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Application to IR and WWW
Published in Suman Saha, Shailendra Shukla, Advanced Data Structures, 2019
Auto complete functionality is used widely in mobile applications and text editor. A trie is an efficient data structure commonly used to implement auto complete functionality. Trie provides an easy way to search for the possible dictionary words to complete a query for the following reasons. Looking up data in a trie is faster in the worst case O(n) (n = size of the string involved in the operation) time compared to an imperfect hash table. An imperfect hash table can have key collisions. A key collision is the hash function mapping of different keys to the same position in a hash table. A trie can provide an alphabetical ordering of the entries by key. Searching in a trie helps trace pointers to reach a node entered by the user. By exploring a trie traversing down the tree, we can easily enumerate all strings that complete the user input. The steps for resolving a query are: Search for given query using the standard trie search algorithm.If query prefix is not present, indicate it by returning −1.If a query is the end of a word in trie, print the query. The end of the query can be checked quickly by checking whether the last matching node has an end word flat. Tries use such flags to mark the ends of word nodes during searches.If last matching node of query has no children, return.Otherwise, recursively print all nodes under subtree of last matching node.
Introduction
Published in Randall L. Eubank, Ana Kupresanin, Statistical Computing in C++ and R, 2011
Randall L. Eubank, Ana Kupresanin
A hash table is a data structure that solves the problem of efficiently mapping a sparse set of keys, or more generally a set of keys that are not integers, into an array while still conserving storage. Knuth (1998b) is one of the best references for hashing. An overview of the topic from a C++ perspective is provided by Sedgewick (1998).
Forwarding
Published in Heqing Zhu, Data Plane Development Kit (DPDK), 2020
Yipeng Wang, Jasvinder Singh, Zhe Tao, Liang Ma, Heqing Zhu
During key insertion into a hash table, keys are hashed and placed into the hash table at specific locations; according to the hash value, hash collision could happen. When two different keys have the same hash value and collide, the latter key might be failed to be inserted into the table. Hash tables use different algorithms to handle the hash collision. In this chapter, we focus on the default hash table algorithm in DPDK (hash library), which is based on cuckoo hashing algorithm.
Grid k-d tree approach for point location in polyhedral data sets – application to explicit MPC
Published in International Journal of Control, 2020
In addition to the BST data structure mentioned above, other data structures have been applied to the point location problem. The hash table is a data structure used to implement an associative array where the time complexity is O(1). A two-stage algorithm was proposed which combines the direct search method with the hash table (Bayat, Johansen, & Jalali, 2011). In this approach, the hash table divides the whole state space into many grids in which the number of affine control laws differs greatly, it then locates the corresponding partition using the direct search method, which produces a fairly low online computation efficiency. To improve online searching efficiency a two-level hash table structure and grid-BST structure based on the hash table method have been used. (Zhang, Xiu, Xie, & Hu, 2016). Other data structures are also used to solve the point location problem in EMPC, namely, quardtree, graph, connected graph, etc. (Herceg, Jones, Kvasnica, & Morari, 2015; Herceg, Mariethoz, & Morari, 2013; Jafargholi, Peyrl, Zanarini, & Herceg, 2014; Oberdieck, Diangelakis, & Pistikopoulos, 2017).
Algorithmic Improvements to MCNP5 for High-Resolution Fusion Neutronics Analyses
Published in Fusion Science and Technology, 2018
Scott W. Mosher, Stephen C. Wilson
In ORNL-TN, a different algorithm was implemented to improve the performance of this conversion process for very large geometry inputs. At the outset, a hash table13 is constructed to map surface IDs to indices. In the average case, a hash table lookup is an O(1) operation, meaning that the average lookup time is independent of the number of entries in the table. Thus, using a hash table reduces the average complexity of surface identifier conversion process from O(n2) to O(n). It should be noted that the worst-case performance of a hash table lookup is O(n). However, this can generally be avoided by using a well-dimensioned table and a hash function that is appropriate for the class of lookup keys being used. For this implementation, Knuth’s multiplicative hash function14 was selected for its simplicity and was found to perform well in tests using very large geometry inputs.