Optimal search algorithm in a big database using interpolation–extrapolation method

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 interpolation search technique when the data is regularly distributed. In this work, the interpolation search 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–extrapolation search (IES) method is proposed where the interpolation method is integrated with an extrapolation method that balances the lower and upper bounds of the search interval. The proposed method provides faster convergence property than the binary search and the interpolation method. Hence, the proposed IES method provides a faster search for items in a big database.