Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: AbstractAs data stream grows exponentially, the aggregate query technique is widely used since it can rapidly obtain the summary information. Typical approximate aggregate query methods, like sliding-window, random sampling, wavelet, sketch index structure, histogram, etc., all evaluate the quality of the algorithms by the average size of query errors and ignore the maximum relative error, which determines the availability of the methods. Regarding this issue, this paper proposes the Reasonable Histogram (RH) method to improve the classic aggregate query method AMH. Based on the analysis of AMH errors’ mathematical characteristics,  build an aggregate query mathematical model based on the Kalman filter, using the optimal estimate of the buckets’ average frequency to calculate the aggregate values of the anomalous points, so as to restrain the maximum relative error.
['Zhixian Tang', 'Jun Feng', 'Zhonghua Zhu', 'Yaqing Shi', 'Jiamin Lu']
I am writing a research paper on a idea RH: An improved AMH aggregate query method
[{"from": "human", "value": "I am writing a research paper on a idea RH: An improved AMH aggregate query method"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: AbstractAs data stream grows exponentially, the aggregate query technique is widely used since it can rapidly obtain the summary information. Typical approximate aggregate query methods, like sliding-window, random sampling, wavelet, sketch index structure, histogram, etc., all evaluate the quality of the algorithms by the average size of query errors and ignore the maximum relative error, which determines the availability of the methods. Regarding this issue, this paper proposes the Reasonable Histogram (RH) method to improve the classic aggregate query method AMH. Based on the analysis of AMH errors\u2019 mathematical characteristics,  build an aggregate query mathematical model based on the Kalman filter, using the optimal estimate of the buckets\u2019 average frequency to calculate the aggregate values of the anomalous points, so as to restrain the maximum relative error."}]