Given a set of facilities and a set of users, a reverse nearest neighbors (RNN) query retrieves every user u for which thequery facility q is its closest facility. Since q is the closest facility to u, the user u is said to be influenced by q. In this paper, we propose arelaxed definition of influence where a user u is said to be influenced by not only its closest facility but also every other facility that isalmost as close to u as its closest facility is. Based on this definition of influence, we propose reverse approximate nearest neighbors(RANN) queries. Formally, given a valuex 1, an RANN query q returns every user u for which distu; qbx NNDistub whereNNDistub denotes the distance between a user u and its nearest facility, i.e., q is an approximate nearest neighbor of u. In this paper,we study both snapshot and continuous versions of RANN queries. In a snapshot RANN query, the underlying data sets do not changeand the results of a query are to be computed only once. In the continuous version, the users continuously change their locations andthe results of RANN queries are to be continuously monitored. Based on effective pruning techniques and several non-trivialobservations, we propose efficient RANN query processing algorithms for both the snapshot and continuous RANN queries. Weconduct extensive experiments on both real and synthetic data sets and demonstrate that our algorithm for both snapshot andcontinuous queries are significantly better than the competitors.
|Number of pages||14|
|Journal||IEEE Transactions on Knowledge and Data Engineering|
|Publication status||Published - 1 Feb 2018|
- continuous monitoring
- influence detection
- Reverse nearest neighbors
- voronoi diagram