Organic streams: Data aggregation and integration based on individual needs

With the high accessibility of the social media, more and more people have been accustomed to sharing their personal contents across the social networks, which results in an explosive increase of data scale. In this study, in order to support information and knowledge discovery in big data, we propose an approach to aggregation and integration of personal big data from life logs in accordance with individual needs, which can benefit the sustainable information utilization process. In details, the organic stream, which is designed as an extensible data carrier, is introduced and developed to formulize and organize the personal big data, in order to extract dynamical individual needs from the tremendous amount of data posted through social media, and further aggregate and integrate the related data in a meaningful way, which can also facilitate the personalized information retrieval and reuse process. The architecture of the system with the foundational modules is given, and the experiment result is presented to demonstrate the usability and effectiveness of our approach.