Full text - Gavin E. Crooks
... construction of datasets of proteins with given sequence and structural properties. The SCOP database is a manually curated structural classification which groups together proteins on the basis of structural similarity. The ASTRAL compendium provides non redundant subsets of SCOP domains on the basi ...
... construction of datasets of proteins with given sequence and structural properties. The SCOP database is a manually curated structural classification which groups together proteins on the basis of structural similarity. The ASTRAL compendium provides non redundant subsets of SCOP domains on the basi ...
38 POINT LOCATION
... through q. We then compare against the separating chain and recursively search the left or right subtree. Thus, this separating chain method [LP77] inspects O(log n) tree nodes at a cost of O(log n) each, giving O(log2 n) query time. To reduce the query time, we can use fractional cascading [CG86, E ...
... through q. We then compare against the separating chain and recursively search the left or right subtree. Thus, this separating chain method [LP77] inspects O(log n) tree nodes at a cost of O(log n) each, giving O(log2 n) query time. To reduce the query time, we can use fractional cascading [CG86, E ...
SMALTA: Practical and Near
... nexthop. Figure 2 shows a slightly more complex example whereby the two entries with nexthop A can be aggregated to a single /14 entry even though there is an entry with nexthop B in between them. We are by no means the first to exploit this type of compression. In 1998, Draves et al. designed an al ...
... nexthop. Figure 2 shows a slightly more complex example whereby the two entries with nexthop A can be aggregated to a single /14 entry even though there is an entry with nexthop B in between them. We are by no means the first to exploit this type of compression. In 1998, Draves et al. designed an al ...
Sorted Range Reporting
... We can represent the interval [1, c] as a union of O(log n) node ranges for nodes vi ∈ T . The search procedure visits each vi and finds the leftmost point (that is, the first point) in every list L(vi ). Those points are kept in a data structure D. Then we repeat the following step k times: We fin ...
... We can represent the interval [1, c] as a union of O(log n) node ranges for nodes vi ∈ T . The search procedure visits each vi and finds the leftmost point (that is, the first point) in every list L(vi ). Those points are kept in a data structure D. Then we repeat the following step k times: We fin ...
Addison Wesley - Algorithms in Java, Parts 1-4, 3rd Edition
... There is a great deal of flexibility in how the material here can be taught, depending on the taste of the instructor and the preparation of the students. There is sufficient coverage of basic material for the book to be used to teach data structures to beginners, and there is sufficient detail and ...
... There is a great deal of flexibility in how the material here can be taught, depending on the taste of the instructor and the preparation of the students. There is sufficient coverage of basic material for the book to be used to teach data structures to beginners, and there is sufficient detail and ...
Judy IV Shop Manual - Judy arrays
... You can think of digital trees as peeling (decoding) leading bits off a key until only one bit is left, but in the case of an unbounded variable-size key there is no definite “bottom” (that is, a definite last bit or maximum length for every key). However, there are always unpopulated subexpanses, e ...
... You can think of digital trees as peeling (decoding) leading bits off a key until only one bit is left, but in the case of an unbounded variable-size key there is no definite “bottom” (that is, a definite last bit or maximum length for every key). However, there are always unpopulated subexpanses, e ...
A Fully-Functional Static and Dynamic Succinct Trees
... as we will see) plus insert and delete, requires this time even in the amortized sense, by a reduction from Fredman and Saks [1989] lower bounds on rank queries. Moreover, we are able to attach and detach whole subtrees, in time O(log1+ n) for any constant > 0 (see Section 2.3 for the precise det ...
... as we will see) plus insert and delete, requires this time even in the amortized sense, by a reduction from Fredman and Saks [1989] lower bounds on rank queries. Moreover, we are able to attach and detach whole subtrees, in time O(log1+ n) for any constant > 0 (see Section 2.3 for the precise det ...
Summarizing Large Query Logs in Ettu
... the task of analyzing such a query log. She might first attempt to identify some aggregate properties about the log. For example, she might count how many times each table is accessed or the frequency with which different classes of join predicates occur. Unfortunately, such fine-grained properties ...
... the task of analyzing such a query log. She might first attempt to identify some aggregate properties about the log. For example, she might count how many times each table is accessed or the frequency with which different classes of join predicates occur. Unfortunately, such fine-grained properties ...
COMPRESSED SUFFIX ARRAYS AND SUFFIX TREES WITH
... positions, where P occurs in T . Efficient offline string matching algorithms, such as that of Knuth, Morris, and Pratt [49], can answer each individual query in O(m + n) time via an efficient text scan. The large mass of existing online text documents makes it infeasible to scan through all the documents ...
... positions, where P occurs in T . Efficient offline string matching algorithms, such as that of Knuth, Morris, and Pratt [49], can answer each individual query in O(m + n) time via an efficient text scan. The large mass of existing online text documents makes it infeasible to scan through all the documents ...
Accelerating Online LCA with Functional Data Structures
... We store a linked list of complete trees, where we are allowed to have two trees of the same size at the front of the list, but after that all trees are of strictly increasing height. data Tree a = Tip a | Bin a (Tree a) (Tree a) data Path a = Nil | Cons !Int !Int (Tree a) (Path a) length :: Path a ...
... We store a linked list of complete trees, where we are allowed to have two trees of the same size at the front of the list, but after that all trees are of strictly increasing height. data Tree a = Tip a | Bin a (Tree a) (Tree a) data Path a = Nil | Cons !Int !Int (Tree a) (Path a) length :: Path a ...
Problem Description Earned Max 1 Inheritance/Polymorphism 2
... In the table below, indicate in the right-hand column the output produced by the statement in the left-hand column. If the statement produces more than one line of output, indicate the line breaks with slashes as in "a / b / c" to indicate three lines of output with "a" followed by "b" followed by " ...
... In the table below, indicate in the right-hand column the output produced by the statement in the left-hand column. If the statement produces more than one line of output, indicate the line breaks with slashes as in "a / b / c" to indicate three lines of output with "a" followed by "b" followed by " ...
Elementary Data Structures
... Need to update next and prev pointers in DLNode Insertion from the empty case (both pointers are null) and removal from a single-element case (both point to the single element) need to be handled Or, make pointers point to dummy nodes (also called sentinels), so that insertion and removal need not w ...
... Need to update next and prev pointers in DLNode Insertion from the empty case (both pointers are null) and removal from a single-element case (both point to the single element) need to be handled Or, make pointers point to dummy nodes (also called sentinels), so that insertion and removal need not w ...
Indexing Structures for Searching in Metric Spaces
... In some applications, the metric space turns out to be of a particular type called vector space, where the elements consist of k coordinates (often termed feature vectors). For example, images are often represented by color and shape histograms [38, 48], typically consisting of 64 or 256 values. A l ...
... In some applications, the metric space turns out to be of a particular type called vector space, where the elements consist of k coordinates (often termed feature vectors). For example, images are often represented by color and shape histograms [38, 48], typically consisting of 64 or 256 values. A l ...
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.