An exploratory study on ontology engineering for software architecture documentation

The usefulness of Software Architecture (SA) documentation depends on how well its Architectural Knowledge (AK) can be retrieved by the stakeholders in a software project. Recent findings show that the use of ontology-based SA documentation is promising. However, different roles in software development have different needs for AK, and building an ontology to suit these needs is challenging. In this paper we describe an approach to build an ontology for SA documentation. This approach involves the use of typical questions for eliciting and constructing an ontology. We outline eight contextual factors, which influence the successful construction of an ontology, especially in complex software projects with diverse AK users. We tested our ‘typical question’ approach in a case study and report how it can be used for acquiring and modeling AK needs.     

LoVis: Local Pattern Visualization for Model Refinement

Linear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre‐selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful for identifying local patterns. In this work, we present an integrated framework with visual representations that allows the user to incrementally build and verify models in three model spaces that support local pattern discovery and summarization: model complementarity, model diversity, and model representivity. Visual representations are designed and implemented for each of the model spaces. Our visualizations enable the discovery of complementary variables, i.e., those that perform well in modeling different subsets of data points. They also support the isolation of local models based on a diversity measure. Furthermore, the system integrates a hierarchical representation to identify the outlier local trends and the local trends that share similar directions in the model space. A case study on financial risk analysis is discussed, followed by a user study.     

Group‐in‐a‐Box Meta‐Layouts for Topological Clusters and Attribute‐Based Groups: Space‐Efficient Visualizations of Network Communities and Their Ties

An important part of network analysis is understanding community structures like topological clusters and attribute‐based groups. Standard approaches for showing communities using colour, shape, rectangular bounding boxes, convex hulls or force‐directed layout algorithms remain valuable, however our Group‐in‐a‐Box meta‐layouts add a fresh strategy for presenting community membership, internal structure and inter‐cluster relationships. This paper extends the basic Group‐in‐a‐Box meta‐layout, which uses a Treemap substrate of rectangular regions whose size is proportional to community size. When there are numerous inter‐community relationships, the proposed extensions help users view them more clearly: (1) the Croissant–Doughnut meta‐layout applies empirically determined rules for box arrangement to improve space utilization while still showing inter‐community relationships, and (2) the Force‐Directed layout arranges community boxes based on their aggregate ties at the cost of additional space. Our free and open source reference implementation in NodeXL includes heuristics to choose what we have found to be the preferable Group‐in‐a‐Box meta‐layout to show networks with varying numbers or sizes of communities. Case study examples, a pilot comparative user preference study (nine participants), and a readability measure‐based evaluation of 309 Twitter networks demonstrate the utility of the proposed meta‐layouts.     

Public Displays of Play: Studying Online Games in Physical Settings

As research on virtual worlds gains increasing attention in educational, commercial, and military domains, a consideration of how player populations are ‘reassembled’ through social scientific data is a timely matter for communication scholars. This paper describes a large‐scale study of virtual worlds in which participants were recruited at public gaming events, as opposed to through online means, and explores the dynamic relationships between players and contexts of play that this approach makes visible. Challenging conventional approaches to quantitatively driven virtual worlds research, which categorizes players based on their involvement in an online game at a particular point in time, this account demonstrates how players' networked gaming activities are contingent on who they are playing with, where, and when.     

基于国家电网的财务管理系统的开发与研究

随着社会生产力的不断发展,越来越多的企业认识到了财务工作的重要性。企业要利用现代高效的运作方式来开发和管理组织中最重要、最昂贵的资源,实现财务管理的精细化、自动化,提高财务资源的利用率,促进企业的发展。     

浅析电力用户终端的管理现状及对策

当前,电力用户终端管理中出现的问题越来越多,已经影响到供电企业的发展和社会的和谐。如何解决这些问题并建立有效的电力终端管理机制,是目前电力企业面临的首要问题。     

基于Web和SNMP的网络用户管理系统

现有的网络管理系统对于园区网中的终端用户缺乏有效的管理手段,满足不了当前园区网用户管理的需要。将Web技术与SNMP网络管理技术相结合,提出了一种基于B/S结构的网络管理模式,并采用ASP.net MVC3框架设计和实现了一个基于SNMP协议的Web网络用户管理系统,在Web平台上将设备管理与用户管理统一结合,通过软硬结合加强对网络用户的管理,减轻网络管理员的负担。     

平安校园安防监控系统需求分析及解决方案

近年来,安防监控系统技术发展迅速,安防产品已形成一个较为成熟的产业。本文通过详细描述平安校园安防监控系统的各个环节的设备特点、选型要求、设计理念等方面,从南通大学平安校园安防监控系统的建设实践出发,总结平安校园安防监控系统建设中的经验与创新。     

在线用户兴趣多样性的实证研究

在线用户的群体兴趣对于分析在线社会网络以及个性化推荐至关重要。研究的目的是引入信息熵这一指标来准确度量用户兴趣的多样性。分别在电影网站MovieLens和音乐网站Last．FM数据上进行实证分析,即统计度相同的用户所选产品的信息熵值。MovieLens的结果表明,随着用户度的增加,熵值出现先上升后下降的趋势,即度小的用户和度大的用户的兴趣比较专一,而一般用户的兴趣较为多样;而Last．FM的结果表明,度小用户的兴趣非常多样,但随着用户听过的音乐数量越多,兴趣越明确。通过建立随机模型与实证结果进行比较,可以发现绝大多数用户在真实数据集上的兴趣的多样性比随机情况要大,可见用户的兴趣对用户行为模式的影响显著。     

基于中位数的用户信誉度排名算法

针对推荐系统易受Spammer攻击的影响,从而导致对象的实际得分不准确的问题,提出基于中位数的用户信誉度排名算法。通过衡量用户信誉度调整用户打分权重,根据中位数具有不易受极端打分影响的特性,选取用户打分与对象得分差距的中位数作为降低用户信誉度的标准,不断迭代调整用户信誉度以及最终得分直至收敛。在多个真实数据集上的运行结果证明,相比现有排名算法,该算法具有更合理的信誉度分布和更高的排名结果准确度,通过该算法预处理后的数据集在SVD++上运行可以得到更低的均方根误差。