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Balloon: A Forward-Secure Append-Only Persistent Authenticated
Balloon: A Forward-Secure Append-Only Persistent Authenticated

... to make data processing by service providers transparent to data subjects whose personal data are being processed. Balloon can also be used as part of a secure logging system, similar to the history tree system by Crosby and Wallach [6]. Another closely related application is as an extension to Cert ...
Linked Lists
Linked Lists

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◦ § 5.19 9.11

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Creating Common Information Structures Using Lists Stored in SAS® DATA Step HASH Objects

... Since we are going to be storing our data in hash tables we have to make our keys, in an individual list, either numeric or character type. All the examples we will cover in this paper will have numeric typed keys. This is primarily because it makes sorting keys straightforward and simplifies the lo ...
Assignment 1 Solutions - McGill School Of Computer Science
Assignment 1 Solutions - McGill School Of Computer Science

Stratified B-trees and Versioned Dictionaries.
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... independently at each level, rather than through a large atomic section. This, in turn, allows the recursive cleaning moundify of one extractMin to run concurrently with another extractMin. The lock-free moundify operation retains the obligation to clear any dirty bit that it sets. However, since th ...
Creating common information structures using list's stored in data step hash objects
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Open Data Structures (in C++)

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RRRpdfauthor=TTTpdfsubject=YYYpdfkeywords=Clemson

... theoretical efficiency of modern cache-oblivious data structures by using classic data structures such as weight-balanced trees or height balanced trees (e.g. AVL trees). We preserve the original topology and algorithms of the given balanced search tree using a simple post-processing with guaranteed ...
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Persistent Data Structures

... the new modification and label it with the time stamp t. To query a specific version of the data structure at a particular time, we then only have to do a single binary search, after which we can access the data structure at that time. This requires O(n) extra space and time for each modification, h ...
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Worksheet 33: Heaps and Priority Queues

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Part 1 - Anna University

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Open Data Structures (in Java)

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HERMES: A Trajectory DB Engine for Mobility
HERMES: A Trajectory DB Engine for Mobility

Range Majority in Constant Time and Linear Space
Range Majority in Constant Time and Linear Space

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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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