of sets of strings based on Bloom filters called BACR for similarity search in databases. Further,  show how a Z-curve-based discretization of geospatial trajectories can be used in order to search for similar trajectories in large databases. Additionally to the already-known estimation of the size of the union and the intersection of sets from Bloom filters,  propose a way to calculate an upper bound for the intersection and a lor bound for the union of sets. Consequently,  show that the Jaccard distance and many other similarity measures allow for a lor bound. This makes exact similarity search on large databases of this type feasible. Finally,  show that the Jaccard distance is incompatible with the union of sets and replace the Jaccard distance appropriately in a way such that even collections of sets of strings can be represented with a single BACR feature vector at least for similarity search applications. The algorithms are thoroughly evaluated and motivated by real-world examples."}]