Given a group of knowledge points P and a query purpose q during a multidimensional space, reverse nearest neighbor (RNN) question finds data points in P whose nearest neighbors are q. Reverse k-nearest neighbor (RkNN) question (where k ges 1) generalizes RNN query to seek out data points whose kNNs embody q. For RkNN question semantics, q is said to own influence to all or any those answer data points. The degree of q's influence on a knowledge point p (isin P) is denoted by kappap where q is that the kappap-th NN of p. We have a tendency to introduce a new variant of RNN question, specifically, ranked reverse nearest neighbor (RRNN) query, that retrieves t data points most affected by q, i.e., the t knowledge points having the smallest kappa's with respect to q. To answer this RRNN query efficiently, we tend to propose 2 novel algorithms, kappa-counting and kappa-browsing that are applicable to both monochromatic and bichromatic eventualities and will be able to deliver results progressively. Through an in depth performance evaluation, we validate that the 2 proposed RRNN algorithms are superior to solutions derived from algorithms designed for RkNN query.

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