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关键词： 数据结构   教学方法   教学改革   实践教学  

摘要： 《数据结构》课程的基础性决定其成为计算机专业的必修课程，其所讨论的知识内容、提倡的技术方法和思路，对进一步学习计算机专业后续课程建立了知识平台。本文讨论了传统的数据结构的教学方法中存在的问题，并提出教学改革方案，更加注重在高职院校的专业基础课教学中体现出实践性。实践表明，该教学方法对于培养高职学生的逻辑思维、数据抽象能力、算法设计和分析能力有较好的效果。

关键词： 图处理   云计算   分布式   数据建模   存储   数据结构  

摘要： 随着社交网络和语义Web等数据应用的兴起,催生了许多图数据处理产品,包括Neo4j,Hyper Graph DB等,然而这些产品在设计时并未充分考虑图应用对数据可用性和可扩展性的更高要求。为此,提出一种基于分布式内存云的图引擎底层建模和存储解决方案。在内存云上搭建分布式键值引擎,进而在键值存储的基础上对图的数据进行建模和读写。在大规模数据集上的实验结果表明,该方案具有较好的图随机访问性能,并能够高效地支持海量规模的图数据应用。

关键词： 航空通信   数据结构   脆弱点   检查模型  

摘要： 对航空通信数据结构进行鲁棒性检测,找到脆弱点对航空安全至关重要。航空通信数据与传统的通信数据不同,包含大量和飞行时产生的随机数据。由于通信数据结构处在不断变化中。传统的数据结构脆弱点检测中,以固定数据结构为前提,没有考虑航空数据结构实时变化造成的的干扰,存在严重的遮蔽效应,降低了航空通信数据结构脆弱点检测方法的准确性。提出了一种依据多维熵值航空通信数据结构的脆弱点检测模型,采用Filter-ary-Sketch及时存储航空通信数据结构信息,间隔一定周期进行基于多维熵值的航空通信数据结构脆弱点检测,若发现异常,按照Filter-ary-Sketch保存的通信数据结构信息定位脆弱点,检测出一个脆弱点,立即进行修正,再进行下一个脆弱点的检测,避免出现遮蔽效应,实现航空通信数据结构脆弱点的准确检测。实验结果说明,对比传统检测模型,所提模型具有较高的检测效率和精度,取得了较好的效果。

关键词： Simplicial complexes   Data structure   Computational topology   Flag complexes   Witness complexes  

摘要： This paper introduces a new data structure, called simplex tree, to represent abstract simplicial complexes of any dimension. All faces of the simplicial complex are explicitly stored in a trie whose nodes are in bijection with the faces of the complex. This data structure allows to efficiently implement a large range of basic operations on simplicial complexes. We provide theoretical complexity analysis as well as detailed experimental results. We more specifically study Rips and witness complexes.

关键词： 数据结构  

ISBN: (纸本)9787562535522

摘要： 本书是高等学校数据结构教学参考资料。主要内容包括：绪论、线性表、堆栈和队列、树和二叉树、图、查找、内部排序、数据结构课程设计、数据结构试题等。

关键词： Algorithms   Verification   Z   refinement   concurrent access   linearizability   nonatomic refinement   theorem proving   KIV  

摘要： Efficient implementations of data structures such as queues, stacks or hash-tables allow for concurrent access by many processes at the same time. To increase concurrency, these algorithms often completely dispose with locking, or only lock small parts of the structure. Linearizability is the standard correctness criterion for such a scenario-where a concurrent object is linearizable if all of its operations appear to take effect instantaneously some time between their invocation and return. The potential concurrent access to the shared data structure tremendously increases the complexity of the verification problem, and thus current proof techniques for showing linearizability are all tailored to specific types of data structures. In previous work, we have shown how simulation-based proof conditions for linearizability can be used to verify a number of subtle concurrent algorithms. In this article, we now show that conditions based on backward simulation can be used to show linearizability of every linearizable algorithm, that is, we show that our proof technique is both sound and complete. We exemplify our approach by a linearizability proof of a concurrent queue, introduced in Herlihy and Wing's landmark paper on linearizability. Except for their manual proof, none of the numerous other approaches have successfully treated this queue. Our approach is supported by a full mechanisation: both the linearizability proofs for case studies like the queue, and the proofs of soundness and completeness have been carried out with an interactive prover, which is KIV.

关键词： succinct data structures   binary sequences   FM-index   algorithm engineering   massive data sets   rank   select   SSE   hugepages  

摘要： Succinct data structures provide the same functionality as their corresponding traditional data structure in compact space. We improve on functions rank and select, which are the basic building blocks of FM-indexes and other succinct data structures. First, we present a cache-optimal, uncompressed bitvector representation that outperforms all existing approaches. Next, we improve, in both space and time, on a recent result by Navarro and Providel on compressed bitvectors. Last, we show techniques to perform rank and select on 64-bit words that are up to three times faster than existing methods. In our experimental evaluation, we first show how our improvements affect cache and runtime performance of both operations on data sets larger than commonly used in the evaluation of succinct data structures. Our experiments show that our improvements to these basic operations significantly improve the runtime performance and compression effectiveness of FM-indexes on small and large data sets. To our knowledge, our improvements result in FM-indexes that are either smaller or faster than all current state of the art implementations. Copyright (C) 2013 John Wiley & Sons, Ltd.

关键词： Mode   Range query   Data structure   Linear space   Array  

摘要： A mode of a multiset S is an element aaS of maximum multiplicity;that is, a occurs at least as frequently as any other element in S. Given an array A[1:n] of n elements, we consider a basic problem: constructing a static data structure that efficiently answers range mode queries on A. Each query consists of an input pair of indices (i,j) for which a mode of A[i:j] must be returned. The best previous data structure with linear space, by Krizanc, Morin, and Smid (Proceedings of the International Symposium on Algorithms and Computation (ISAAC), pp. 517-526, 2003), requires query time in the worst case. We improve their result and present an O(n)-space data structure that supports range mode queries in worst-case time. In the external memory model, we give a linear-space data structure that requires I/Os per query, where B denotes the block size. Furthermore, we present strong evidence that a query time significantly below cannot be achieved by purely combinatorial techniques;we show that boolean matrix multiplication of two matrices reduces to n range mode queries in an array of size O(n). Additionally, we give linear-space data structures for the dynamic problem (queries and updates in near O(n (3/4)) time), for orthogonal range mode in d dimensions (queries in near O(n (1-1/2d) ) time) and for half-space range mode in d dimensions (queries in time). Finally, we complement our dynamic data structure with a reduction from the multiphase problem, again supporting that we cannot hope for much more efficient data structures.