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Martinez-Villaseñor, Lourdes
Fuzzy aggregation of similarity values for electronic health record interoperability
An enhanced process of concept alignment for dealing with overweight and obesity
2019
,
Martinez-Villaseñor, Lourdes
,
Ponce, Hiram
,
González-Mendoza, Miguel
Schema matching is used for data integration, mediation, and conversion between heterogeneous sources. Nevertheless, mappings identified with an automatic or semi-automatic process can never be completely certain. In a process of concept alignment, it is necessary to manage uncertainty. In this paper, we present a fuzzy-based process of concept alignment for uncertainty management in schema matching problem. The ultimate goal is to enable interoperability between different electronic health records. Data integration of health information is done through the mediation of our ubiquitous user model framework. Results look promising and fuzzy theory proved to be a good fit for modeling uncertain schema matching. Fuzzy combined similarities can handle uncertainty in the schema matching process to enable interoperability between electronic health records improving the quality of mappings and diminishing the human error to verify the mappings. © 2019 - IOS Press and the authors. All rights reserved
2013
,
Martinez-Villaseñor, Lourdes
,
González-Mendoza, Miguel
A major challenge for creating personalized diet and activity applications is to capture static, semi-static and dynamic information about a person in a user-friendly way. Sharing and reusing information between heterogeneous sources like social networking applications, personal health records, specialized applications for diet and exercise monitoring, and personal devices with attached sensors can achieve a better understanding of the user. Gathering distributed user information from heterogeneous sources and making sense of it to enable user model interoperability entails handling the semantic heterogeneity of the user models. In this paper, we enhance the process of concept alignment to automatically determine semantic mapping relations to enable interoperability between heterogeneous health and fitting applications. We add an internal structure similarity measure to increase the quality of generated mappings of our previous work. We show that the addition of an internal structure analysis of source data in the process of concept alignment improves the efficiency and effectiveness of measuring results. Constrain and data type verification done in the internal structure analysis proved to be useful when dealing with common conflicts between concepts.