P2P网络中规模估计方法的研究

P2P系统经常需要分布式方法来估计系统中具有某种特征的节点数量,即规模估计。研究了基于抽样理论的规模估方法,该方法具有较好的健壮性和可扩展性。针对P2P应用,对两个基于抽样理论的规模估计算法进行了改进,分别是基于抽样冲突和基于样本分布算法。实验结果指出改进算法牺牲少量的精度而大大减小运行开销。并首次指出当总采样量不变时,基于样本分布的规模估计方法更适合采用“单次大样本”的策略。     

基于多次采样的规模估计算法

计算机网络呈现出动态、大规模和自主组织的特征,使用分布式方法估计具有某种特征的节点数量是网络领域的重要问题。该文研究了基于抽样理论的规模估计方法,该方法具有良好的可扩展性,可以较好地应用在非结构网络环境中。分析基于采样冲突和基于二项式分布的2个算法,实验结果表明基于采样冲突算法的开销小、精度高。当总采样量不变时,基于分布的估算方法采用大样本比小样本策略的估计精度要高。     

非结构化P2P网络资源搜索改进算法

为了改善非结构化对等网络(peer-to-peer,P2P)资源搜索的网络负载大、搜索时间长的缺点。对现有P2P网络资源搜索算法进行了研究,在此基础上,提出一种基于索引表的跳跃式算法,该算法中每一个节点存有一定数量邻居节点的资源索引,节点利用资源索引表以跳跃方式查询节点,网络中的某些节点需要查询资源索引表,而某些节点无需查询资源索引表,直接转发查询消息即可。通过OPNET进行仿真实验表明,该算法能有效的减少网络负载和搜索延时,提高了搜索成功率。     

基于节点兴趣和Q-learning的P2P网络搜索机制

将智能手机设备加入基于非结构化P2P网络的资源共享系统中能够满足人们对资源共享的多样化、便利性、高频性、实时性、高效性等要求,但是该系统网络规模的扩张和网络节点互异性的加大,必将导致系统资源搜索效率的降低、冗余信息的剧增以及网络更加不稳定。为了解决这些问题,文中设计了一种改进的基于节点兴趣和Q-learning的资源搜索机制。首先将节点根据兴趣相似度进行兴趣聚类,划分兴趣集,然后根据兴趣集中节点的能力值构建兴趣树,该结构避免了消息环路的产生,极大地降低了冗余信息;在资源搜索中,兴趣树内采用洪泛算法转发消息,兴趣树之间采用基于Q-learning的消息转发机制,不断强化最可能获取目标资源的路径,查询消息优先在这些路径上传播。另外,针对“热点”资源问题,设计了自适应热点资源索引机制,减少了重复路径搜索,进一步减少了冗余消息量;针对节点失效的问题,给出了根节点冗余机制和捎带检测的策略方法,分别解决了根节点失效和普通节点失效导致的兴趣树的不完整性问题,分析表明该方法能够减少消息冗余量。仿真实验结果表明,与GBI-BI算法和Interest CN算法相比,所提搜索算法能够提高命中率,缩短响应时间,减少冗余信息,具有较好的综合性能,最终解决了由于智能手机设备加入P2P网络导致的资源搜索效率下降、网络流量开销大的问题。     

一种自组织资源发现模型和算法

针对具有动态性和不稳定性资源的网格计算环境的资源发现问题,提出一种基于资源索引节点的自组织资源发现模型,该模型采用了基于组的分层资源组织方式,通过信息节点管理组内资源信息,所有信息节点形成树型覆盖网络,可以在信息节点树型覆盖网络实现分布式资源定位.并提出以资源索引节点索引所有信息节点中资源的关键属性,设计了基于资源索引节点的智能资源发现算法,实验结果表明,该算法在系统负栽变化情况下,能保持稳定的性能,相比集中式资源发现算法、结构化P2P资源发现算法和分布式资源算法性能更优.     

基于历史数据的软件缺陷估计方法改进

针对捕获-再捕获模型的估计器普遍存在低估的现象,提出一种基于历史数据的缺陷估计改进方法,在基于已有研究的部分数据进行虚拟评审后,对比了改进前后估计器的缺陷估计效果,结果表明,改进后的估计器普遍较改进前的估计器的估计精度要高。     

一种基于MCB的自适应和声搜索定位算法

针对无线传感器网络锚节点稀疏条件下节点定位中存在的翻转现象和定位精度问题,提出了一种基于MCB的自适应和声搜索定位算法。通过引入MCB算法中的采样思想,随机产生网络拓扑约束下的未知节点的坐标,引入自适应的和声保留概率和音调调节概率,达到提高搜索能力和定位精度目的。仿真结果表明：算法能有效解决翻转现象,提高定位精度,提出的算法在定位精度和计算量方面优于对比算法。     

IS-P2P:一种基于索引的结构化P2P网络模型

在分析无结构与有结构P2P网络结构的基础上,提出了一种新的基于索引的有结构P2P网络模型IS-P2P（Index-based Structured P2P Networks）.IS-P2P网络采用两层混合结构,上层由比较稳定的索引节点组成有结构索引网络,使用文档路由搜索机制,提供资源的发布和查找功能.下层由普通节点组成分布式网络.IS-P2P模型充分利用P2P网络中节点的性能差异,具有高效的查找性能,且能适应P2P网络高度动态性.进一步计算IS-P2P模型中索引网络路由性能、查询处理速度、索引节点索引数据库大小以及索引节点转发查询消息代价表明,IS-P2P具有良好的性能.     

对基于风险测试模型的扩充与改进

基于风险的测试策略被认为是促进资源优化配置,提高测试有效性的行之有效的测试策略。鉴于已有的基于风险测试模型存在的不足,McCabe的圈复杂性度量被引入进来改进风险因素度量指标,以期建立更客观合理的度量模型。同时,捕获—再捕获方法被应用于一轮测试后模块中剩余缺陷数的估算,这使得一直以来在风险测试策略中,要求根据已有测试信息动态调整测试力量的想法,有了具体的实现手段。     

RSSN:一种基于漫步采样的超节点对等网络

超节点对等网的引入,有效解决了网络节点异构性所带来的低性能节点对于文件定位效率低的问题。但是传统超节点对等网构建效率低,不能适应目前高度动态的网络环境。提出一种高效可靠的超节点对等网RSSN,RSSN通过漫步算法对网络叶节点采样,从采样集合中选出高性能节点建立预备超节点,通过判断网络需求调整超节点层,并利用预备超节点备份文件索引信息,提高对等网的稳定性。仿真实验表明,相较Gnutella0.6超节点对等网,RSSN能够有效地提高对等网中超节点的平均性能和利用率,并能适应高动态的网络环境。     

Algorithms for multidimensional partitioning of static files

The problem of multidimensional file partitioning (MDFP) arises in large databases that are subject to frequent range queries on one or more attributes. In an MDFP scheme, the search attribute space is partitioned into cells, which are mapped to physical disk locations. This mapping preserves the order of the search attribute values so that range queries can be answered most efficiently, while maintaining good performance for other types of queries. Recently, MDFP schemes have been suggested to include both dynamic and static file organizations. Optimal and heuristic MDFP algorithms are developed for the static case. The results of extensive computational experiments show that the proposed heuristics perform better than known static ones. It is also shown that incorporating a static algorithm into a dynamic MDFP such as a grid file at conversion and/or periodical reorganization points significantly improves the resulting storage utilization of the data file and decreases the size of the directory file     

Optimization and evaluation of shortest path queries

We investigate the problem of how to evaluate efficiently a collection of shortest path queries on massive graphs that are too big to fit in the main memory. To evaluate a shortest path query efficiently, we introduce two pruning algorithms. These algorithms differ on the extent of materialization of shortest path cost and on how the search space is pruned. By grouping shortest path queries properly, batch processing improves the performance of shortest path query evaluation. Extensive study is also done on fragment sizes, cache sizes and query types that we show that affect the performance of a disk-based shortest path algorithm. The performance and scalability of proposed techniques are evaluated with large road systems in the Eastern United States. To demonstrate that the proposed disk-based algorithms are viable, we show that their search times are significant better than that of main-memory Dijkstra's algorithm.     

Searching the Web with Queries

In this paper we study the problem of searching the Web with online learning algorithms. We consider that Web documents can be represented by vectors of n boolean attributes. A search engine is viewed as a learner, and a user is viewed as a teacher. We investigate the number of queries a search engine needs from the user to search for a collection of Web documents. We design several efficient learning algorithms to search for any collection of documents represented by a disjunction (or a conjunction) of relevant attributes with the help of membership queries or equivalence queries.     

为物化视图选择构造搜索空间的新策略IMVPP

数据仓库中物化视图选择算法的代价与搜索空间的尺寸紧密相关。提出了一种基于输入查询的公共子表达式的候选视图搜索空间构造方法IMVPP,利用算法1计算出的公共子表达式,能被其他查询共享,并可对输入查询进行重写,有利于缩减视图搜索空间,提高查询效率。理论分析与实验结果表明,此方法是有效、可行的。     

Performance analysis of partial-match search algorithms for BD trees

Database systems are becoming increasingly popular for answering queries. Partial-match search queries are an important class of queries in such a system. Several storage structures have been proposed to answer these queries efficiently. The BD tree is an example of such a storage structure. A previous study indicated that the k-d tree performance is better than that of the BD tree for partial-match search queries. A recent paper reported some improved algorithms. However, it is unclear whether the improved algorithms show the BD tree in a favourable light for partial-match search queries. This paper explores the performance of these algorithms and compares their performance to that of the k-d tree. Since the BD tree construction process uses some heuristics to make it a better balanced tree, this paper also evaluates the effect of these heuristics on the partial-match search algorithms. The major conclusions of this study are that the BD tree performance for partial-match search is better than that of the k-d tree when an improved algorithm is used for partial-match search, and only the DZ expression rearrangement heuristic has substantial effect on partial-match search performance.     

Deep learning the semantics of change sequences for query expansion

The overexpansion problem negatively affects the quality of query expansion. To improve the quality of queries for searching code, this paper proposed a DBN-based algorithm for effective query expansion. The deep belief network (DBN) model is trained on the code sequences and their change sequences, which aims to capture the meaningful terms during the evolution of source code. In contrast to previous studies, the proposed model not only extracts relevant terms to expand a query but also excludes irrelevant terms from the query. It addresses two problems in query expansion, including the overexpansion of the original query and the negative influence of the changed terms in the target source code. Experiments on both artificial queries and real queries show that the proposed algorithm outperforms several query expansion algorithms for code search.     

On processing continuous frequent K-N-match queries for dynamic data over networked data sources

Similarity search is one of the critical issues in many applications. When using all attributes of objects to determine their similarity, most prior similarity search algorithms are easily influenced by a few attributes with high dissimilarity. The frequent k-n-match query is proposed to overcome the above problem. However, the prior algorithm to process frequent k-n-match queries is designed for static data, whose attributes are fixed, and is not suitable for dynamic data. Thus, we propose in this paper two schemes to process continuous frequent k-n-match queries over dynamic data. First, the concept of safe region is proposed and four formulae are devised to compute safe regions. Then, scheme CFKNMatchAD-C is developed to speed up the process of continuous frequent k-n-match queries by utilizing safe regions to avoid unnecessary query re-evaluations. To reduce the amount of data transmitted by networked data sources, scheme CFKNMatchAD-C also uses safe regions to eliminate transmissions of unnecessary data updates which will not affect the results of queries. Moreover, for large-scale environments, we further propose scheme CFKNMatchAD-D by extending scheme CFKMatchAD-C to employ multiple servers to process continuous frequent k-n-match queries. Experimental results show that scheme CFKNMatchAD-C and scheme CFKNMatchAD-D outperform the prior algorithm in terms of average response time and the amount of produced network traffic.     

AN INTERACTIVE SEARCH ASSISTANT ARCHITECTURE BASED ON INTRINSIC QUERY STREAM CHARACTERISTICS

Search engine query log mining has evolved over time to more like data stream mining due to the endless and continuous sequence of queries known as query stream. In this paper, we propose an online frequent sequence discovery (OFSD) algorithm to extract frequent phrases from within query streams, based on a new frequency rate metric, which is suitable for query stream mining. OFSD is an online, single pass, and real-time frequent sequence miner appropriate for data streams. The frequent phrases extracted by the OFSD algorithm are used to guide novice Web search engine users to complete their search queries more efficiently. YourEye, our online phrase recommender is then introduced. The advantages of YourEye compared with Google Suggest, a service powered by Google for phrase suggestion, is also described. Various characteristics of two specific Web search engine query logs are analyzed and then the query logs are used to evaluate YourEye. The experimental results confirm the significant benefit of monitoring frequent phrases within the queries instead of the whole queries because none-separable items. The number of the monitored elements substantially decreases, which results in smaller memory consumption as well as better performance. Re-ranking the retrieved pages based on past users clicks for each frequent phrase extracted by OFSD is also introduced. The preliminary results show the advantages of the proposed method compared to the similar work reported in Smyth et al.     

Performance analysis of parallel query processing algorithms forobject-oriented databases

Two types of parallel processing and optimization algorithms for processing object-oriented databases are the hybrid-hash pointer-based (HHP) algorithms and multi-wavefront (MWF) algorithms. We analyze these two algorithms and develop analytical formulas to capture their main performance features. We study their performance in three application environments, characterized by large databases having many object classes, each of which, respectively, (1) contains a large number of instances; (2) contains a relatively small number of instances; and (3) is of varying size. A horizontal data partitioning strategy is used in (1). A class-per-node assignment strategy is used in (2). In (3), object classes are partitioned horizontally and assigned to a varying number of processors depending on their different sizes. The MWF algorithm has three distinguishing features which contribute to its better performance: (a) a two-phase processing strategy, (b) vertical partitioning of horizontal segments, and (c) dynamic determination of the collision point in MWF propagations, which results in an optimized query execution plan. If these features are adopted by an HHP algorithm, its performance is comparable with that of the MWF algorithm because the difference in CPU time between them is negligible. The computing environment is a network of workstations having a shared-nothing architecture. The schema and some queries selected from the OO7 benchmark are used in the performance analyses and comparisons. The queries are modified slightly in different data environments in order to reflect the features of diverse database applications