Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In data mining applications and spatial and multimedia databases, a useful tool is the  k NN join, which is to produce the  k  nearest neighbors (NN), from a dataset  S , of every point in a dataset  R . Since it involves both the join and the NN search, performing  k NN joins efficiently is a challenging task. Meanwhile, applications continue to witness a quick (exponential in some cases) increase in the amount of data to be processed. A popular model nowadays for large-scale data processing is the shared-nothing cluster on a number of commodity machines using  MapReduce  [6]. Hence, how to execute  k NN joins efficiently on large data that are stored in a MapReduce cluster is an intriguing problem that meets many practical needs. This work proposes novel (exact and approximate) algorithms in MapReduce to perform efficient parallel  k NN joins on large data.  demonstrate our ideas using Hadoop. Extensive experiments in large real and synthetic datasets, with tens or hundreds of millions of records in both  R  and  S  and up to 30 dimensions, have demonstrated the efficiency, effectiveness, and scalability of our methods.
['Chi Zhang', 'Feifei Li', 'Jeffrey Jestes']
I am writing a research paper on a idea Efficient parallel kNN joins for large data in MapReduce
[{"from": "human", "value": "I am writing a research paper on a idea Efficient parallel kNN joins for large data in MapReduce"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In data mining applications and spatial and multimedia databases, a useful tool is the  k NN join, which is to produce the  k  nearest neighbors (NN), from a dataset  S , of every point in a dataset  R . Since it involves both the join and the NN search, performing  k NN joins efficiently is a challenging task. Meanwhile, applications continue to witness a quick (exponential in some cases) increase in the amount of data to be processed. A popular model nowadays for large-scale data processing is the shared-nothing cluster on a number of commodity machines using  MapReduce  [6]. Hence, how to execute  k NN joins efficiently on large data that are stored in a MapReduce cluster is an intriguing problem that meets many practical needs. This work proposes novel (exact and approximate) algorithms in MapReduce to perform efficient parallel  k NN joins on large data.  demonstrate our ideas using Hadoop. Extensive experiments in large real and synthetic datasets, with tens or hundreds of millions of records in both  R  and  S  and up to 30 dimensions, have demonstrated the efficiency, effectiveness, and scalability of our methods."}]