Abstract Little work has been reported in the literature to support k -nearest neighbor ( k -NN) searches/queries in hybrid data spaces (HDS)

An HDS is composed of a combination of continuous and non-ordered discrete dimensions. This combination presents new challenges in data organization and search ordering. In this paper, we present an algorithm for k -NN searches using a multidimensional index structure in hybrid data spaces.

We examine the concept of search stages and use the properties of an HDS to derive a new search heuristic that greatly reduces the number of disk accesses in the initial stage of searching. Further, we present a performance model for our algorithm that estimates the cost of performing such searches. Our experimental results demonstrate the effectiveness of our… algorithm and the accuracy of our performance estimation model.

Highlights • Developed algorithm for searching multi-dimensional hybrid data spaces. • Introduced the method of improving search performance by examining search stages. • Suggested new search heuristic to improve initial stage of searching by 33%. • Derived theoretical model accurately predicts the performance of algorithm.

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