一种使用简化预排序的k-支配Skyline查询算法

近年来,Skyline查询在多目标决策、数据挖掘、数据库可视化等方面得到广泛应用.然而在高维空间环境下,skyline查询因为返回的结果集过大而不能提供有用的信息.因此,学术界提出了七-支配skyline查询的概念.它通过弱化数据点之间的支配关系,使数据点间更容易产生支配关系,从而使结果集的大小保持在一个合适的范围内.现有七-支配skyline查询算法分为建立索引和不建立索引两种类型.其中不建立索引的算法在高维空间,反相关数据和渐近输出等方面表现比较差,而基于索引的算法花费大量时间去建立索引,整体性能都不高.本文提出一种基于简化预排序的七-支配skyline查询算法(SPA),实现用O(n)的时间复杂度对数据进行简化预排序.理论论证和实验数据都显示了SPA算法远比国内外现有的最好算法更加高效.     

一种基于索引的高效k-支配Skyline算法

由于在多标准决策支持等应用上具有重要的意义,skyline查询成为近年来数据库和数据挖掘领域的一个研究热点.然而随着数据集维数的增加,数据点之间形成支配关系的可能性越来越小,导致了skyline点数目过多而无法提供任何有效信息.为了在高维数据集中找到更重要和更有意义的skyline点,人们提出了k-支配skyline的定义.但现有的用于k-支配skyline的算法在时间效率、空间复杂度和渐进输出性上都有待提高.该文提出了一种基于索引的高效k-支配skyline算法,通过为数据集建立两个索引,算法可以高效地进行计算,在时间、空间和渐进性上均优于现有的算法.     

一种面向不完整数据流上的k-支配skyline查询算法

skyline查询是数据挖掘一个重要的研究方向,在基于数据的决策支持等应用中有着重要的作用.由于现实应用中存在着大量的不完整数据流,但大多数现有的skyline查询算法都依赖于如下的假设:1)任意数据点的所有维度值都是已知的;2)数据集是稳定、有界的并且可以随意访问.此外,随着数据维度的增加,skyline数据点的个数会变得过多,因此引入了k-支配skyline的概念,但是不完整数据的k-支配关系并不具有传递性,现有的skyline查询算法都无法适用.基于这些问题,考虑到数据流高维、无界、顺序性的特点,并且在某些维度上可能具有缺失值的特性,提出了一种新的基于滑动窗口的不完整数据流的k-支配skyline查询算法,实验结果表明,算法不仅可以支持不完整数据流上的k-支配skyline计算,并能够保证效率和性能.     

一种指数平滑预测的参数优化方法及实现

时间序列预测法在各种基于时态数据库的计算中有着广泛的应用前景.文中介绍了时间序列预测法中的单指数平滑、双指数平滑和三指数平滑三种指数平滑预测方法,不同的预测方法适合于对不同时间特性的数据、平稳性数据、趋势性数据或季节波动性数据进行预测,使用相应的预测方法均达到很好的平滑效果.同时还介绍了如何应用1Gs算法对指数平滑的多数进行优化,从而得到更好的平滑效果和预测结果,使之在社会实际当中发挥更好的作用.     

基于图连通支配集的子图匹配优化算法

在子图匹配过程中,随着图规模不断增长,匹配时间呈现指数爆炸的趋势.对此,提出一种基于图连通支配集的子图匹配优化算法VF-SMDS.根据贪心算法构建查询图的最小连通支配子图;通过代价模型计算最小连通支配子图节点的匹配代价,构建最优k查询节点匹配序列;通过支配节点的结构特征缩小查询节点搜索空间范围,在数据图中遍历到满足要求的节点,得到最终答案集.实验将VF-SMDS与GADDI、SPath、VF2++、VF3和SubISO方法进行对比.实验结果表明,在处理较大规模子图匹配问题时,VF-SMDS查询效率更高.     

基于灰色综合关联分析的多目标优化方法

针对现有多目标优化方法存在的搜索性能弱、效率低等问题,提出一种基于灰色综合关联分析的多目标优化方法.该多目标优化方法采用单目标优化算法构建高质量的参考序列,计算参考序列与优化解的目标函数值序列之间的灰色综合关联度,定义基于灰色综合关联度的解支配关系准则,将灰色综合关联度作为多目标优化算法的适应度值.以带顺序相关调整时间的多目标流水车间调度问题作为应用对象,建立总生产成本、最大完工时间、平均流程时间及机器平均闲置时间的多目标函数优化模型.提出基于灰色关联分析的多目标烟花算法,对所建立的多目标优化模型进行优化求解.仿真实验表明,所提出多目标烟花算法的性能优于3种基于不同多目标优化方法的烟花算法及两种经典多目标算法,验证了所提出的多目标优化方法及多目标算法的可行性和有效性.     

数据流中ρ-支配轮廓查询算法

数据流上的轮廓查询算法不能直接处理ρ-支配轮廓查询,而传统的ρ-支配轮廓查询无法在数据更新频繁时满足查询处理的实时性需求。因此,提出了数据流上的ρ-支配轮廓查询算法。首先,系统地介绍了完全支配、ρ-支配和ρ-支配轮廓的定义,进而提出了数据流上ρ-支配轮廓的定义。然后,通过深入分析数据流上的ρ-支配轮廓的性质,得出基于时序支配的数据过滤方法,并提出了基于滑动窗口的ρ-支配轮廓查询算法(ρ-dominant skyline query over sliding window,DSSW),提高了数据流上的ρ-支配轮廓计算的效率。最后,通过大量的实验证明,DSSW算法相比较于传统的ρ-支配轮廓查询算法,在响应时间及存储空间上均有明显优势。     

时间序列的细微距离发现

时间序列是信息系统一储存在的一类重要数据对象,而序列间的距离计算是很多时间序列数据开采或数据提取问题的核心。针对目前的序列距离定义模型对非总体的细微关联特征不敏感的问题,提出了一种新的时间序列距离定义模－时间序列的细微距离ＭＤ（Ｘ,Ｙ）,并提出了一种将时间序列由时域映射到频域,在频域中分离出不同的序列变化形式,以确定时间序列细微差别程度的算法－ＦＤＤ算法。ＦＤＤ算法具有较高的效率,且可以 作基准值     

时序图中Top-k稠密子图查询算法研究

稠密子图的查询是图分析领域的重要研究问题之一,在社交用户相关性分析、Web中社群分析等方面都有着广泛的应用.目前,关于稠密子图查询的研究工作主要基于静态图.而在实际应用中,时序信息会对稠密子图查询产生重要的影响,使得图拓扑结构随时间序列不断发生变化,包含的信息量也不断增加,使得已有的针对静态图的查找方法不再适用于时序图.因此,如何高效地在时序图上查找稠密子图仍然是一个挑战.为了解决上述挑战,首先规范化地定义了基于时序图的稠密子图查找问题;然后,根据图的拓扑结构和包含时间标签的边之间的相似度,提出一种基于阈值的近似查找算法DTS-base.为了加快算法的收敛速度,提出了一个基于快速计算最大相似度时间片的优化算法DTS-opt.最后,通过在真实数据集上的实验,证明了所提算法的高效性和可扩展性.     

多因素时间序列Skyline的快速查询算法

在给出多因素时间序列Skyline(MFTS)形式化定义的基础上,提出一种MFTS快速查询算法,获得并存储各时间点上的多因素值,计算每个最小时间区间的准MFTS,从而得到指定区间的MFTS。实验分析结果表明,在时间序列重合较少的情况下,该算法具有较好的查询效率。     

A neural network ensemble method with jittered training data for time series forecasting

Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing decision makers in many areas. Combining multiple models can be an effective way to improve forecasting performance. Recently, considerable research has been taken in neural network ensembles. Most of the work, however, is devoted to the classification type of problems. As time series problems are often more difficult to model due to issues such as autocorrelation and single realization at any particular time point, more research is needed in this area.In this paper, we propose a jittered ensemble method for time series forecasting and test its effectiveness with both simulated and real time series. The central idea of the jittered ensemble is adding noises to the input data and thus augments the original training data set to form models based on different but related training samples. Our results show that the proposed method is able to consistently outperform the single modeling approach with a variety of time series processes. We also find that relatively small ensemble sizes of 5 and 10 are quite effective in forecasting performance improvement.     

司法消噪与多技术融合的时间序列预测

针对时间序列的在线精确预测问题,建立了融合预测算法。创新地提出了司法消噪算法,在保留数据的原始信息前提下,实现了对时间序列中数据噪声和新稳态的处理;利用经验模式分解方法对除噪后的数据进行平稳化分解处理;结合BP神经网络、最小二乘支持向量机分别对分解后的低频、高频项进行预测,实现对时间序列的在线精确预测。该算法克服了BP神经网络的高频易发散和最小二乘支持向量机的计算高耗时问题。基于患者呼吸周期序列预测的仿真和临床实验结果表明,该算法能实现时间序列的在线精确预测,且误差小于单一的BP算法,耗时小于单一的最小二乘支持向量机预测算法。     

Multiple forecasts with autoregressive time series models: case studies

It is indisputable that accurate forecasts of economic activities are vital to successful business and government policies. In many circumstances, instead of a single forecast, simultaneous prediction intervals for multiple forecasts are more useful to decision-makers. For example, based on previous monthly sales records, a production manager would be interested in the next 12 interval forecasts of the monthly sales using for the annual inventory and manpower planning. For Gaussian autoregressive time series processes, several procedures for constructing simultaneous prediction intervals have been proposed in the literature. These methods assume a normal error distribution and can be adversely affected by departures from normality which are commonly encountered in business and economic time series. In this article, we explore the bootstrap methods for the construction of simultaneous multiple interval forecasts. To understand the mechanisms and characteristics of the proposed bootstrap procedures, several macro-economic time series are selected for illustrative purposes. The selected series are fitted reasonably well with autoregressive models which form an important class in time series. As a matter of fact, the major ideas discussed in this paper with autoregressive processes can be extended to other more complicated time series models.     

季节性组合预测模型在医院门诊量中的应用研究

医院门诊量是一个具有复杂的非线性组合特征的季节性时问序列。为解决传统时间序列预测大多数都是使用单一模型，以致影响预测精度等问题，采用了最优加权组合预测方法将季节性ARIMA乘积模型和季节性神经网络模型进行组合优化。结果表示，季节性组合预测模型在拟合精度和预测准确性方面优于任何单一预测方法，为季节性时间序列预测提供了一种新的实用方法。     

跨时间序列关联规则分析的高效处理算法

多元金融时间序列之间是互相影响的。该文就跨时间序列的关联规则挖掘提出一种新方法:ES-Apriori,此方法通过减少数据库扫描次数,优化内存分配,能够高效地分析多元时间序列之间的关联规则。试验表明,用此方法分析中国证券市场的股票时间序列非常有效。     

An approach for temporal analysis of email data based on segmentation

Many kinds of information are hidden in email data, such as the information being exchanged, the time of exchange, and the user IDs participating in the exchange. Analyzing the email data can reveal valuable information about the social networks of a single user or multiple users, the topics being discussed, and so on. In this paper, we describe a novel approach for temporally analyzing the communication patterns embedded in email data based on time series segmentation. The approach computes egocentric communication patterns of a single user, as well as sociocentric communication patterns involving multiple users. Time series segmentation is used to uncover patterns that may span multiple time points and to study how these patterns change over time. To find egocentric patterns, the email communication of a user is represented as an item-set time series. An optimal segmentation of the item-set time series is constructed, from which patterns are extracted. To find sociocentric patterns, the email data is represented as an item-setgroup time series. Patterns involving multiple users are then extracted from an optimal segmentation of the item-setgroup time series. The proposed approach is applied to the Enron email data set, which produced very promising results.     

Modeling data with multiple time dimensions

A large class of problems in time series analysis can be represented by a set of overlapping time series with different starting times. These time series may be treated as different probes of the same underlying process. Such probes may follow a characteristic lifecycle as a function of the time since the series began. They may also be subject to environmental shocks according to calendar time. In addition, the calibration of each probe may be unknown such that each series may show a different magnitude of response to the underlying lifecycles and environmental impacts.This paper describes an approach to analyzing these multiple time series as a single set such that the underlying lifecycles and calendar-based shocks may be measured. Simultaneously, the individual calibrations of the time series are also measured. This technique is referred to as dual-time dynamics, and it applies to many important business problems. Applications to tree ring analysis, the SETI@home project, and retail loan portfolio forecasting are provided. Other areas of possible application include digital media services, insurance, human resource management, health care, and biological systems to name a few.     

一种指数平滑预测的参数优化方法及实现

时间序列预测法在各种基于时态数据库的计算中有着广泛的应用前景。文中介绍了时间序列预测法中的单指数平滑、双指数平滑和三指数平滑三种指数平滑预测方法，不同的预测方法适合于对不同时间特性的数据、平稳性数据、趋势性数据或季节波动性数据进行预测，使用相应的预测方法均达到很好的平滑效果。同时还介绍了如何应用IGS算法对指数平滑的参数进行优化，从而得到更好的平滑效果和预测结果，使之在社会实际当中发挥更好的作用。