A concept model of ‘two-ties-aware’ and design of a discovery engine based on user experienced Bigdata

IoT/Bigdata is a hot research topic all over the world in recent years and is expecting to change the world greatly in the near future. Comparing with the data in traditional websites, Bigdata from IoT devices have 4 big V-features, i.e., volume, velocity, variety, and veracity. Due to the above four features, it is hard to provide timely services to users by data analysis, especially with the great growth of data types, volume and so on. Data should be able to aware situations/demands of users, and automatically be adjusted for discovering the situations/demands of users´. Therefore, in this paper, we propose a two-ties-aware mechanism for Bigdata management and analysis. The first-tie-aware is to automatically grasp the situations around the user, and encapsulate the situation together when data is generated. The second-tie-aware is to automatically change the data to fit users´ situations/demands. Furthermore, we propose a novel discovery algorithm based on the two-tiles-aware model. Given the user inputs from their ambiguous memory fragments, the discovery algorithm tries to discover the truly wanted information. Currently, the system is going to be implemented based on some open sources.