An Ontological Approach to Modelling and Analysing System Performance

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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 formalize the definition of Metrics using the technology of Formal Ontologies as implemented in the Semantic Web.We believe it is possible to create a logical theory of Metrics analysis. We believe that consistency analysis provides a basis for detecting root causes in changes in Metrics. Hence, our second objective is to develop a reusable, interoperable logical theory of longitudinal and transversal analysis of Metrics. We will extend the Metrics’ consistency axioms to address longitudinal (temporal) and transversal (cross city) consistency across heterogeneous models.Strong, successful cities are prerequisites for a strong, vibrant nation. In Canada, over 80% of Canadians currently live in urban areas. The sustainability of urban regions (in environmental, social and fiscal terms), and their resilience to both man-made and natural large-scale events, are thus of first-order national importance to our well-being and quality of life. Dealing intelligently and effectively with the complex problems of urban management and design requires use of the best information, analysis and decision-support tools available. This program’s contributions include:• The formal definition of Metrics that provide precise, unambiguous and machine interpretable definitions of system performance,• The ability to perform consistency and comparative analyses of Metrics based upon their definitions (as opposed to ad hoc software implementations), and• In the case of cities, the standardization of terminology thereby enabling cities to publish their open data using common vocabularies or ontologies.