Range Query Grouping In Spatial Networks Using Dual Distance Measurements
Location-based services and the abundant usage of smart phones and GPS-enabled devices, the necessity of outsourcing spatial data has grown rapidly.It deals with the approximate string search in large spatial databases.Specifically, this investigate range queries augmented with a string similarity search predicate in both Euclidean space and road networks. Euclidean space is ordinary two- or three-dimensional space. These called as the spatial approximate string (SAS) query. In Euclidean space, this propose an approximate solution, the MHR-tree, which embeds min-wise signatures into an R-tree. The min-wise signature for an index node u keeps a concise representation of the union of q-grams from strings under the subtree of u. This analyzes the pruning functionality of such signatures based on the set resemblance between the query string and the q-grams from the subtrees of index nodes. It also discusses how to estimate the selectivity of a SAS query in Euclidean space.Presented a novel adaptive algorithm to find balanced partitions using both the spatial and string information stored in the tree. For queries on road networks, we propose a novel exact method, RSASSOL, which significantly outperforms the baseline algorithm which is serving in basis, as for measurement, calculation or location in practice.The RSASSOL combines the q-gram-based inverted lists and the reference nodes based pruning.
S.Udhayakumar M.E., (CSE) , D.Sureshkumar M.E., (CSE)