Ranked Reverse Nearest Neighbor Search ABSTRACT: 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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Online Index Recommendations for High-Dimensional Databases Using Query Workloads An Efficient Clustering Scheme to Exploit Hierarchical Data in Network Traffic analysis