P-ring: an efficient and robust P2P range index structure
... this goal by hashing. Items are assigned to peers based on the hash value of their search key. Such an assignment has been shown to be very close to a uniform distribution with high probability [23]. However, hashing destroys the value ordering among the search key values, and thus cannot be used to ...
... this goal by hashing. Items are assigned to peers based on the hash value of their search key. Such an assignment has been shown to be very close to a uniform distribution with high probability [23]. However, hashing destroys the value ordering among the search key values, and thus cannot be used to ...
Recursive Data Structure Profiling
... area. Thus, to identify the links, we need to track the flow of heapgenerated addresses as the program executes. There are at least two ways of tracking this by instrumenting the binary appropriately, as we will see in Section 4. We can construct a graph whose adjacency list representation is specif ...
... area. Thus, to identify the links, we need to track the flow of heapgenerated addresses as the program executes. There are at least two ways of tracking this by instrumenting the binary appropriately, as we will see in Section 4. We can construct a graph whose adjacency list representation is specif ...
GUITAR: Piecing Together Android App GUIs from Memory Images
... pottery (the GUI) from its unearthed fragments (data structures) [25]. To address this challenge, we present GUITAR1 , a system which automatically reconstructs app GUIs from Android phone memory images and redraws them as they originally appeared. Interestingly, GUITAR does not require app-specific ...
... pottery (the GUI) from its unearthed fragments (data structures) [25]. To address this challenge, we present GUITAR1 , a system which automatically reconstructs app GUIs from Android phone memory images and redraws them as they originally appeared. Interestingly, GUITAR does not require app-specific ...
20.96. The TextIO Monad Transformer (cont`d)
... 10.14. Architecture of the Implementation .................................................................... 11. 11 Generic Programming .................................................................................................. 11.1. Motivation .............................................. ...
... 10.14. Architecture of the Implementation .................................................................... 11. 11 Generic Programming .................................................................................................. 11.1. Motivation .............................................. ...
OpenVMS Distributed Lock Manager Performance
... Generates significant load on existing lock master, from which you may have been trying to off-load work. In some cases, node may thus be saturated and unable to initiate lock remastering Programs running locally on existing lock master can generate so many requests that tree won’t move because yo ...
... Generates significant load on existing lock master, from which you may have been trying to off-load work. In some cases, node may thus be saturated and unable to initiate lock remastering Programs running locally on existing lock master can generate so many requests that tree won’t move because yo ...
Universal Symbolic Execution and its Application to Likely Data
... 1. K RYSTAL assumes that there is a test harness and a set of test inputs for the given data structure. K RYSTAL performs a variant of symbolic execution, called universal symbolic execution, along each test execution path to compute a symbolic memory and a symbolic path condition set. 2. K RYSTAL u ...
... 1. K RYSTAL assumes that there is a test harness and a set of test inputs for the given data structure. K RYSTAL performs a variant of symbolic execution, called universal symbolic execution, along each test execution path to compute a symbolic memory and a symbolic path condition set. 2. K RYSTAL u ...
Universal Symbolic Execution and its Yamini Kannan Koushik Sen
... 1. K RYSTAL assumes that there is a test harness and a set of test inputs for the given data structure. K RYSTAL performs a variant of symbolic execution, called universal symbolic execution, along each test execution path to compute a symbolic memory and a symbolic path condition set. 2. K RYSTAL u ...
... 1. K RYSTAL assumes that there is a test harness and a set of test inputs for the given data structure. K RYSTAL performs a variant of symbolic execution, called universal symbolic execution, along each test execution path to compute a symbolic memory and a symbolic path condition set. 2. K RYSTAL u ...
Data Structures and Algorithms in Java - Go Green
... 800 East 96th Street, Indianapolis, Indiana 46240 ...
... 800 East 96th Street, Indianapolis, Indiana 46240 ...
Fast Local Searches and Updates in Bounded Universes
... An x-fast trie on U is a binary tree whose leaves are elements of U and whose internal nodes represent prefixes of these leaves. The height of the tree is Θ(log U ). At any internal node, moving to the left child appends a 0 to the prefix and moving to the right child appends a 1. The prefix at the ...
... An x-fast trie on U is a binary tree whose leaves are elements of U and whose internal nodes represent prefixes of these leaves. The height of the tree is Θ(log U ). At any internal node, moving to the left child appends a 0 to the prefix and moving to the right child appends a 1. The prefix at the ...
Implementing Database Operations Using SIMD
... same time on multiple data items. This is especially productive for applications that process large arrays of numeric values, a typical characteristic of multimedia applications. SIMD technology comes in various flavors on a number of architectures, including “MMX,” “SSE,” and “SSE2” on Intel machin ...
... same time on multiple data items. This is especially productive for applications that process large arrays of numeric values, a typical characteristic of multimedia applications. SIMD technology comes in various flavors on a number of architectures, including “MMX,” “SSE,” and “SSE2” on Intel machin ...
Towards Optimal Range Medians - Department of Computer Science
... Our algorithm is based on the following key observation (see also Figure 1): Suppose we partition the elements in array A of length n into two smaller arrays: A.low which contains all elements with the n/2 smallest6 values in A, and A.high which contains all elements with the n/2 largest values. Th ...
... Our algorithm is based on the following key observation (see also Figure 1): Suppose we partition the elements in array A of length n into two smaller arrays: A.low which contains all elements with the n/2 smallest6 values in A, and A.high which contains all elements with the n/2 largest values. Th ...
Lars Arge
... – Make R-tree by inserting 20.000 rectangles – Delete the first inserted 10.000 and insert them again • Search time improvement of 20-50% ...
... – Make R-tree by inserting 20.000 rectangles – Delete the first inserted 10.000 and insert them again • Search time improvement of 20-50% ...
Algorithms and Data Structures for Games Programming
... (STL)) provides very efficient implementations of the most common data structures and algorithms. When I’m using Standard Library (STL) features on something I’m not too sure about, I have (Josuttis 1999), (Reese 2007), (Meyers 2001) and (Stroustrup 1997) by my right hand. For both STL and C++, (Str ...
... (STL)) provides very efficient implementations of the most common data structures and algorithms. When I’m using Standard Library (STL) features on something I’m not too sure about, I have (Josuttis 1999), (Reese 2007), (Meyers 2001) and (Stroustrup 1997) by my right hand. For both STL and C++, (Str ...
TNamed - Root
... Subsequent .x mymacro.C+(42) check for changes, only rebuild if needed Exactly as fast as e.g. Makefile based stand-alone binary! CINT knows types, functions in the file, e.g. call ...
... Subsequent .x mymacro.C+(42) check for changes, only rebuild if needed Exactly as fast as e.g. Makefile based stand-alone binary! CINT knows types, functions in the file, e.g. call ...
Oblivious Data Structures - Cryptology ePrint Archive
... to hide what fraction of memory is committed, each memory allocation operation needs to scan through O(N ) memory. Our oblivious memory allocator algorithm can be adopted on ORAM-capable secure processor [14, 31, 35] for allocation of oblivious memory. 3. Graph algorithms. We achieve asymptotic imp ...
... to hide what fraction of memory is committed, each memory allocation operation needs to scan through O(N ) memory. Our oblivious memory allocator algorithm can be adopted on ORAM-capable secure processor [14, 31, 35] for allocation of oblivious memory. 3. Graph algorithms. We achieve asymptotic imp ...
2nd Workshop on Algorithm Engineering WAE`98 { Proceedings
... elements in the same workpiece. They are the reason why the problem is non-trivial, since we have no knowledge whether an edge is neighboured to one, several or no other edges (Figure 8). Consider two edges that are situated next to each other. The gap between them may have been intended by the engi ...
... elements in the same workpiece. They are the reason why the problem is non-trivial, since we have no knowledge whether an edge is neighboured to one, several or no other edges (Figure 8). Consider two edges that are situated next to each other. The gap between them may have been intended by the engi ...
Binary search tree
In computer science, binary search trees (BST), sometimes called ordered or sorted binary trees, are a particular type of containers: data structures that store ""items"" (such as numbers, names and etc.) in memory. They allow fast lookup, addition and removal of items, and can be used to implement either dynamic sets of items, or lookup tables that allow finding an item by its key (e.g., finding the phone number of a person by name).Binary search trees keep their keys in sorted order, so that lookup and other operations can use the principle of binary search: when looking for a key in a tree (or a place to insert a new key), they traverse the tree from root to leaf, making comparisons to keys stored in the nodes of the tree and deciding, based on the comparison, to continue searching in the left or right subtrees. On average, this means that each comparison allows the operations to skip about half of the tree, so that each lookup, insertion or deletion takes time proportional to the logarithm of the number of items stored in the tree. This is much better than the linear time required to find items by key in an (unsorted) array, but slower than the corresponding operations on hash tables.They are a special case of the more general B-tree with order equal to two.