An Ontological Approach to Modelling and Analysing System Performance

本研究集中在集成系统设计和分析的一个方面，即系统性能测量的形式化、标准化和自动化分析(以下简称Metrics)。我们希望研究的一类系统是城市，而我们希望形式化和分析的绩效衡量标准是“城市指标”。城市指标衡量城市服务和功能，包括教育、卫生、安全、交通和水。有数千套不同的城市(或城市)指标，数百家机构汇编和审查这些指标。然而，这些指标通常没有标准化、一致性或可比性(随着时间或跨城市)……(Hornweg等人，2007年)。“开放数据”运动加剧了这一问题。在“开放数据”运动中，城市发布了大量数据，但没有两个城市或城市内的部门使用相同的数据模型(Fox，2013a)。开放数据的这种“巴别塔”式的后果是，很难对城市数据进行组合、分析和比较。问题并不是就指标的表面定义达成一致，因为定义中的每个术语都必须进一步定义(即，扎根)并提供支持数据。**我们相信有可能为Metrics确定和创建一个通用的、可重复使用的本体。我们相信，一个本体可以通过组合现有的基础、异质的本体，并用跨基础公理来扩展它们来创建。异构性指的是跨越分析模型(例如，比率)、统计模型(例如，人口测量)、地理空间模型(例如，城市边界)、时间模型(例如，时间段)和描述逻辑模型(例如，学生、教师等的定义)的本体。而且还包含了诸如出处、有效性和信任等元知识。我们相信，本体将使自动向下钻取和检测支持的异类数据中的不一致成为可能。因此，我们的第一个目标是形成

This research focuses on one aspect of the design and analysis of integrated systems, namely the formalization, standardization and automated analysis of system performance measurement (referred to as Metrics from here on in). One class of systems we wish to study is cities and the performance measures we wish to formalize and analyze are "city indicators." City indicators measure city services and functions, including Education, Health, Safety, Transportation, and Water. "There are thousands of different sets of city (or urban) indicators and hundreds of agencies compiling and reviewing them. . However, these indicators are usually not standardized, consistent or comparable (over time or across cities) ..." (Hornweeg et al., 2007). This problem has been exacerbated by the "Open Data" movement where cities are publishing vast amounts of data, but no two cities nor departments within a city use the same data models (Fox, 2013a). A consequence of this "babel" of open data is that it is difficult to combine, analyze and compare city data. The problem does not end with an agreement on the surface definition of an indicator, as each term within the definition has to be further defined (i.e., grounded) and supporting data provided.**We believe it is possible to identify and create a generic, reusable ontology for Metrics. We believe an ontology can be created by combining existing foundation, heterogeneous ontologies, and extending them with trans-foundation axioms. By heterogeneous, we mean ontologies that span analytical models (e.g., ratios), statistical models (e.g., population measurements), geo-spatial models (e.g., city boundaries), temporal models (e.g., time periods) and description logic models (e.g., definitions of students, teachers, etc.). But also incorporate meta-knowledge such as provenance, validity and trust. We believe that the ontology will make it possible to automate the drilling down and detection of inconsistencies in the supporting, heterogeneous data. Hence, our first objective is to forma