Fast data search is an important element of big data in the modern era of internet of things, cloud computing, and social networks. search using traditional binary-search algorithm can be accelerated by employing an i...
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Fast data search is an important element of big data in the modern era of internet of things, cloud computing, and social networks. search using traditional binary-search algorithm can be accelerated by employing an interpolationsearch technique when the data is regularly distributed. In this work, the interpolationsearch is investigated in which the search results provided unexpected sluggish progress during a search in a large database due to the irregular distribution of data. Irregular distribution of data does not allow the interpolation to make a good prediction about the location of the search item. To overcome this issue, an interpolation-extrapolationsearch (IES) method is proposed where the interpolationmethod is integrated with an extrapolationmethod that balances the lower and upper bounds of the search interval. The proposed method provides faster convergence property than the binary search and the interpolationmethod. Hence, the proposed IES method provides a faster search for items in a big database.
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