B-trees are tree data structures that allow efficient retrieval of records from disk storage. They are commonly used in databases and file systems. R-trees are a type of tree data structure used for indexing multi-dimensional spatial data like geographic coordinates. B-trees organize data by keys, allowing efficient insertion, deletion, and retrieval of records. R-trees cluster data objects based on their proximity to each other to support spatial queries.
R-Trees are an excellent data structure for managing geo-spatial data. Commonly used by mapping applications and any other applications that use the location to customize content. Minimum Bounding Rectangle (MBR) is a commonly used concept in R-trees, which are a modified form of B-trees.
R-Trees are an excellent data structure for managing geo-spatial data. Commonly used by mapping applications and any other applications that use the location to customize content. Minimum Bounding Rectangle (MBR) is a commonly used concept in R-trees, which are a modified form of B-trees.
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Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. Many different carrier file formats can be used, but digital images
are the most popular because of their frequency on the Internet. For hiding secret information in
images, there exists a large variety of stenographic techniques some are more complex than others and all of them have respective strong and weak points.
Students will be able to learn the concepts of advanced trees like Splay Tree, B Tree, Red Black Tree, Priority Queue or Heap. In Heap Data Structure the following methods are covered: Binary Heap, d-heap, Leftist Heap and Skew Heap.
A graph search (or traversal) technique visits every node exactly one in a systematic fashion. Two standard graph search techniques have been widely used: Depth-First Search (DFS) Breadth-First Search (BFS)
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Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. Many different carrier file formats can be used, but digital images
are the most popular because of their frequency on the Internet. For hiding secret information in
images, there exists a large variety of stenographic techniques some are more complex than others and all of them have respective strong and weak points.
Students will be able to learn the concepts of advanced trees like Splay Tree, B Tree, Red Black Tree, Priority Queue or Heap. In Heap Data Structure the following methods are covered: Binary Heap, d-heap, Leftist Heap and Skew Heap.
A graph search (or traversal) technique visits every node exactly one in a systematic fashion. Two standard graph search techniques have been widely used: Depth-First Search (DFS) Breadth-First Search (BFS)
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
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3. Why do we use B-trees
• It was difficult to access a large amount of
data from the secondary memory
• Many of the algorithms were introduced to
make our search very fast, to access the
required data from the secondary memory
• B-trees are more effective and faster
• B-trees are used in many of the database
management system
4. Definition of a B-tree
• A B-tree of order m is an m-way tree (i.e., a tree where each
node may have up to m children) in which:
1. the number of keys in each non-leaf node is one less than
the number of its children and these keys partition the
keys in the children in the fashion of a search tree
2. all non-leaf nodes except the root have at least m / 2
children
3. the root is either a leaf node, or it has from two to m
children
The number m is large than or equal to 2.
6. B-tree of order 5
all internal nodes have at least ceil(5 / 2) = ceil(2.5) = 3 children
maximum number of children that a node can have is 5
7. Insertion
• B-tree of order 5:
CNGAHEKQMFWLTZDPRXYS
Order 5 means that a node can have a
maximum of 5 children and 4 keys.
All nodes other than the root must have a
minimum of 2 keys.
20. R-tree
• R-trees are tree data structures used for
spatial access methods, for indexing multi-
dimensional information such as
geographical coordinates, rectangles or
polygons.
21. R-Tree Motivation
y axis
10 m
g h
8 l
k
e f
6
i j
d
4
b a
2 c
x axis
0 2 4 6 8 10
Range query: find the objects in a given range.
E.g. find all hotels in Boston.
No index: scan through all objects. Inefficient!
B+-tree: only cluster based on one dim. Inefficient!
21
22. R-Tree: Clustering by Proximity
y axis
10 m
g h
8 l
k
e f
6
i j
d
4
b
E3 a
Minimum Bounding Rectangle (MBR)
2 c
x axis
0 2 4 6 8 10
Root
E E
1 2
E E E E E E
1 3 4 5 6 7 E
2
a b c d e f g h i j k l m
22 E
E 4 E E E
3 5 6 7
23. y axis
R-Tree
10 m E7
g h
8 l
E6
E5 k
e f
6 E4 i j
d
4
b
E3 a
2 c
x axis
0 2 4 6 8 10
Root
E E
1 2
E E E E E E
1 3 4 5 6 7 E
2
a b c d e f g h i j k l m
23 E E
E 4 5 E E
3 6 7
24. y axis R-Tree
10 m
g h
8 l
k
e f E2
6
i j
d E1
4
b a
2 c
x axis
0 2 4 6 8 10
Root
E E
1 2
E E E E E E
1 3 4 5 6 7 E
2
a b c d e f g h i j k l m
E E E E E
3 24 4 5 6 7
25. y axis R-Tree
10 m
g h
8 l
k
e f E
6 2
i j
d E1
4
b a
2 c
x axis
0 2 4 6 8 10
Root
E E
1 2
E E E E E E
1 3 4 5 6 7 E
2
a b c d e f g h i j k l m
E E E E E
3 25 4 5 6 7
26. Range query (given range Q)
Start at root.
1. If current node is non-leaf, for each
entry <E, ptr>, if box E overlaps Q,
search subtree identified by ptr.
2. If current node is leaf, for every object in
the leaf page, report if contained in Q.
27. y axis Range Query
10 m
g h
8 l
k
e f E
6 2
i j
d E1
4
b a
2 c
x axis
0 2 4 6 8 10
Root
E E
1 2
E E E E E E
1 3 4 5 6 7 E
2
a b c d e f g h i j k l m
E E E E E
3 27 4 5 6 7
28. y axis Range Query
10 m
g h
8 l
k
e f E
6 2
i j
d E1
4
b a
2 c
x axis
0 2 4 6 8 10
Root
E E
1 2
E E E E E E
1 3 4 5 6 7 E
2
a b c d e f g h i j k l m
E E E E E
3 28 4 5 6 7
29. Aggregation Query
• Given a range, find some aggregate value
of objects in this range.
• COUNT, SUM, AVG, MIN, MAX
• E.g. find the total number of hotels in
Massachusetts.
• Straightforward approach: reduce to a range query.
• Better approach: along with each index entry, store aggregate of the
sub-tree.
29
30. Aggregation Query
y axis
10 m
g h
8 l
k
e f E
6 2
i j
d E1
4
b a
2 c
x axis
0 2 4 6 8 10
Root
E :8 E :5
1 2
E E :3 E :2 E :3 E :3 E :2
1 3 4 5 6 7 E
2
a b c d e f g h i j k l m
E E E E E
3 30 4 5 6 7
31. Aggregation Query
y axis
10 m
g h
8 l
k
e f E
6 2
i j
d E1
4
b a
Subtree pruned!2 c
x axis
0 2 4 6 8 10
Root
E :8 E :5
1 2
E E :3 E :2 E :3 E :3 E :2
1 3 4 5 6 7 E
2
a b c d e f g h i j k l m
E E E E E
3 31 4 5 6 7
32. Insert object o
• Start at root and go down to “best-fit” leaf L.
– Go to child whose box needs least enlargement
to cover B; resolve ties by going to smallest area
child.
• If best-fit leaf L has space, insert entry and
stop. Otherwise, split L into L1 and L2.
– Adjust entry for L in its parent so that the box
now covers (only) L1.
– Add an entry (in the parent node of L) for L2.
(This could cause the parent node to recursively
split.)
33. E.g. 1: no split, no enlargement
y axis
10 m insert o
g h
8 l
k
e f E
6 2
i j
d E1
4
b a
2 c
x axis
0 2 4 6 8 10
Root
E E
1 2
E E E E E E
1 3 4 5 6 7 E
2
a b c d e f g h i j k l m o
E E E E E
3 33 4 5 6 7
34. E.g. 2: no split, but enlargement
y axis
10 m insert o
g h
8 l
k
e f E
6 2
i j
d E1
4
b a
2 c
x axis
0 2 4 6 8 10
Root
E E
1 2
E E E E E E
1 3 4 5 6 7 E
2
a b c d e f g h i j k l m o
E E E E E
3 34 4 5 6 7
35. E.g. 2: no split, but enlargement
y axis
10 m insert o
g h
8 l
k
e f E
6 2
i j
d E1
4
b a
2 c
x axis
0 2 4 6 8 10
Root
E E
1 2
E E E E E E
1 3 4 5 6 7 E
2
a b c d e f g h i j k l m o
E E E E E
3 35 4 5 6 7
36. y axis E.g. 3: split
10 m
g h
8 l
k
e f E
6 2
i j
d E1
4
b a insert o
2 c
x axis
0 2 4 6 8 10
Root
E E
1 2
E E E E E E
1 3 4 5 6 7 E
2
a b c d e f g h i j k o l m
E E E E E
3 36 4 5 6 7
37. y axis E.g. 3: split
10 m
g h
8 l
o k
e f E
6 2
i j
d E1
4
b a
2 c
x axis
0 2 4 6 8 10
Root
E E
1 2
E E E E E E E’
1 3 4 5 6 7 6 E
2
a b c d e f g h i o j k l m
E E E E E
3 37 4 5 6 7