Web-based interactive mapping from data dictionaries to ontologies, with an application to cancer registry

Background The Kentucky Cancer Registry (KCR) is a central cancer registry for the state of Kentucky that receives data about incident cancer cases from all healthcare facilities in the state within 6 months of diagnosis. Similar to all other U.S. and Canadian cancer registries, KCR uses a data dictionary provided by the North American Association of Central Cancer Registries (NAACCR) for standardized data entry. The NAACCR data dictionary is not an ontological system. Mapping between the NAACCR data dictionary and the National Cancer Institute (NCI) Thesaurus (NCIt) will facilitate the enrichment, dissemination and utilization of cancer registry data. We introduce a web-based system, called Interactive Mapping Interface (IMI), for creating mappings from data dictionaries to ontologies, in particular from NAACCR to NCIt. Method IMI has been designed as a general approach with three components: (1) ontology library; (2) mapping interface; and (3) recommendation engine. The ontology library provides a list of ontologies as targets for building mappings. The mapping interface consists of six modules: project management, mapping dashboard, access control, logs and comments, hierarchical visualization, and result review and export. The built-in recommendation engine automatically identifies a list of candidate concepts to facilitate the mapping process. Results We report the architecture design and interface features of IMI. To validate our approach, we implemented an IMI prototype and pilot-tested features using the IMI interface to map a sample set of NAACCR data elements to NCIt concepts. 47 out of 301 NAACCR data elements have been mapped to NCIt concepts. Five branches of hierarchical tree have been identified from these mapped concepts for visual inspection. Conclusions IMI provides an interactive, web-based interface for building mappings from data dictionaries to ontologies. Although our pilot-testing scope is limited, our results demonstrate feasibility using IMI for semantic enrichment of cancer registry data by mapping NAACCR data elements to NCIt concepts.

DISTRIBUTION TREN OF SENIOR HIGH SCHOOL AS ORIGIN SCHOOL OF COLLEGE STUDENT STIKOM BALI BASE GIS

GIS (Geographic Information System) is an information system that can store and integrate spatial data and non-spatial data can be used for interactive mapping of STIKOM Student School origin in Denpasar. During this time the spread of origin of high school / vocational school / equivalent STIKOM Bali students has never been mapped to find out the trend of increase or decrease in the number of origin of STIKOM Bali student schools from 2013-2018 and predict the number of students in accordance with the origin of schools in the city of Denpasar. This study aims to provide information to the management of STIKOM Bali regarding the distribution trends of the interests of prospective students to continue to tertiary level, especially STIKOM Bali. This research will collaborate between statistical science and the concept of GIS (Geographic Information System). Statistically the number of STIKOM Bali students is based on the origin of schools in Denpasar City and predicts it for the next 3 years using a trend analysis of semi-average methods (Semi Average Methods) as a material for evaluating the performance of STIKOM Bali management in improving the performance of campus promotions. This method makes trends by finding the average group of data which consists of grouping data into 2 parts, calculating average arithmetic, calculating the difference, formulating the value of change and making equations for subsequent trends. The results of these calculations are mapped with the concept of GIS (Geographic Information System) using ArcView as software to implement that integrates spatial data with non-spatial data.

Exploring the visual correlation of choropleth maps using an evolutionary algorithm

Multivariate choropleth maps are often used to compare patterns of different spatial variables. This approach can be implemented by simultaneously drawing a series of choropleth maps, with each representing a particular variable. In this paper, we develop an evolutionary algorithm that can be used to generate a set of classifications that allow a user to explore the spatial patterns of multiple choropleth maps in terms of their visual correlation. Synthetic and census data are used to demonstrate the effectiveness of our approach. We also discuss the role of our approach in an interactive mapping environment and its implication for spatial data mining.