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Indexing Structures for Files and Physical Database Design
Indexing Structures for Files and Physical Database Design

... The structure of the leaf nodes of a B+ tree of order p is: • 1. Each leaf node is of the form  <,,….,< ….,,Pnext>  where q<=p, each Pri is a data pointer and Pnext points to the next leaf node  of the B+ tree. • 2. Within each leaf node, K1<= K2<=Kq‐1 for q<=p • 3. Each  ...
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... Ignoring the recursive calls, emptying a buffer containing X items requires O(m + X/B) time. Under normal circumstances, we have X > M , so the total buffer-emptying time is O(X/B); if we amortize the cost of emptying a buffer against the X insertions that filled the buffer, the amortized time for a ...
Elementary Data Structures
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... if t = S.length − 1 then can replace the array with A ← new array of a larger one size … Š How large should the new for i ← 0 to t do array be? A[i] ← S[i] ...
Data Structure and Algorithm Analysis part 2
Data Structure and Algorithm Analysis part 2

... such as constants or variable names The other nodes contain operators. This particular tree happens to be binary, because all of the operations are binary It is possible for nodes to have more than two children. It is also possible for a node to have only one child, such as unary minus operator We c ...
6) R-tree: Typically the preferred method for indexing spatial data
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... 2. Z-order, Morton order, or Morton code is a function which maps multidimensional data to one dimension while preserving locality of the data points. It was introduced in 1966 by G. M. Morton. The z-value of a point in multidimensions is simply calculated by interleaving the binary representations ...
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Memory Optimization - UT Computer Science
Memory Optimization - UT Computer Science

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binary search tree

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fa10 - University of Illinois at Urbana
fa10 - University of Illinois at Urbana

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Advanced Data Structures - Department of Computer Science
Advanced Data Structures - Department of Computer Science

... [J.R. Driscoll, N. Sarnak, D.D. Sleator, R.E. Tarjan, Making Data Structures Persistent, Journal of Computer and System Sciences, 38(1), 86-124, 1989] ...
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... Stack (§1.5) In a push operation, when the array is full, instead of throwing an exception, we Algorithm push(o) if t = S.length − 1 then can replace the array with A ← new array of a larger one size … How large should the new for i ← 0 to t do array be? A[i] ← S[i] ...
< 1 ... 56 57 58 59 60 61 62 63 64 ... 76 >

Interval tree

In computer science, an interval tree is a tree data structure to hold intervals. Specifically, it allows one to efficiently find all intervals that overlap with any given interval or point. It is often used for windowing queries, for instance, to find all roads on a computerized map inside a rectangular viewport, or to find all visible elements inside a three-dimensional scene. A similar data structure is the segment tree.The trivial solution is to visit each interval and test whether it intersects the given point or interval, which requires O(n) time, where n is the number of intervals in the collection. Since a query may return all intervals, for example if the query is a large interval intersecting all intervals in the collection, this is asymptotically optimal; however, we can do better by considering output-sensitive algorithms, where the runtime is expressed in terms of m, the number of intervals produced by the query. Interval trees have a query time of O(log n + m) and an initial creation time of O(n log n), while limiting memory consumption to O(n). After creation, interval trees may be dynamic, allowing efficient insertion and deletion of an interval in O(log n). If the endpoints of intervals are within a small integer range (e.g., in the range [1,...,O(n)]), faster data structures exist with preprocessing time O(n) and query time O(1+m) for reporting m intervals containing a given query point.
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