An Ontology Approach to Data Integration using Mapping Method

Text mining is a technique to discover meaningful patterns from the available text documents. The pattern sighting from the text and document association of document is a well-known problem in data mining. Analysis of text content and categorization of the documents is a composite task of data mining. Some of them are supervised and some of them unsupervised manner of document compilation. The term “Federated Databases” refers to the in sequence integration of distributed, autonomous and heterogeneous databases. Nevertheless, a federation can also include information systems, not only databases. At integrating data, more than a few issues must be addressed. Here, we focus on the trouble of heterogeneity, more specifically on semantic heterogeneity – that is, problems correlated to semantically equivalent concepts or semantically related/unrelated concepts. In categorize to address this problem; we apply the idea of ontologies as a tool for data integration. In this paper, we make clear this concept and we briefly explain a technique for constructing ontology by using a hybrid ontology approach.

Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care

Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseases. However, data centralization for big data raises privacy and regulatory concerns. Covered topics include (1) an introduction to privacy of patient data and distributed learning as a potential solution to preserving these data, a description of the legal context for patient data research, and a definition of machine/deep learning concepts; (2) a presentation of the adopted review protocol; (3) a presentation of the search results; and (4) a discussion of the findings, limitations of the review, and future perspectives. Distributed learning from federated databases makes data centralization unnecessary. Distributed algorithms iteratively analyze separate databases, essentially sharing research questions and answers between databases instead of sharing the data. In other words, one can learn from separate and isolated datasets without patient data ever leaving the individual clinical institutes. Distributed learning promises great potential to facilitate big data for medical application, in particular for international consortiums. Our purpose is to review the major implementations of distributed learning in health care.