A Knowledge Discovery Based Big Data for Context aware Monitoring Model for Assisted Healthcare

Cloud computing is a fast growing technology that eliminates the need to maintain expensive hardware, software and dedicated storage space. It is an on-demand computing that stores data in remote locations rather than storing in local storage attached to the computer. Big data utilizes cloud computing to store, process and analyze a large amount of data. The existing models have no prior knowledge about the storage repositories. The rapid increase in the amount of data leads to storage and time complexity. To overcome these drawbacks, this paper proposes a fuzzy based MapReduce Apriori algorithm using context aware monitoring to extract the required information for decision making. It also proposed a Context Aware Healthcare Monitoring (CAHM) system to monitor the users’ activities and to classify them based on their behavior. Context awareness provides a personalized service to the users related to their expectations without any explicit request. The proposed algorithm has two functions: Mapper and Reducer. The mapper function gets a key value pair as input and generates intermediate key value pairs. The reducer function aggregates all the associated key value pairs using context aware information. This algorithm takes less execution time and achieves high accuracy. The use of context aware monitoring improves the accuracy of the system. The experimental results evaluate the proposed system in terms of efficiency, accuracy and activity count.