Architecture analysis of enterprise systems modifiability - Models, analysis, and validation

Enterprise architecture (EA) models can be used in order to increase the general understanding of enterprise systems and to perform various kinds of analysis. This paper presents instantiated architectural models based on a metamodel for enterprise systems modifiability analysis, i.e. for assessing the cost of making changes to enterprise-wide systems. The instantiated architectural models detailed are based on 21 software change projects conducted at four large Nordic companies. Probabilistic relational models (PRMs) are used for formalizing the EA analysis approach. PRMs enable the combination of regular entity-relationship modeling aspects with means to perform enterprise architecture analysis under uncertainty. The modifiability metamodel employed in the analysis is validated with survey and workshop data (in total 110 experts were surveyed) and with the data collected in the 21 software change projects. Validation indicates that the modifiability metamodel contains the appropriate set of elements. It also indicates that the metamodel produces estimates within a 75% accuracy in 87% of the time and has a mean accuracy of 88% (when considering projects of 2000 man-hours or more).     

XML文档到关系数据库的映射策略

在分析已有数据映射策略的基础上,提出新的XML 文档到关系数据库存储的映射策略和算法,以避免在数据转换过程中丢失信息,解决在查询和重构时因转换结果中关系过多或过少带来的问题。结合实例给出转换后的关系表,从而证明该映射策略能有效处理元素的循环引用,并保留元素之间的结构关系。     

基于遗传算法的关系数据库水印优化算法研究

提出了利用遗传算法在关系数据库海量的元组中选择最优元组进行水印嵌入,实现了水印短时间内的优化嵌入,大大提高了水印嵌入的效率。同时,采用纠错编码技术和投票选取机制来增加水印的鲁棒性。实验结果表明,这种关系数据库水印优化方案大大提高了水印嵌入的效率,并可以较好地协调水印的鲁棒性和数据库可用性之间的矛盾,实现了关系数据库水印的优化。     

首饰设计教学中关于艺术首饰与商业首饰的问题

艺术首饰是完全为艺术而服务的首饰,它本身就是一门艺术,属于纯艺术的范畴,艺术首饰的设计也就成了一种纯艺术活动。商业首饰则是为商业销售而服务的首饰,其创作动机和目的都在于产品的成功销售。高度发达的当代工艺美术正力图打破纯艺术与实用艺术之间的界限,这就为传统意义上的纯艺术和工艺美术建造了一个崭新的发展空间,为艺术首饰与商业首饰的融合提供了基础和可能,也为高校的首饰设计教学带来了新的思路。     

广告制胜的先决要素——视觉强度

视觉强度是所有诉诸视觉的艺术和设计追求的目的之一。广告是视觉艺术。若要释放这股强劲的潜能,视觉强度是视觉力量的保证。视觉强度,即对于视觉客体来说,是指对视觉主体的刺激或冲击程度;对于视觉主体来说,是指接受视觉对象刺激或冲击的强度,也称视觉感知度。一个人对不同的视觉对象产生不同的视觉感知度,而同一个视觉客体对不同人也可产生不同的冲击或刺激程度。空间与时间的差异也可引起视觉强度的差异。     

基于递归和表函数的一到多数据转换研究

针对关系表达式难以进行无限制一到多数据转换的问题,通过关系代数的扩展表达一到多数据转换,采用递归查询和表函数实现无限制一到多数据转换,在每个输入元组上产生一个或者多个输出元组。递归查询通过创建初始结果,递归获取结果集,并返回最终结果集;表函数声明变量集合,使用过程体和游标循环访问表,迭代输出元组。实验分析不同参数影响因素下有限制和无限制转换的不同方法,结果表明扩展方法能够改善系统性能。     

基于Simfusion和本体的视频语义提取

目前大多数的视频语义概念提取研究没有考虑到视频多模态之间的关联共生特性,而在样本的标注方面采用自定义的概念进行标注,会影响语义概念提取的准确率。针对上述问题,提出结合Simfusion算法和用本体知识库标注样本的方法提取视频的语义概念,该方法根据镜头内容变化提取关键帧,在提取出镜头内容时,有效地利用镜头多模态之间的时序关联共生特性,同时运用本体知识库中的概念标注样本、训练分类器,弥补传统方法在标注样本时存在的主观、不规范等不足。实验结果表明,该方法在视频语义概念提取的研究中,有较高的准确度、可操作性强。     

关系数据库近似匹配查询方法研究

针对传统海量数据精确查询负载过大的问题,引入基于仿真的置信区间自动抽样方法(Bootstrap)对数据库提供支持。通过对部分或采样数据进行查询,将查询简化到基础数据上,在对整个数据集查询一次的时间内,完成对多个样本重复多次的查询,得到数据库查询的置信区间;再进行基础SQL查询,得到符合用户要求的近似结果。实验结果表明,引入Bootstrap方法进行数据查询是有效的。     

Application of VPRS model with enhanced threshold parameter selection mechanism to automatic stock market forecasting and portfolio selection

This study proposes a technique based upon Fuzzy C-Means (FCM) classification theory and related fuzzy theories for choosing an appropriate value of the Variable Precision Rough Set (VPRS) threshold parameter (β) when applied to the classification of continuous information systems. The VPRS model is then combined with a moving Average Autoregressive Exogenous (ARX) prediction model and Grey Systems theory to create an automatic stock market forecasting and portfolio selection mechanism. In the proposed mechanism, financial data are collected automatically every quarter and are input to an ARX prediction model to forecast the future trends of the collected data over the next quarter or half-year period. The forecast data are then reduced using a GM(1, N) model, classified using a FCM clustering algorithm, and then supplied to a VPRS classification module which selects appropriate investment stocks in accordance with a pre-determined set of decision-making rules. Finally, a grey relational analysis technique is employed to weight the selected stocks in such a way as to maximize the rate of return of the stock portfolio. The validity of the proposed approach is demonstrated using electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The portfolio results obtained using the proposed hybrid model are compared with those obtained using a Rough Set (RS) selection model. The effects of the number of attributes of the RS lower approximation set and VPRS β-lower approximation set on the classification are systematically examined and compared. Overall, the results show that the proposed stock forecasting and stock selection mechanism not only yields a greater number of selected stocks in the β-lower approximation set than in the RS approximation set, but also yields a greater rate of return.     

Minimizing a nonlinear function under a fuzzy max-t-norm relational equation constraint

This work studies a nonlinear optimization problem subject to fuzzy relational equations with max-t-norm composition. Since the feasible domain of fuzzy relational equations with more than one minimal solution is non-convex, traditional nonlinear programming methods usually cannot solve them efficiently. This work proposes a genetic algorithm to solve this problem. This algorithm first locates the feasible domain through the maximum solution and the minimal solutions of the fuzzy relational equations, to significantly reduce the search space. The algorithm then executes all genetic operations inside this feasible domain, and thus avoids the need to check the feasibility of each solution generated. Moreover, it uses a local search operation to fine-tune each mutated solution. Experimental results indicate that the proposed algorithm can accelerate the searching speed and find the optimal solution.