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Optimizing spatial database

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Optimizing spatial database

  1. 1. OPTIMIZING SPATIALDATABASESBY ANDA VELICANU, ŞTEFAN OLARUPresented By: Ishraq Fataftah
  2. 2. Agenda Introduction. Spatial Indexing Structure.  R-Tree Index.  Quadtree Index. Comparing Spatial Indexes. Oracle Examples. Conclusion.
  3. 3. Introduction City Map
  4. 4. Introduction Cont. Spatial Objects: Consists of lines, surfaces, volumes and higher dimension objects that are used in applications of computer-aided design, cartography, geographic information systems. Spatial Data: The values of the objects’ spatial attributes (length, configuration, perimeter, area, volume, etc.)
  5. 5. Introduction Cont. Spatial Databases: is a collection of spatial and non-spatial data that is interrelated, of data descriptions and links between data. It offers additional functions that allow processing spatial data types.  Geometry.  Geography.
  6. 6. Introduction Cont. Optimizing spatial databases means optimizing the queries, which requires less time spent by running the queries before receiving an answer.
  7. 7. Spatial Indexes Indexing spatial data: a mechanism to decrease the number of searches (optimize spatial queries). A spatial index is used to locate objects in the same area of data or from different locations. Spatial indexes include:  Grid index.  Z-order.  Quadtree, Octree.  UB-tree,  R-tree.  kd-tree,  M-tree.
  8. 8. Spatial Indexes: Grid Index
  9. 9. Spatial Indexes: Z-Order
  10. 10. Spatial Indexes:UB Tree
  11. 11. Spatial Indexes: Kd tree
  12. 12. Spatial Indexes: m tree
  13. 13. Spatial Indexes: R-Tree
  14. 14. Spatial Indexes: R-Tree
  15. 15. Spatial Indexes: R-Tree Objects (geometric shapes, lines or points) are grouped using a MBR (Minimum Bounding Rectangle). Objects are added to an MBR with an index, leading to the smallest distance possible. Queries and updates get the R-tree’s root and browses down to the leaves.
  16. 16. Spatial Indexes: R-Tree Criteria that may affect the response time of an R-tree configuration for the two-dimensional case,  The MBR area  The MBR perimeter  The distance between bounding rectangles  Using the storage space
  17. 17. Spatial Indexes: R-Tree Building an R-tree index depends on two characteristics:  The way the objects are inserted in the tree.  Incremental with dynamic data.  Batch with known data.  Dimensionality  Linear.  Dimensional.
  18. 18. Spatial Indexes: R-Tree Spatial object: Contour (outline) of the area around the building(s). Minimum bounding region (MBR) of the object. 18
  19. 19. Spatial Indexes: Quadtree This type of indexing starts is a tree whose inner nodes have up to four children. Used to partition a two-dimensional space by dividing it into four identically shaped regions.  Bein equal parts on each level.  Depend on incoming data.
  20. 20. Spatial Indexes: Quadtree Types of Quadtree indexes are classified by:  The type of data that is represented  The independence of the tree’s shape on the order in which data is processed.  Variability of the tree obtained from data processing.
  21. 21. Spatial Indexes: Quadtree Region Quadtree:  Decomposing the region into four equal quadrants.  Each node in the tree has either four children or none (leaf node).  A Region tree with four sizes and a depth of n can be used for representing an image of 2n × 2n pixels, each pixel’s value is 0 or 1.
  22. 22. Spatial Indexes: Quadtree Point Quadtree:  Based on binary trees used to represent two- dimensional point data type.  Complex nodes that contain more than two pointers (left, right) and information.  4 pointers: NW, NE, SW and SE,  The key represented in x, y coordinates,  Information.  The tree shape depends on the order in which data is processed.
  23. 23. Spatial Indexes: Quadtree Edge Quadtree:  Used mostly to store lines and not points.  Curves are approximated by subdividing the cells in a very fine resolution.  Result as very unbalanced trees.  Rarely used.
  24. 24. Spatial Indexes: Quadtree Common characteristics between all Quadtree types:  The space is split into cells;  Each cell or group of cells has a maximum capacity, and when it is reached the group of cells splits;  The tree’s dimension and shape depend (strictly or not) on how the new data is inserted.
  25. 25. Comparing Spatial Indexing Quadtrees  use interior spaces for queries and data geometries.  Pieces of space are labeled as interior or border, considering whether or not they are within the geometry.  Inner surfaces arising from the execution of a query are also identified R-tree uses only inner queries.
  26. 26. Comparing Spatial Indexing
  27. 27. Comparing Spatial Indexing Quadtree has its advantages in terms of more complex types of queries. Basic spatial operations are performed much faster using an R-tree indexing type.
  28. 28. Oracle Spatial Examples Oracle Spatial is a component of Oracle Database. Oracle Spatial supports the object-relational model for representing the geometry. SDO_GEOMETRY.
  29. 29. Oracle Spatial Examples
  30. 30. Conclusion Spatial Databases are widely used nowadays. Optimizing Spatial Databases is of a significant importance. Spatial databases can be optimized using spatial indexes like R-tree or Quadtree and other indexing structures. Oracle supports spatial indexing using R-Tree and Quadtree.

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