Maintaining External Memory Efficient Hash Tables
... bits. Update operations are supported with high probability in constant time and the algorithm is stable. For the static case, Hagerup and Tholey [10] hold the space record: They show how to construct a minimal perfect hash function in expected O(n + log log |U |) time such that its encoding require ...
... bits. Update operations are supported with high probability in constant time and the algorithm is stable. For the static case, Hagerup and Tholey [10] hold the space record: They show how to construct a minimal perfect hash function in expected O(n + log log |U |) time such that its encoding require ...
Efficient data structures for sparse network representation
... following of pointers, usually referring to memory that is not loaded into the processor cache. In addition, there is an allocation overhead for each node, typical value of which is two machine words [8] — that is, 64 or 128 bits — per node. The main virtues of trees, ordered iteration and small wor ...
... following of pointers, usually referring to memory that is not loaded into the processor cache. In addition, there is an allocation overhead for each node, typical value of which is two machine words [8] — that is, 64 or 128 bits — per node. The main virtues of trees, ordered iteration and small wor ...
Introduction to Hash Tables
... • declare hash obj(dataset: 'dataset_name', duplicate: 'replace' | 'error', hashexp: n, ordered: 'a' | 'd' | 'no', suminc: 'count_var'); • dataset: loads the hash object from a data set. • duplicate: controls how duplicate keys are handled when loading from a data set. • hashexp: n declares 2 to the ...
... • declare hash obj(dataset: 'dataset_name', duplicate: 'replace' | 'error', hashexp: n, ordered: 'a' | 'd' | 'no', suminc: 'count_var'); • dataset: loads the hash object from a data set. • duplicate: controls how duplicate keys are handled when loading from a data set. • hashexp: n declares 2 to the ...
Java Review
... A log file or audit trail is a dictionary implemented by means of an unsorted sequence – We store the items of the dictionary in a sequence (based on a doubly-linked list or array), in arbitrary order ...
... A log file or audit trail is a dictionary implemented by means of an unsorted sequence – We store the items of the dictionary in a sequence (based on a doubly-linked list or array), in arbitrary order ...
Vectors
... Growable Array-based Vector In a push operation, when Algorithm push(o) the array is full, instead of if t = S.length - 1 then throwing an exception, we A new array of can replace the array with size … a larger one for i 0 to t do A[i] S[i] How large should the new SA array be? ...
... Growable Array-based Vector In a push operation, when Algorithm push(o) the array is full, instead of if t = S.length - 1 then throwing an exception, we A new array of can replace the array with size … a larger one for i 0 to t do A[i] S[i] How large should the new SA array be? ...
Chapter 12: Dictionary (Hash Tables)
... by a hash algorithm is the remainder after dividing this value by the hash table size. So, for example, Amy’s hash function returns values from 0 to 25. She divided by the table size (6) in order to get an index. The idea of hashing can be used to create a variety of different data structures. Of co ...
... by a hash algorithm is the remainder after dividing this value by the hash table size. So, for example, Amy’s hash function returns values from 0 to 25. She divided by the table size (6) in order to get an index. The idea of hashing can be used to create a variety of different data structures. Of co ...
Tutorial 4 – ADT, Containers, Sequence Containers 1. Container
... For array‐based list, most of the time only 1 element is accessed/modified as no shifting is required. However, some lists allow their capacity (not just size) to grow dynamically. STL vector is one such list.
For such lists, when the array is already at capacity, the entire underlying array ha ...
... For array‐based list, most of the time only 1 element is accessed/modified as no shifting is required. However, some lists allow their capacity (not just size) to grow dynamically. STL vector
6.897 Advanced Data Structures (Spring`05)
... consecutive elements of the summary. We construct a data structure for the summary elements, and then we recurse for the intervals between every two summary elements, as well as the two extremes (before the min and max in the summary). When we’re down to O(1) elements we use some brute-force solutio ...
... consecutive elements of the summary. We construct a data structure for the summary elements, and then we recurse for the intervals between every two summary elements, as well as the two extremes (before the min and max in the summary). When we’re down to O(1) elements we use some brute-force solutio ...
Hashing - METU OCW
... a subset of the operations allowed by binary search trees. • The implementation of hash tables is called hashing. • Hashing is a technique used for performing insertions, deletions and finds in constant average time (i.e. O(1)) • This data structure, however, is not efficient in operations that requ ...
... a subset of the operations allowed by binary search trees. • The implementation of hash tables is called hashing. • Hashing is a technique used for performing insertions, deletions and finds in constant average time (i.e. O(1)) • This data structure, however, is not efficient in operations that requ ...
ppt - EECG Toronto
... • Create array with more entries than we have items to store • “hashing function” h(key) to map from key to array entry ...
... • Create array with more entries than we have items to store • “hashing function” h(key) to map from key to array entry ...
6.18_Exam2Review - Help-A-Bull
... Increment c by 1 b. If myArray[r] < myArray[c] i. Swap myArray[r] and myArray[c] ...
... Increment c by 1 b. If myArray[r] < myArray[c] i. Swap myArray[r] and myArray[c] ...
Hashing
... a subset of the operations allowed by binary search trees. • The implementation of hash tables is called hashing. • Hashing is a technique used for performing insertions, deletions and finds in constant average time (i.e. O(1)) • This data structure, however, is not efficient in operations that requ ...
... a subset of the operations allowed by binary search trees. • The implementation of hash tables is called hashing. • Hashing is a technique used for performing insertions, deletions and finds in constant average time (i.e. O(1)) • This data structure, however, is not efficient in operations that requ ...
Hashing - METU Computer Engineering
... a subset of the operations allowed by binary search trees. • The implementation of hash tables is called hashing. • Hashing is a technique used for performing insertions, deletions and finds in constant average time (i.e. O(1)) • This data structure, however, is not efficient in operations that requ ...
... a subset of the operations allowed by binary search trees. • The implementation of hash tables is called hashing. • Hashing is a technique used for performing insertions, deletions and finds in constant average time (i.e. O(1)) • This data structure, however, is not efficient in operations that requ ...
Bloom filter
A Bloom filter is a space-efficient probabilistic data structure, conceived by Burton Howard Bloom in 1970, that is used to test whether an element is a member of a set. False positive matches are possible, but false negatives are not, thus a Bloom filter has a 100% recall rate. In other words, a query returns either ""possibly in set"" or ""definitely not in set"". Elements can be added to the set, but not removed (though this can be addressed with a ""counting"" filter). The more elements that are added to the set, the larger the probability of false positives.Bloom proposed the technique for applications where the amount of source data would require an impractically large amount of memory if ""conventional"" error-free hashing techniques were applied. He gave the example of a hyphenation algorithm for a dictionary of 500,000 words, out of which 90% follow simple hyphenation rules, but the remaining 10% require expensive disk accesses to retrieve specific hyphenation patterns. With sufficient core memory, an error-free hash could be used to eliminate all unnecessary disk accesses; on the other hand, with limited core memory, Bloom's technique uses a smaller hash area but still eliminates most unnecessary accesses. For example, a hash area only 15% of the size needed by an ideal error-free hash still eliminates 85% of the disk accesses, an 85–15 form of the Pareto principle (Bloom (1970)).More generally, fewer than 10 bits per element are required for a 1% false positive probability, independent of the size or number of elements in the set (Bonomi et al. (2006)).