Slideshow:
Answering Partial Match queries using a kd-tree index
Answering Partial Match queries using a kd-tree index
- Example
Find all data records with age = 50
Answering Partial Match queries using a kd-tree index
- Example
Find all data records with age = 50
Answering Partial Match queries using a kd-tree index
- Example
Find all data records with age = 50
Answering Partial Match queries using a kd-tree index
- Example
Find all data records with age = 50
Answering Partial Match queries using a kd-tree index
- Example
Find all data records with age = 50
Analysis: how efficient is the kd-tree for Partial Match queries ?
Partial Match query: Find all "yellow" key value < C
Analysis: how efficient is the kd-tree for Partial Match queries ?
Partial Match query: Find all "yellow" key value < C
Analysis: how efficient is the kd-tree for Partial Match queries ?
Partial Match query: Find all "yellow" key value < C
And so on...
Analysis: how efficient is the kd-tree for Partial Match queries ?
Conclussion:
Answering Range queries using a kd-tree index
Answering Range queries using a kd-tree index -
Example
Find records with 35 < age < 50 and 50 < salary < 100
Answering Range queries using a kd-tree index -
Example
Find records with 35 < age < 50 and 50 < salary < 100
Answering Range queries using a kd-tree index -
Example
Find records with 35 < age < 50 and 50 < salary < 100
Answering Range queries using a kd-tree index -
Example
Find records with 35 < age < 50 and 50 < salary < 100
Answering Range queries using a kd-tree index -
Example
Find records with 35 < age < 50 and 50 < salary < 100
Answering "Nearest Neighbor" queries using a kd-tree index
(It's very difficult to prune off branches in a kd-tree using a nearest neighbor candidate)
Answering "Nearest Neighbor" queries using a kd-tree index
-
Partial Match queries
- The query specifies conditions on some dimensions but not on all dimensions
Search Algorithm:
- For a dimension
for which the search value is
given (= specified):
- Take the (one) branch of the subtree for the search value
- For a dimension
for which the search value is
not given
(= not specified):
- Take both branches of the subtree !!!
Example:
- Find all person with
age = 50
- Analysis: how efficient is
the kd-tree ?
- Assume the kd-tree
is perfectly balance
(same height everywhere).
There are 2 dimensions and there are 2n levels:
- At level 1, we can
eliminate
half of
all records:
- We need only to process 1/2 of all records
- At level 3, we can
eliminate
another half of
all remaining records:
- We need only to process 1/4 of all records
- At level 5, we can
eliminate
another half of
all remaining records:
- We need only to process 1/8 of all records
And so on !!!
- If we
have 2n levels
in the kd-tree:
- We need only to process (1/2)n of all records
Example: a 4 (= 22) level kd-tree:
- Assume the kd-tree
is perfectly balance
(same height everywhere).
-
Range queries
- Find objects that are located either partial or wholly within a certain range
Search Algorithm:
- For the search range is
completely contained
by the left subtree, then:
- Take only the left branch of the subtree for the search value
- For the search range is
completely contained
by the right subtree, then:
- Take only the right branch of the subtree for the search value
- Otherwise
(the search range
straddles
at the search value
):
- Search both subtrees
Example:
-
Nearest neighbor queries:
- Not easy to
to find
the
nearest neighbor
using a kd-tree index:
It requires up and down traversal/search in the kd-tree.....
- Not easy to
to find
the
nearest neighbor
using a kd-tree index:
-
Where-am-I queries:
- Not applicable:
- KD-tree can
only stores
points
- Cannot store objects
- KD-tree can
only stores
points
- Not applicable: