MX-Quadtree
- PDF / 43,049 Bytes
- 1 Pages / 547.087 x 737.008 pts Page_size
- 55 Downloads / 245 Views
quad-trees) or bounding rectangles (as in R-trees). MRAtrees store, in addition to this information, aggregate properties of the indexed entities, e. g., the SUM of their values, the MIN value, etc. Several such aggregates can be stored or alternatively, only those that are often queried. Main Text MRA-trees are useful in answering aggregate queries approximately and in a progressive manner. Traditional multi-dimensional indexes help aggregate query answering by quickly gathering all relevant tuples. However, they have the limitation that each of those tuples must be handled individually. Moreover, approximate answers and answer quality guarantees cannot be easily computed. MRA-trees avoid visiting entire subsets of the data since they are summarized adequately at high-level tree index nodes and they can provide deterministic answer quality guarantees since the aggregate characteristics of the data at various levels of resolution are available throughout the tree. By exploring the tree progressively, the answer quality can improve all the way to the exact answer. They can therefore be used when the user specifies either a time deadline or answer quality requirement, trying to optimize the quality and running time, respectively, under these constraints.
765
Cross References Aggregate Queries, Progressive Approximate Progressive Approximate Aggregation
Multiscale Databases Modeling and Multiple Perceptions
Multi-Type Nearest Neighbor Query Trip Planning Queries in Road Network Databases
Mutation Geographic Dynamics, Visualization And Modeling
MX-Quadtree Quadtree and Octree
M
Data Loading...