Data Structures
... Arrays are the simplest data structure and are themselves objects. Multiple object references, all to the same type (or a subtype) are stored in consecutive blocks of memory in the array object. Element zero of the array is stored starting at the start address of the array. Subsequent elements follo ...
... Arrays are the simplest data structure and are themselves objects. Multiple object references, all to the same type (or a subtype) are stored in consecutive blocks of memory in the array object. Element zero of the array is stored starting at the start address of the array. Subsequent elements follo ...
hash table
... The point of the hash function is to reduce the range of array indices that need to be handled. Instead of reserving memory space for |U| values, we need to handle only m values so reducing storage The main problem with this scheme is that two values may hash to the same slot, a collision There are ...
... The point of the hash function is to reduce the range of array indices that need to be handled. Instead of reserving memory space for |U| values, we need to handle only m values so reducing storage The main problem with this scheme is that two values may hash to the same slot, a collision There are ...
Storing a Compressed Function with Constant Time Access
... and provide constant access time. For example, if f maps city names to weather, and there are two kinds of weather, f can be stored in close to 1 bit per value, with fast random access to the value for a particular city name. This is of course much less than the space usage required for also storing ...
... and provide constant access time. For example, if f maps city names to weather, and there are two kinds of weather, f can be stored in close to 1 bit per value, with fast random access to the value for a particular city name. This is of course much less than the space usage required for also storing ...
ppt (new version
... PreProcess can be used to speed-up IsMember. What is the worstcase complexity of PreProcess? (ii) Write a function IsMember I that takes advantage of the data structure B. What is the worst-case complexity of IsMember ? What is pre-processing and what is it good for? Think about the following scenar ...
... PreProcess can be used to speed-up IsMember. What is the worstcase complexity of PreProcess? (ii) Write a function IsMember I that takes advantage of the data structure B. What is the worst-case complexity of IsMember ? What is pre-processing and what is it good for? Think about the following scenar ...
Document
... Dictionary in many ways and one of them is with the use of a Hash Table A hash table is an array that holds a set of (key, value) pairs, along with a hashing function which maps keys to a specific position in the array. The hash function takes values as keys and transforms them into a number (0,1,…, ...
... Dictionary in many ways and one of them is with the use of a Hash Table A hash table is an array that holds a set of (key, value) pairs, along with a hashing function which maps keys to a specific position in the array. The hash function takes values as keys and transforms them into a number (0,1,…, ...
投影片 1 - CSIE -NCKU
... Network applications such as IP traceback , route lookup, TCP flow state monitoring, and malware detection often require large data storage resources, fast queries, and frequent updates. Hash tables are traditional data structures that allow large amounts of data to be stored, queried, and updated ...
... Network applications such as IP traceback , route lookup, TCP flow state monitoring, and malware detection often require large data storage resources, fast queries, and frequent updates. Hash tables are traditional data structures that allow large amounts of data to be stored, queried, and updated ...
Heap Sort
... data structure implemented as an array of objects, where the search keys correspond to the array indices ► Insert and find operations involve straight forward array accesses: O(1) time complexity ...
... data structure implemented as an array of objects, where the search keys correspond to the array indices ► Insert and find operations involve straight forward array accesses: O(1) time complexity ...
Bitwise Operators
... • First in, last out data structure. • Can ‘pop’ or ‘push’ things to the top of the ...
... • First in, last out data structure. • Can ‘pop’ or ‘push’ things to the top of the ...
Efficient Minimal Perfect Hash Language Models
... Querying an n-gram from training always returns the correct index into the array But the hash function will also return integer values in the range 0 to N − 1 for n-grams that have not been seen before (i.e. were not used to build the hash function)! ...
... Querying an n-gram from training always returns the correct index into the array But the hash function will also return integer values in the range 0 to N − 1 for n-grams that have not been seen before (i.e. were not used to build the hash function)! ...
CSC 2500 Computer Organization
... If, when an element is inserted, it hashes to the same value as an already inserted element, then we have a collision and need to resolve it. There are several methods for dealing with collision, we will discuss the simplest approach: separate chaining. ...
... If, when an element is inserted, it hashes to the same value as an already inserted element, then we have a collision and need to resolve it. There are several methods for dealing with collision, we will discuss the simplest approach: separate chaining. ...
An Optimal Bloom Filter Replacement
... false positive rate decreases. For example, to get a probability at most , the number of bits needed to false positive rate below 1%, 7 memory accesses are needed. If S is large this will almost surely mean 7 ∗ This work was initiated during Dagstuhl seminar No. 04091 cache misses, since the addres ...
... false positive rate decreases. For example, to get a probability at most , the number of bits needed to false positive rate below 1%, 7 memory accesses are needed. If S is large this will almost surely mean 7 ∗ This work was initiated during Dagstuhl seminar No. 04091 cache misses, since the addres ...
Data structures and web filtering
... code required to compile and run the project. • Tests: You should include code for all your test cases. • README.txt: This file should contain your name, your NetID, all known issues you have with your submitted code, and the names of anyone you have discussed the homework with. You should briefly d ...
... code required to compile and run the project. • Tests: You should include code for all your test cases. • README.txt: This file should contain your name, your NetID, all known issues you have with your submitted code, and the names of anyone you have discussed the homework with. You should briefly d ...
Hashing and Packet Based Algorithms
... – “Constant” time (time to hash). – Small amount of space. – But with some probability of being wrong. ...
... – “Constant” time (time to hash). – Small amount of space. – But with some probability of being wrong. ...
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)).