The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems. Recommender systems are personalized information filtering used to identify a set of items that will be of interest to a certain user. This paper reviews recommender systems and presents their pros and cons. (Schein et al., 2002;Yu et al., 2004). 따라서 최 근에는 개인화 된 추천 시스템을 구현하기 위해 정보필터링 방법과 연관성 분석 등 다양한 추천 기법이 연구되고 있다 Jin et al., 2010
RDF is a data model for representing labeled directed graphs, and it is used as an important building block of semantic web. Due to its flexibility and applicability, RDF has been used in applications, such as semantic web, bioinformatics, and social networks. In these applications, large-scale graph datasets are very common. However, existing techniques are not effectively managing them. In this paper, we present a scalable, efficient query processing system for RDF data, named SPIDER, based on the well-known parallel/distributed computing framework, Hadoop. SPIDER consists of two major modules (1) the graph data loader, (2) the graph query processor. The loader analyzes and dissects the RDF data and places parts of data over multiple servers. The query processor parses the user query and distributes sub queries to cluster nodes. Also, the results of sub queries from multiple servers are gathered (and refined if necessary) and delivered to the user. Both modules utilize the MapReduce framework of Hadoop. In addition, our system supports some features of SPARQL query language. This prototype will be foundation to develop real applications with large-scale RDF graph data.
Objectives: People are living longer, but often with diseases or chronic conditions. As a consequence, interest in resolving insurance blind spots is growing. This study provides substandard risk-relevant statistics to help substandard risks who are likely to fall in insurance blind spots obtain insurance coverage, such as the reimbursement of medical costs, as well as to stimulate insurance product development. Methods: This study uses National Health Insurance Service (NHIS) cohort data to determine the relevant statistics. The incidence rates of severe diseases are derived and compared against standard risks to establish a set of relative risk factors. These incidence rates of standard and substandard risks are then compared. Results: Currently, an individual's cancer history is used in the underwriting process for simplified issue insurance. However, underwriting focusing on hospitalization and procedures related to serious illnesses could lower premiums for substandard risks. Moreover, the statistical results could be used to expand the coverage of health insurance products. Conclusions: This study's relative risk factors can be used to derive simplified issue premium rates for substandard risks. They can also be used to implement discount and loading schemes for medical reimbursement insurance and help insurance companies implement proactive risk management.
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