Exploring the use of topological data analysis to automatically detect data quality faults.

Data quality problems may occur in various forms in structured and semi-structured data sources. This paper details an unsupervised method of analyzing data quality that is agnostic to the semantics of the data, the format of the encoding, or the internal structure of the dataset. A distance function is used to transform each record of a dataset into an n-dimensional vector of real numbers, which effectively transforms the original data into a high-dimensional point cloud. The shape of the point cloud is then efficiently examined via topological data analysis to find high-dimensional anomalies that may signal quality issues. The specific quality faults examined in this paper are the detection of records that, while not exactly the same, refer to the same entity. Our algorithm, based on topological data analysis, provides similar accuracy for both higher and lower quality data and performs better than a baseline approach for data with poor quality.

OOSTT: a Resource for Analyzing the Organizational Structures of Trauma Centers and Trauma Systems.

Organizational structures of healthcare organizations has increasingly become a focus of medical research. In the CAFÉ project we aim to provide a web-service enabling ontology-driven comparison of the organizational characteristics of trauma centers and trauma systems. Trauma remains one of the biggest challenges to healthcare systems worldwide. Research has demonstrated that coordinated efforts like trauma systems and trauma centers are key components of addressing this challenge. Evaluation and comparison of these organizations is essential. However, this research challenge is frequently compounded by the lack of a shared terminology and the lack of effective information technology solutions for assessing and comparing these organizations. In this paper we present the Ontology of Organizational Structures of Trauma systems and Trauma centers (OOSTT) that provides the ontological foundation to CAFÉ's web-based questionnaire infrastructure. We present the usage of the ontology in relation to the questionnaire and provide the methods that were used to create the ontology.