Items by Zhou, Lizhu

In this paper, we study a novel problem of staring people dis- covery from social networks, which is concerned with finding people who are not only authoritative but also sociable in the social network. We formalize this problem as an optimiza- tion programming problem. Taking the co-author network as a case study, we define three objective functions and pro- pose two methods to combine these objective functions. A genetic algorithm based method is further presented to solve this problem. Experimental results show that the proposed solution can effectively find the staring people from social networks.

In a traditional keyword-search system in XML data, a user composes a keyword query, submits it to the system, and retrieves relevant subtrees. In the case where the user has limited knowledge about the data, often the user feels “left in the dark” when issuing queries, and has to use a tryand-see approach for finding information. In this paper, we study a new information-access paradigm for XML data, called “Inks,” in which the system searches on the underlying data “on the fly” as the user types in query keywords. Inks extends existing XML keyword search methods by interactively answering keyword queries. We propose effective indices, early-termination techniques, and efficient search algorithms to achieve a high interactive speed. We have implemented our algorithm. The experimental results show that Inks achieves high search efficiency and result quality.