Spatial Query Integrity with Voronoi Neighbors ABSTRACT
... platform for hosting data from businesses and individuals, further enabling many location-based applications. Nevertheless, in this database outsourcing paradigm, the authentication of the query results at the client remains a challenging problem. In this paper, we focus on the Outsourced Spatial Da ...
... platform for hosting data from businesses and individuals, further enabling many location-based applications. Nevertheless, in this database outsourcing paradigm, the authentication of the query results at the client remains a challenging problem. In this paper, we focus on the Outsourced Spatial Da ...
- Free Documents
... There are six simple recursive algorithms for tree traversal. The most commonly used ones are inorder LNR postorder LRN preorder NLR. Another technique is to move left to right from level to level. This algorithm is iterative, and its implementation involves using a queue. ...
... There are six simple recursive algorithms for tree traversal. The most commonly used ones are inorder LNR postorder LRN preorder NLR. Another technique is to move left to right from level to level. This algorithm is iterative, and its implementation involves using a queue. ...
CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE
... A linked list is a collection of records called nodes, each containing at least one field that gives the location of the next node in the list. The linked list is a very flexible dynamic data structure. It is a low level structure upon which high level data structure can be built. 3 marks 1. Create: ...
... A linked list is a collection of records called nodes, each containing at least one field that gives the location of the next node in the list. The linked list is a very flexible dynamic data structure. It is a low level structure upon which high level data structure can be built. 3 marks 1. Create: ...
File Systems - The University of Alabama in Huntsville
... serial access is enforced to make sure data is consistent. That is, one operation is performed in its entirety before the next one begins. • However, a read from user 1, followed by a write from user 2, followed by a read from user 1 means that user 1 is reading two different versions of the file. U ...
... serial access is enforced to make sure data is consistent. That is, one operation is performed in its entirety before the next one begins. • However, a read from user 1, followed by a write from user 2, followed by a read from user 1 means that user 1 is reading two different versions of the file. U ...
INTRODUCTION TO DATA STRUCTURE
... GRAPHS• A graph is a kind of data structure, specifically an abstract data type (ADT) that consist of a set of nodes and a set of edges that establish relationships between the node. • The graph ADT follows directly from the graph, a graph G defined as follows G=(V,E), where V is a finite, non-empt ...
... GRAPHS• A graph is a kind of data structure, specifically an abstract data type (ADT) that consist of a set of nodes and a set of edges that establish relationships between the node. • The graph ADT follows directly from the graph, a graph G defined as follows G=(V,E), where V is a finite, non-empt ...
dataStruct
... • If there’s an element x with key k, then T [k] contains a pointer to x. • Otherwise, T [k] is empty, represented by NIL. ...
... • If there’s an element x with key k, then T [k] contains a pointer to x. • Otherwise, T [k] is empty, represented by NIL. ...
A Dynamic Index for Multi-Dimensional Objects
... R-trees are an extension of B-trees for multi-dimensional objects that are either points or regions. Like B-trees, they are balanced (all leaf nodes appear on the same level, which is a desirable feature) and guarantee that the space utilization is at least 5070. However, if R-Trees are built using ...
... R-trees are an extension of B-trees for multi-dimensional objects that are either points or regions. Like B-trees, they are balanced (all leaf nodes appear on the same level, which is a desirable feature) and guarantee that the space utilization is at least 5070. However, if R-Trees are built using ...
The Search Problem
... So the worst case search would require log (N + 1) key comparisons. For an unsuccessful search, the decision tree must have at least 1 + log (N + 1) levels, so the worst case search would require log (N + 1) key comparisons. ...
... So the worst case search would require log (N + 1) key comparisons. For an unsuccessful search, the decision tree must have at least 1 + log (N + 1) levels, so the worst case search would require log (N + 1) key comparisons. ...
Class Notes for Week 4
... HEAPIFY is an important subroutine for maintaining heap property. Given a node i in the heap with children l and r. Each sub-tree rooted at l and r is assumed to be a heap. The sub-tree rooted at i may violate the heap property [ key(i) > key(l) OR key(i) > key(r) ] Thus Heapify lets the value of th ...
... HEAPIFY is an important subroutine for maintaining heap property. Given a node i in the heap with children l and r. Each sub-tree rooted at l and r is assumed to be a heap. The sub-tree rooted at i may violate the heap property [ key(i) > key(l) OR key(i) > key(r) ] Thus Heapify lets the value of th ...
CS790 – Introduction to Bioinformatics
... The AVL tree is a BST with the following additional property: For every node, the heights of its left and right subtrees differ by at most 1. • The depth of an n node tree will be, at most, O(log n), so search and insert are O(log n) operations, even in the worst case. • Insert and delete must mai ...
... The AVL tree is a BST with the following additional property: For every node, the heights of its left and right subtrees differ by at most 1. • The depth of an n node tree will be, at most, O(log n), so search and insert are O(log n) operations, even in the worst case. • Insert and delete must mai ...
Chapter 12 Trees - Margaret M. Fleck
... Notice that the values at one level aren’t uniformly bigger than the values at the next lower level. For example, 18 in the bottom level is larger than 12 on the middle level. But values never decrease as you move along a path from a leaf up to the root. Trees with the heap property are convenient f ...
... Notice that the values at one level aren’t uniformly bigger than the values at the next lower level. For example, 18 in the bottom level is larger than 12 on the middle level. But values never decrease as you move along a path from a leaf up to the root. Trees with the heap property are convenient f ...
Index Structures
... index blocks, except that the B-tree block has an extra pointer. We pick n to be as large as will allow n+1 pointers and n keys to fit in one block. Suppose block size is 4096 bytes Keys are integers of 4 bytes Pointers are 8 bytes If no header information is kept on the blocks, we want to find the ...
... index blocks, except that the B-tree block has an extra pointer. We pick n to be as large as will allow n+1 pointers and n keys to fit in one block. Suppose block size is 4096 bytes Keys are integers of 4 bytes Pointers are 8 bytes If no header information is kept on the blocks, we want to find the ...
types of searchings - SSC Recruitment Results
... calculate than average time. If there are n items in our collection whether it is stored as an array or as linked list-then it is obvious that in the worst case, when there is no item in the collection with the desired key, then n comparisons of the key with keys of the items in the collection will ...
... calculate than average time. If there are n items in our collection whether it is stored as an array or as linked list-then it is obvious that in the worst case, when there is no item in the collection with the desired key, then n comparisons of the key with keys of the items in the collection will ...
View PDF - CiteSeerX
... disk accesses (where M is the size of main memory). B While these algorithms and techniques focused on suffix tree construction in secondary storage, the problems of searching and updating suffix trees (insertion/deletion of all suffixes of a string) on disks have not received as much attention. An ...
... disk accesses (where M is the size of main memory). B While these algorithms and techniques focused on suffix tree construction in secondary storage, the problems of searching and updating suffix trees (insertion/deletion of all suffixes of a string) on disks have not received as much attention. An ...
Binary Trees
... (a) T is a tree if the set of nodes is empty. (An empty tree is a tree.) (b) The set consists of a root, R, and exactly two distinct binary trees, the left subtree TL and the right subtreeTR. The nodes in T consist of node R and all the nodes in TL and TR. ...
... (a) T is a tree if the set of nodes is empty. (An empty tree is a tree.) (b) The set consists of a root, R, and exactly two distinct binary trees, the left subtree TL and the right subtreeTR. The nodes in T consist of node R and all the nodes in TL and TR. ...
Red-Black Trees - York College of Pennsylvania
... pass back up the tree to update node heights and potentially rebalance the tree - A top-down insertion into a red-black tree requires only a single pass down the tree ...
... pass back up the tree to update node heights and potentially rebalance the tree - A top-down insertion into a red-black tree requires only a single pass down the tree ...
B-tree
In computer science, a B-tree is a tree data structure that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time. The B-tree is a generalization of a binary search tree in that a node can have more than two children (Comer 1979, p. 123). Unlike self-balancing binary search trees, the B-tree is optimized for systems that read and write large blocks of data. B-trees are a good example of a data structure for external memory. It is commonly used in databases and filesystems.