Knowledge process of health big data using MapReduce-based associative mining

Big-data knowledge processing technology facilitates efficient health management services by systematically collecting and promoting information using distributed/parallel processing with the health platform's common data model. Thus, it enables knowledge expansion for healthcare data. In this study, we propose a big-data knowledge process for the health industry using Hadoop's MapReduce software for association mining. The proposed method provides efficient health management knowledge services by collecting and processing heterogeneous health information using WebBot and the common data model. Hadoop is a proprietary method of effectively processing distributed big data. It is a knowledge processing model that combines MapReduce-based distributed processing and a method of finding mining-based associations. The input data in MapReduce is extracted from chronic disease nomenclature from health big data. The corpus divides big data into several blocks of a certain size, creating map tasks. Through the map function of the mapper of each map task, <|key|, value> sets composed of pairs of a key and a value are created. In the map process, a key is created using the same method used for a frequent item set of the Apriori algorithm. The key is a set of 2(p)keys and its value is set to the occurrence frequency of the key. By summing up the values of the same keys by combining, the size of data is decreased and the load of a software program is also decreased. In addition, for each key, the reducer is designated through hash partitioning and stored in the reduce task. In the reduce process, the results of the map are allocated to each reducer, and alignment and merge steps are taken based on the keys. For the same |key|, the values are summed up by performing the reduce function. In this instance, keys whose values fail to meet the minimum support criterion are eliminated. Therefore, from a set of <|key|, value>, a frequent item set that meets the minimum support criterion is extracted. The association rules between datasets constituting the frequent item set are determined, and the support and reliability are calculated to examine whether they are actually associated. As the value of the frequent item set is higher, the support and reliability are also higher. Thus means that the association is obvious. A knowledge base is then constructed using the extracted association rules by repeatedly performing the MapReduce process. Closely associated knowledge bases are created and semantically related in real time with high probability. Furthermore, mining-based knowledge processing of health big data infers more meaningful associations between chronic diseases. The proposed method adds technological value and intelligent efficiency to support the health and medical fields promote healthy lives.