R-Tree is typically used for which type of data?

R-Tree is typically used for which type of data?

• A. The standard spatial index in production databases that groups nearby objects into flexible overlapping bounding rectangles organized hierarchically, supporting points and polygons.
• B. A simple binary search tree for numeric keys.
• C. A text indexing structure for documents.
• D. A write-optimized log-structured merge tree.

Answer: (A)

R-Tree is a data structure designed for indexing multi-dimensional spatial data. It represents each object by its bounding rectangle and builds a hierarchical tree where each internal node stores the bounding box that encloses all its children. This arrangement lets the system prune large parts of the data during queries by quickly checking which bounding boxes intersect a query region, so range searches and nearest-neighbor lookups run efficiently. The use of flexible, overlapping bounding rectangles makes the index robust to irregular spatial distributions and works well for both simple points and complex polygons. That aligns with a spatial index in production databases that groups nearby objects into these bounding rectangles and organizes them hierarchically, enabling fast spatial queries. The other options describe data structures for different purposes: a simple binary search tree for numeric keys isn’t designed for multi-dimensional geometry; a text indexing structure targets documents; a write-optimized log-structured merge tree is geared toward append-heavy workloads and isn’t specialized for spatial indexing.

R-Tree is a data structure designed for indexing multi-dimensional spatial data. It represents each object by its bounding rectangle and builds a hierarchical tree where each internal node stores the bounding box that encloses all its children. This arrangement lets the system prune large parts of the data during queries by quickly checking which bounding boxes intersect a query region, so range searches and nearest-neighbor lookups run efficiently. The use of flexible, overlapping bounding rectangles makes the index robust to irregular spatial distributions and works well for both simple points and complex polygons.

That aligns with a spatial index in production databases that groups nearby objects into these bounding rectangles and organizes them hierarchically, enabling fast spatial queries. The other options describe data structures for different purposes: a simple binary search tree for numeric keys isn’t designed for multi-dimensional geometry; a text indexing structure targets documents; a write-optimized log-structured merge tree is geared toward append-heavy workloads and isn’t specialized for spatial indexing.