A Locality Preserving Cache-Oblivious Dynamic Dictionary
... We use two separate cache-oblivious structures, the packed-memory structure of Bender, Demaine, and Farach-Colton [10] (which is closely based upon previous structures of Itai, Kronheim and Rodeh [24] and Willard [46, 47, 48, 49]) and the static B-tree of Prokop [33]. The packed-memory structure mai ...
... We use two separate cache-oblivious structures, the packed-memory structure of Bender, Demaine, and Farach-Colton [10] (which is closely based upon previous structures of Itai, Kronheim and Rodeh [24] and Willard [46, 47, 48, 49]) and the static B-tree of Prokop [33]. The packed-memory structure mai ...
Data Structures and Analysis - Department of Computer Science
... we take the input size as a variable (e.g., n) and use it in our counting, we cannot always determine which basic operations will be performed. This is because input size alone is not the only determinant of an algorithm’s running time: often the value of the input matters as well. Consider, for exa ...
... we take the input size as a variable (e.g., n) and use it in our counting, we cannot always determine which basic operations will be performed. This is because input size alone is not the only determinant of an algorithm’s running time: often the value of the input matters as well. Consider, for exa ...
BLAST Tree: Fast Filtering for Genomic Sequence Classification, IEEE International Conference on Bioinformatics and Bioengineering (BIBE 10), Washington, DC, Stuart King, Yanni Sun, Jim Cole, Sakti Pramanik, October 2010.
... requirements since tails are effectively "large leaf nodes" and leaf nodes usually represent the majority of nodes within a tree. B. Space Savings of BlastTree Structure As mentioned, the fanout of the tree drastically changes for different levels of the tree. The top of the tree is effectively full ...
... requirements since tails are effectively "large leaf nodes" and leaf nodes usually represent the majority of nodes within a tree. B. Space Savings of BlastTree Structure As mentioned, the fanout of the tree drastically changes for different levels of the tree. The top of the tree is effectively full ...
Ch18
... given order, from the B + -tree shown below. Show how the tree will shrink and show the final tree. The deleted values are: 65, 75, 43, 18, ...
... given order, from the B + -tree shown below. Show how the tree will shrink and show the final tree. The deleted values are: 65, 75, 43, 18, ...
Data Structures Using C(cs221) - Prof. Ramkrishna More Arts
... 3. Sorting the content of array in ascending or descending order 4. How to find the time complexity of an algorithm? Ready References Let LIST be a collection of data elements into memory. Searching refers to the operation of finding the location of given ITEM in LIST. The searching said to be succe ...
... 3. Sorting the content of array in ascending or descending order 4. How to find the time complexity of an algorithm? Ready References Let LIST be a collection of data elements into memory. Searching refers to the operation of finding the location of given ITEM in LIST. The searching said to be succe ...
index-ceng351-fall2012
... • Primary vs Secondary: If search key contains primary key, then called primary index • Clustered vs. Unclustered: If order of data records is same as or close to data entries (in the index), then it is called clustered. ...
... • Primary vs Secondary: If search key contains primary key, then called primary index • Clustered vs. Unclustered: If order of data records is same as or close to data entries (in the index), then it is called clustered. ...
Data Structures Lab Manual – BE II/IV – I Sem
... Linear Data Structures: The linear data structures as mentioned above are: Arrays, Lists, Stacks, Queues, Dequeues, etc. ...
... Linear Data Structures: The linear data structures as mentioned above are: Arrays, Lists, Stacks, Queues, Dequeues, etc. ...
Chapter 7 Data Structure Transformations
... weak deletion, decreasing the actual size. If by this the actual size of the block becomes 2i−1, we check whether there is a block of nominal size 2i−1; if there is none, we rebuild the block of actual size 2i−1 as block of nominal size 2i−1. Else, we rebuild the block of actual size 2i−1 together w ...
... weak deletion, decreasing the actual size. If by this the actual size of the block becomes 2i−1, we check whether there is a block of nominal size 2i−1; if there is none, we rebuild the block of actual size 2i−1 as block of nominal size 2i−1. Else, we rebuild the block of actual size 2i−1 together w ...
FR2611841189
... given a search window W. It is necessary to place locks on all granules that overlap with the search window in order to prevent writers from inserting into or deleting from these granules until the transaction is completed. Selection starts by checking whether the search window overlaps with ext(T). ...
... given a search window W. It is necessary to place locks on all granules that overlap with the search window in order to prevent writers from inserting into or deleting from these granules until the transaction is completed. Selection starts by checking whether the search window overlaps with ext(T). ...
Chapter9
... TABLE 9-1 Values of first, last, and mid and the number of comparisons for search item 89 Data Structures Using C++ 2E ...
... TABLE 9-1 Values of first, last, and mid and the number of comparisons for search item 89 Data Structures Using C++ 2E ...
Sequential Search Search Algorithms
... • We assume a good probe sequence has been used. That is, for any key, each permutation of 0, 1 . . . , m − 1 is equally likely as a probe ...
... • We assume a good probe sequence has been used. That is, for any key, each permutation of 0, 1 . . . , m − 1 is equally likely as a probe ...
R*-trees
... • The cell in the 4d kd-tree of u is intersected by two different 3-dimensional hyperplanes defined by sides of query Q • The intersection of each pair of such 3dimensional hyper-planes is a 2dimensional hyper-plane • Lemma: # of cells in a d-dimensional kdtree that intersect an axis-parallel fdimen ...
... • The cell in the 4d kd-tree of u is intersected by two different 3-dimensional hyperplanes defined by sides of query Q • The intersection of each pair of such 3dimensional hyper-planes is a 2dimensional hyper-plane • Lemma: # of cells in a d-dimensional kdtree that intersect an axis-parallel fdimen ...
Binary search tree
In computer science, binary search trees (BST), sometimes called ordered or sorted binary trees, are a particular type of containers: data structures that store ""items"" (such as numbers, names and etc.) in memory. They allow fast lookup, addition and removal of items, and can be used to implement either dynamic sets of items, or lookup tables that allow finding an item by its key (e.g., finding the phone number of a person by name).Binary search trees keep their keys in sorted order, so that lookup and other operations can use the principle of binary search: when looking for a key in a tree (or a place to insert a new key), they traverse the tree from root to leaf, making comparisons to keys stored in the nodes of the tree and deciding, based on the comparison, to continue searching in the left or right subtrees. On average, this means that each comparison allows the operations to skip about half of the tree, so that each lookup, insertion or deletion takes time proportional to the logarithm of the number of items stored in the tree. This is much better than the linear time required to find items by key in an (unsorted) array, but slower than the corresponding operations on hash tables.They are a special case of the more general B-tree with order equal to two.