Building the Search Pattern of Social Media User Based on Cyber Individual Model

As the Web enters Big Data age, users and search engines may find it more and more difficult to effectively use and manage such big data. On one hand, people expect to get more accurate information with less search steps. On the other hand, search engines are expected to incur fewer resources of computing, storage and network, while serving the users more effectively. After more and more personal data becomes available, the basic issue is how to generate Cyber-I’s initial models and make the models growable. The ultimate goal is for the growing models to successively approach to or become more similar as individual’s actual characteristics along with increasing personal data from various sources covering different aspects. In this paper, we propose the concept of search pattern, summarize search engines into three search patterns and compare them in order to seek the more efficient one. We propose a new search pattern termed as ExNa, which can be incorporated into search engines to support more efficient search with better results.