Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Every designer of a new data structure wants to know how ll it performs in comparison with others. But finding, coding and testing applications as benchmarks can be tedious and time-consuming. Besides, how a benchmark uses a data structure may considerably affect its apparent efficiency, so the choice of applications may bias the results.  address these problems by developing a tool for inductive benchmarking. This tool, Auburn, can generate benchmarks across a wide distribution of uses.  precisely define ‘the use of a data structure’, upon which  build the core algorithms of Auburn: how to generate a benchmark from a description of use, and how to extract a description of use from an application.  then apply inductive classification techniques to obtain decision trees for the choice beten competing data structures.  test Auburn by benchmarking several implementations of three common data structures: queues, random-access lists and heaps. These and other results show Auburn to be a useful and accurate tool, but they also reveal some limitations of the approach.
['Graeme E. Moss', 'Colin Runciman']
I am writing a research paper on a idea Inductive benchmarking for purely functional data structures
[{"from": "human", "value": "I am writing a research paper on a idea Inductive benchmarking for purely functional data structures"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Every designer of a new data structure wants to know how ll it performs in comparison with others. But finding, coding and testing applications as benchmarks can be tedious and time-consuming. Besides, how a benchmark uses a data structure may considerably affect its apparent efficiency, so the choice of applications may bias the results.  address these problems by developing a tool for inductive benchmarking. This tool, Auburn, can generate benchmarks across a wide distribution of uses.  precisely define \u2018the use of a data structure\u2019, upon which  build the core algorithms of Auburn: how to generate a benchmark from a description of use, and how to extract a description of use from an application.  then apply inductive classification techniques to obtain decision trees for the choice beten competing data structures.  test Auburn by benchmarking several implementations of three common data structures: queues, random-access lists and heaps. These and other results show Auburn to be a useful and accurate tool, but they also reveal some limitations of the approach."}]