Data Structures So Far
... The time complexity of an algorithm is the largest time required on any input of size n. (Worst case analysis.) O(n2): For any input size n ≥ n0, the algorithm takes no more than cn2 time on every input. Ω(n2): For any input size n ≥ n0, the algorithm takes at least cn2 time on at least one inpu ...
... The time complexity of an algorithm is the largest time required on any input of size n. (Worst case analysis.) O(n2): For any input size n ≥ n0, the algorithm takes no more than cn2 time on every input. Ω(n2): For any input size n ≥ n0, the algorithm takes at least cn2 time on at least one inpu ...
Relational Database
... – A table is not allowed to have 2 different rows that have the same value for the key – Database systems enforce key constraints • By blocking any attempt to modify a table that will result in a violation of the key constraint ...
... – A table is not allowed to have 2 different rows that have the same value for the key – Database systems enforce key constraints • By blocking any attempt to modify a table that will result in a violation of the key constraint ...
Comparison of Spatial Hashing Algorithms for Mobile Wireless Network Simulations
... To support the requirements above, several data structure/algorithm combinations were considered. Each differs in their scalability and efficiency to perform the above operations, so their selection depends on how often the above operations are performed. Previous studies [1-11] have suggested algor ...
... To support the requirements above, several data structure/algorithm combinations were considered. Each differs in their scalability and efficiency to perform the above operations, so their selection depends on how often the above operations are performed. Previous studies [1-11] have suggested algor ...
Midterm Solutions
... P. The weighted quick-union data type achieves logarithmic time per union,find, and connected operation. P. In Java, hashCode() returns a 32-bit integer, so there are only 232 possible values. Since there are infinitely many different strings, an infinite number of strings share the same hash code. ...
... P. The weighted quick-union data type achieves logarithmic time per union,find, and connected operation. P. In Java, hashCode() returns a 32-bit integer, so there are only 232 possible values. Since there are infinitely many different strings, an infinite number of strings share the same hash code. ...
Asynchronous Memory Access Chaining
... on a long-latency memory access. Consequently, the performance bottleneck for many database operations is accessing main memory [2, 21]. A modern CPU employs multiple out-of-order cores, which are designed to hide the memory access latency by identifying and issuing multiple independent memory acces ...
... on a long-latency memory access. Consequently, the performance bottleneck for many database operations is accessing main memory [2, 21]. A modern CPU employs multiple out-of-order cores, which are designed to hide the memory access latency by identifying and issuing multiple independent memory acces ...
An Optimal Bloom Filter Replacement
... particular, if O(n/) space is used, a single hash function with Bloom filters for rather small . In section 5 suffices. In distributed applications, the data structure we describe an alternative data structure based on [8], may be compressed to the optimal n log(1/) + O(n) for which no provably g ...
... particular, if O(n/) space is used, a single hash function with Bloom filters for rather small . In section 5 suffices. In distributed applications, the data structure we describe an alternative data structure based on [8], may be compressed to the optimal n log(1/) + O(n) for which no provably g ...
Beyond Bloom Filters
... • Bloom filter provides an answer in – “Constant” time (time to hash). – Small amount of space. – But with some probability of being wrong. ...
... • Bloom filter provides an answer in – “Constant” time (time to hash). – Small amount of space. – But with some probability of being wrong. ...
Lecture 9. Semantic Analysis – Scoping and Symbol Table
... Generally, symbol table is only needed to answer those two questions, i.e., once all declarations have been processed to build the symbol table, and all uses have been processed to link each ID node in the abstract-syntax tree with the corresponding symbol-table entry, then the symbol table itself i ...
... Generally, symbol table is only needed to answer those two questions, i.e., once all declarations have been processed to build the symbol table, and all uses have been processed to link each ID node in the abstract-syntax tree with the corresponding symbol-table entry, then the symbol table itself i ...
Midterm Solutions
... Implement the symbol table using a separate-chaining (or linear-probing) hash table so that each insert/search takes O(1) time. Thus, the overall subroutine takes O(N ) time. To determine whether any 3 (or more lines) intersect at a point, run the previous subroutine N times, once for each line i. T ...
... Implement the symbol table using a separate-chaining (or linear-probing) hash table so that each insert/search takes O(1) time. Thus, the overall subroutine takes O(N ) time. To determine whether any 3 (or more lines) intersect at a point, run the previous subroutine N times, once for each line i. T ...
Midterm Solutions
... Implement the symbol table using a separate-chaining (or linear-probing) hash table so that each insert/search takes O(1) time. Thus, the overall subroutine takes O(N ) time. To determine whether any 3 (or more lines) intersect at a point, run the previous subroutine N times, once for each line i. T ...
... Implement the symbol table using a separate-chaining (or linear-probing) hash table so that each insert/search takes O(1) time. Thus, the overall subroutine takes O(N ) time. To determine whether any 3 (or more lines) intersect at a point, run the previous subroutine N times, once for each line i. T ...
Data structures and complexity
... to some power of the length of the input. Such problems are said to be solvable in polynomial time. (Of course if the power is 50, then the problem is practically ...
... to some power of the length of the input. Such problems are said to be solvable in polynomial time. (Of course if the power is 50, then the problem is practically ...
Engineering a Sorted List Data Structure for 32 Bit Keys
... eight bits in t2i contain the logical-or of 32 bits in t1i . This data structure is only allocated if |Mi | ≥ 2. The L3-top data structures tij with bit arrays t1ij [0..255] and t2ij [0..7] reflect the entries of Mij in a fashion analogous to the L2-top data structure. Hash Tables use open addressin ...
... eight bits in t2i contain the logical-or of 32 bits in t1i . This data structure is only allocated if |Mi | ≥ 2. The L3-top data structures tij with bit arrays t1ij [0..255] and t2ij [0..7] reflect the entries of Mij in a fashion analogous to the L2-top data structure. Hash Tables use open addressin ...
Lecture 15
... indexed by an integer range, usually from 0 to the table size minus one A has function turns the search key (identifier name) into an integer hash value in the index range, and the item corresponding to the search key is stored in the bucket at this index The has function should distribute the key i ...
... indexed by an integer range, usually from 0 to the table size minus one A has function turns the search key (identifier name) into an integer hash value in the index range, and the item corresponding to the search key is stored in the bucket at this index The has function should distribute the key i ...
ppt
... Time: If we use a universal hash function to determine the bucket index, then each bucket receives only a constant expected number of elements, so it takes O(1) expected amortized time to find an element in a bucket. The prefixfree codes we use allow O(1) decoding of any element. Space: The prefix-f ...
... Time: If we use a universal hash function to determine the bucket index, then each bucket receives only a constant expected number of elements, so it takes O(1) expected amortized time to find an element in a bucket. The prefixfree codes we use allow O(1) decoding of any element. Space: The prefix-f ...
authentication
... – System uses Kerberos to validate a user password. – Client obtains ticket for user. • Service immaterial, usually ticket granting service (TGS). • If authenticator successfully decrypted, password valid. • System erases ticket and session key. ...
... – System uses Kerberos to validate a user password. – Client obtains ticket for user. • Service immaterial, usually ticket granting service (TGS). • If authenticator successfully decrypted, password valid. • System erases ticket and session key. ...
Implementing ADTs
... picking the right one for the job is an important step in design "Get your data structures correct first, and the rest of the program will write itself." -Davids Johnson ...
... picking the right one for the job is an important step in design "Get your data structures correct first, and the rest of the program will write itself." -Davids Johnson ...
Chapter 3
... picking the right one for the job is an important step in design "Get your data structures correct first, and the rest of the program will write itself." -Davids Johnson ...
... picking the right one for the job is an important step in design "Get your data structures correct first, and the rest of the program will write itself." -Davids Johnson ...
Topics-Ch4Ch5
... • Insertion anomaly – Suppose we need to add a new employee to EMPLOYEE2. Since the primary key is (EmpID, Course), to insert a row both EmpID and Course must be supplied. This is an anomaly, because the user should be able to enter employee data without supplying Course data. • Deletion anomaly – S ...
... • Insertion anomaly – Suppose we need to add a new employee to EMPLOYEE2. Since the primary key is (EmpID, Course), to insert a row both EmpID and Course must be supplied. This is an anomaly, because the user should be able to enter employee data without supplying Course data. • Deletion anomaly – S ...
A Comparison of Adaptive Radix Trees and Hash Tables
... hashing. We provide different variants of the same hashing scheme because some variants are tuned for performance, while other are tuned for space efficiency. However, it is not our intention to compare ART against structures already considered (covered) in the original ART paper [1] again. Conseque ...
... hashing. We provide different variants of the same hashing scheme because some variants are tuned for performance, while other are tuned for space efficiency. However, it is not our intention to compare ART against structures already considered (covered) in the original ART paper [1] again. Conseque ...
Java Review
... while B.hasNext() do p = B.next() if p.element().key() = k then t = p.element().value() B.replace(p,(k,v)) return t {return the old value} S.insertLast((k,v)) n = n + 1 {increment variable storing number of entries} return null {there was no previous entry with key equal to k} ...
... while B.hasNext() do p = B.next() if p.element().key() = k then t = p.element().value() B.replace(p,(k,v)) return t {return the old value} S.insertLast((k,v)) n = n + 1 {increment variable storing number of entries} return null {there was no previous entry with key equal to k} ...
Data structures and performance analysis
... To simulate k different hash functions for String objects, you may append single characters to the end of your Strings before hashing (e.g. a, b, c, . . .). To determine if an element is not in the Bloom filter, check all of its hash indices. If any of them is zero, the element must not be in the Bl ...
... To simulate k different hash functions for String objects, you may append single characters to the end of your Strings before hashing (e.g. a, b, c, . . .). To determine if an element is not in the Bloom filter, check all of its hash indices. If any of them is zero, the element must not be in the Bl ...
SET
... integers is easy to order, but a set of points may be difficult or impossible to order. Most operations on list-based sets are more difficult to implement than for bit vectors, except I/O which is now pretty easy. Three basic operations can be defined that can assist with the implementation of most ...
... integers is easy to order, but a set of points may be difficult or impossible to order. Most operations on list-based sets are more difficult to implement than for bit vectors, except I/O which is now pretty easy. Three basic operations can be defined that can assist with the implementation of most ...
SET
... integers is easy to order, but a set of points may be difficult or impossible to order. Most operations on list-based sets are more difficult to implement than for bit vectors, except I/O which is now pretty easy. Three basic operations can be defined that can assist with the implementation of most ...
... integers is easy to order, but a set of points may be difficult or impossible to order. Most operations on list-based sets are more difficult to implement than for bit vectors, except I/O which is now pretty easy. Three basic operations can be defined that can assist with the implementation of most ...
Programming for GCSE - Teaching London Computing
... • Traverse the left subtree. • Visit the root. • Traverse the right subtree. ...
... • Traverse the left subtree. • Visit the root. • Traverse the right subtree. ...
Student Technologies Quick Start Guide
... student portal page. Please read the directions completely. After 3 bad attempts, you will be locked out for 30 minutes before being able to continue. Select your security questions and answers carefully. IMPORTANT: Passwords must be changed every 90 days or they expire and you will be locked out. I ...
... student portal page. Please read the directions completely. After 3 bad attempts, you will be locked out for 30 minutes before being able to continue. Select your security questions and answers carefully. IMPORTANT: Passwords must be changed every 90 days or they expire and you will be locked out. I ...
Rainbow table
A rainbow table is a precomputed table for reversing cryptographic hash functions, usually for cracking password hashes. Tables are usually used in recovering a plaintext password up to a certain length consisting of a limited set of characters. It is a practical example of a space/time trade-off, using less computer processing time and more storage than a brute-force attack which calculates a hash on every attempt, but more processing time and less storage than a simple lookup table with one entry per hash. Use of a key derivation function that employs a salt makes this attack infeasible.Rainbow tables are an application of an earlier, simpler algorithm by Martin Hellman.