摘要翻译：
基于已完成项目的多元数据的软件成本估算需要建立有效的模型。这些模型基本上描述了数据中的关系，或者基于变量之间的相关性，或者基于项目之间的相似性。收集的数据量的持续增长以及为了发现能够推动建立合理模型的模式而进行初步分析的需求，导致研究人员转向能够有效描述数据及其关系的智能和节省时间的工具。本文的目标是提出一个创新的可视化工具，广泛应用于生物信息学，它以一种美学和智能的方式来表示数据中的关系。为了说明该工具的功能，我们使用了一个来自软件工程项目的众所周知的数据集。
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英文标题：
《Discovering patterns of correlation and similarities in software project
  data with the Circos visualization tool》
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作者：
Makrina Viola Kosti, Sofia Lazaridou, Nikoleta Bourazani, Lefteris
  Angelis
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Software Engineering        软件工程
分类描述：Covers design tools, software metrics, testing and debugging, programming environments, etc. Roughly includes material in all of ACM Subject Classes D.2, except that D.2.4 (program verification) should probably have Logics in Computer Science as the primary subject area.
涵盖设计工具、软件度量、测试和调试、编程环境等。大致包括ACM所有主题课程D.2的材料，除了D.2.4（程序验证）可能应该有计算机科学中的逻辑作为主要主题领域。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Software cost estimation based on multivariate data from completed projects requires the building of efficient models. These models essentially describe relations in the data, either on the basis of correlations between variables or of similarities between the projects. The continuous growth of the amount of data gathered and the need to perform preliminary analysis in order to discover patterns able to drive the building of reasonable models, leads the researchers towards intelligent and time-saving tools which can effectively describe data and their relationships. The goal of this paper is to suggest an innovative visualization tool, widely used in bioinformatics, which represents relations in data in an aesthetic and intelligent way. In order to illustrate the capabilities of the tool, we use a well known dataset from software engineering projects.
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PDF链接：
https://arxiv.org/pdf/1110.1303