一种最大向量平均个数的估计方法

提出一种估计n个d维向量中最大向量平均个数的方法。该方法通过分析单个向量与其他向量子集的支配关系,求出最大向量平均个数的解析式。证明解析式满足已知的递归关系,得到最大向量平均个数的近似估计。与已有方法相比,该方法可应用到估计k个其他向量支配的平均个数问题。     

Skyline and mapping aware join query evaluation

Growing interests in multi-criteria decision support applications have resulted in a flurry of efficient skyline algorithms. In practice, real-world decision support applications require to access data from disparate sources. Existing techniques define the skyline operation to work on a single set, and therefore, treat skylines as an “add-on” on top of a traditional Select-Project-Join query plan. In many real-world applications, the skyline dimensions can be anti-correlated such as the attribute pair {price, mileage} for cars and {price, distance} for hotels. Anti-correlated data are particularly challenging for skyline evaluation and therefore have commonly been ignored by existing techniques. In this work, we propose a robust execution framework called SKIN to evaluate skyline over joins. The salient features of SKIN are: (a) effective in reducing the two primary costs, namely the cost of generating the join results and the cost of dominance comparisons to compute the final skyline of join results, (b) shown to be robust for both skyline-friendly (independent and correlated) as well as skyline-unfriendly (anti-correlated) data distributions. SKIN is effective in exploiting the skyline knowledge in both local within individual data sources and across disparate sources—to significantly reduce the above-mentioned costs incurred during the evaluation of skyline over join. Our experimental study demonstrates the superiority of our proposed approach over state-of-the-art techniques to handle a wide variety of data distributions.     

基于动态扫描理论的3D空间轮廓查询算法

轮廓操作和轮廓计算在数据库通信、决策支持、数据可视化以及空间数据库等应用中具有重要作用。该文分析现有方法,指出其在渐进处理、查询高效性和用户后期筛选方便性等方面存在的问题,提出基于轮廓点支配定理、应用空间几何原理的3D动态割面轮廓查询算法。通过实例分析和实验证明其可行性、高效性和准确性。     

XML 数据流上Top-K 关键字查询处理

利用关键字可以在模式未知的情况下对XML数据进行查询.在当前的XML数据流上的关键字查询处理中,打分函数往往不能都满足各种用户不同的需求.提出了一种基于skyline的XML数据流上的Top-K关键字查询.对于这种查询,不需要考虑影响结果与查询相关性的复杂因素,只需利用skyline挑选与查询最相关的结果.提出了两种XML数据流上的有效的基于skyline的Top-K关键查询处理算法,包括对单查询和多查询的处理算法.通过扩展实验对两种算法的有效性和可扩展性进行了验证.经过实验验证,所提出的查询处理算法的效率几乎不受关键字个数、查询结果数量、查询数量等参数的影响,运行时间和文档大小大致呈线性关系.     

n-of-N 数据流模型上高效概率Skyline 计算

研究概率数据流上的q-skyline计算问题.与只支持滑动窗口数据流模型的已有方法相比,所提出的方法能够支持更为通用的n-of-N数据流模型.采用将q-skyline查询转换为区间树上刺入查询的方法支持n-of-N数据流模型.提出PnNM算法维护支持n-of-N数据流模型所需的相关数据结构,高效处理了不确定对象候选集合更新和区间更新等维护工作;提出PnNCont算法实现连续查询处理.理论分析和实验结果表明,算法能够有效地支持概率数据流n-of-N模型上的q-skyline查询处理.     

层次立方体(H-Cube)的自适应计算

多维数据立方（Cube）的计算对联机分析处理（online analytical processing，OLAP）有着极为重要的作用。针对传统的多维数据Cube计算中的不足，提出了自适应的计算方法，并给出了带有层次的Cube计算算法HCA算法。     

滨湖住区形态研究与实践

深入分析了目前城市滨湖地区发展中存在的主要问题,结合岳阳中航·翡翠湾规划设计实例,提出了一种基于国情较为合理的滨湖住区形态,改善了人居环境,使滨湖景观更加完美。     

Efficient and scalable continuous skyline monitoring in two-tier streaming settings

Two-tier streaming settings are a typical dynamic environment where continuous skylines represent an important semantic indicator for multiple attributes. To monitor skylines over the dynamic data in such settings, one needs to continuously update the skyline query results in order to reflect the new data values. This paper tackles the problem of continuous skyline monitoring on a central query server over dynamic data from multiple data sites. Simply sending the updates of tuple values to the server is cost-prohibitive. Therefore, we propose an approach that allows the central server to collaborate with the data sites to monitor the possible skyline changes. By doing so, the processing load is distributed over all the data sites instead of only on the central server. Furthermore, this collaborative approach minimizes the bandwidth consumption between the server and the data sites, which is often critical in a widely distributed environment such as a wide-area sensor network. We give theoretical upper bounds for the computation costs and communication costs of the proposed collaborative approach. We also conduct extensive experiments on both synthetic and real data sets. The experimental results demonstrate that our collaborative approach is efficient, scalable and well-balanced in terms of communication costs and computation costs.     

Efficient processing of probabilistic group subspace skyline queries in uncertain databases

Due to the pervasive data uncertainty in many real applications, efficient and effective query answering on uncertain data has recently gained much attention from the database community. In this paper, we propose a novel and important query in the context of uncertain databases, namely probabilistic group subspace skyline (PGSS) query, which is useful in applications like sensor data analysis. Specifically, a PGSS query retrieves those uncertain objects that are, with high confidence, not dynamically dominated by other objects, with respect to a group of query points in ad-hoc subspaces. In order to enable fast PGSS query answering, we propose effective pruning methods to reduce the PGSS search space, which are seamlessly integrated into an efficient PGSS query procedure. Furthermore, to achieve low query cost, we provide a cost model, in light of which uncertain data are pre-processed and indexed. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our proposed approaches.