Data Structures for NLP
... while (entry && entry->v->key!=key) entry=entry->next; if (!entry) make_new_entry(key); return entry; ...
... while (entry && entry->v->key!=key) entry=entry->next; if (!entry) make_new_entry(key); return entry; ...
sorted
... Not indexable (immediately) Space is allocate for each new element and consecutive elements are linked together with a pointer. Note, though, that the middle can be modified in constant time. head ...
... Not indexable (immediately) Space is allocate for each new element and consecutive elements are linked together with a pointer. Note, though, that the middle can be modified in constant time. head ...
ch11
... • The two attributes that define a data type are: domain and a set of operations. • An array is a collection of items of the same type. It is efficient to select an element. The addresses of array[i] is the address of array + sizeof(overhead) + i*sizeof(type). For example, if the type is int, then s ...
... • The two attributes that define a data type are: domain and a set of operations. • An array is a collection of items of the same type. It is efficient to select an element. The addresses of array[i] is the address of array + sizeof(overhead) + i*sizeof(type). For example, if the type is int, then s ...
Encoding Nearest Larger Values
... Unfortunately, none of the above sequences appears in the Online Encyclopedia of Integer Sequences4 . Consider the sequence generated by some arbitrary tie breaking rule. If zi is the i-th term in this sequence, then limn→∞ lg(zn )/n is the constant factor in the asymptotic space bound required to ...
... Unfortunately, none of the above sequences appears in the Online Encyclopedia of Integer Sequences4 . Consider the sequence generated by some arbitrary tie breaking rule. If zi is the i-th term in this sequence, then limn→∞ lg(zn )/n is the constant factor in the asymptotic space bound required to ...
PDF
... the probability of false positives is taken over the choice of a uniformly sampled element (instead of over the internal randomness of the data structure), but note that the exact same ideas carry over to randomized ones. Specifically, for using asymptotically optimal space, an approximate membershi ...
... the probability of false positives is taken over the choice of a uniformly sampled element (instead of over the internal randomness of the data structure), but note that the exact same ideas carry over to randomized ones. Specifically, for using asymptotically optimal space, an approximate membershi ...
Document
... Dynamic arrays are arrays that grow (or shrink) as required In fact a new array is created when the old array becomes full by creating a new array object, copying over the values from the old array and then assigning the new array to the existing array reference variable ...
... Dynamic arrays are arrays that grow (or shrink) as required In fact a new array is created when the old array becomes full by creating a new array object, copying over the values from the old array and then assigning the new array to the existing array reference variable ...
Space-Efficient Data Structures for Top-k
... recursively using a priority queue to retrieve top-k ...
... recursively using a priority queue to retrieve top-k ...
Indexing and Hashing.key
... • Non-leaf nodes have the same pointer/search-key value structure, except that the pointers lead to further tree nodes, and the first and last pointers (P1 and Pn) point to the tree nodes for search-key values that are less than and greater than the node’s values, respectively Note how a B+-tree’s n ...
... • Non-leaf nodes have the same pointer/search-key value structure, except that the pointers lead to further tree nodes, and the first and last pointers (P1 and Pn) point to the tree nodes for search-key values that are less than and greater than the node’s values, respectively Note how a B+-tree’s n ...
A Comparison of Adaptive Radix Trees and Hash Tables
... populated branches, such as uniform random distribution. In the context of our example, each node would contain an array of 256 pointers, even if only a single child node exists; leading to high memory overhead. This is the reason why radix trees have usually been considered as a data structure that ...
... populated branches, such as uniform random distribution. In the context of our example, each node would contain an array of 256 pointers, even if only a single child node exists; leading to high memory overhead. This is the reason why radix trees have usually been considered as a data structure that ...
Efficient representation of integer sets
... integer sets and implement some basic set theoretical operations such as union, intersection and difference. In computational terms, there are two main advantages that come directly out of this representation. First, the amount of memory needed by this data structure is very small. Second, any gener ...
... integer sets and implement some basic set theoretical operations such as union, intersection and difference. In computational terms, there are two main advantages that come directly out of this representation. First, the amount of memory needed by this data structure is very small. Second, any gener ...
List of Practical - Guru Tegh Bahadur Institute of Technology
... Create a linked list with nodes having information about a student and Insert a new node at specified position. Create a linked list with nodes having information about a student and Delete of a node with the roll number of student specified. Create a linked list with nodes having information about ...
... Create a linked list with nodes having information about a student and Insert a new node at specified position. Create a linked list with nodes having information about a student and Delete of a node with the roll number of student specified. Create a linked list with nodes having information about ...
Hashcube: Efficiently-queryable skycube compression
... faster than CSC. The iteration of all hash keys only takes 5 − 10× as long as the direct lookup in the lattice. Further, on anticorrelated data, Hashcube converges towards the lattice with increasing |P | (Figure 4). By contrast, CSC is rather slow, beaten in most instances by simply computing the s ...
... faster than CSC. The iteration of all hash keys only takes 5 − 10× as long as the direct lookup in the lattice. Further, on anticorrelated data, Hashcube converges towards the lattice with increasing |P | (Figure 4). By contrast, CSC is rather slow, beaten in most instances by simply computing the s ...
h + 1
... 1. Are we guaranteed to find an empty cell if there is one? 2. Are we guaranteed we won’t be checking the same cell twice during one insertion? 3. What should the load factor be to obtain O(1) average-case insert, search, and delete? ...
... 1. Are we guaranteed to find an empty cell if there is one? 2. Are we guaranteed we won’t be checking the same cell twice during one insertion? 3. What should the load factor be to obtain O(1) average-case insert, search, and delete? ...
file (215 KB, doc)
... 4. What is the best definition of a collision in a hash table? o A. Two entries are identical except for their keys. o B. Two entries with different data have the exact same key. o C. Two entries with different keys have the same exact hash value. o D. Two entries with the exact same key have differ ...
... 4. What is the best definition of a collision in a hash table? o A. Two entries are identical except for their keys. o B. Two entries with different data have the exact same key. o C. Two entries with different keys have the same exact hash value. o D. Two entries with the exact same key have differ ...
List of Practicals - Guru Tegh Bahadur Institute of Technology
... Create a linked list with nodes having information about a student and Insert a new node at specified position. Create a linked list with nodes having information about a student and Delete of a node with the roll number of student specified. Create a linked list with nodes having information about ...
... Create a linked list with nodes having information about a student and Insert a new node at specified position. Create a linked list with nodes having information about a student and Delete of a node with the roll number of student specified. Create a linked list with nodes having information about ...
arraylists
... The Array List ADT extends the notion of array by storing a sequence of arbitrary objects ...
... The Array List ADT extends the notion of array by storing a sequence of arbitrary objects ...
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)).