Browsing by Author "ZHAO, Feng"

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    BROAD: Diversified Keyword Search in Databases
    (2011-03-21T08:18:20Z) ZHAO, Feng; ZHANG, Xiaolong; TUNG, Anthony K. H.; CHEN, Gang
    Keyword search in databases has received a lot of attention in the database community as it is an effective approach for querying a database without knowing its underlying schema. However, keyword search queries often return too many results. One standard solution is to rank results such that the “best” results appear first. Still, this approach can suffer from redundancy problem where many high ranking results are in fact coming from the same part of the database and results in other parts of the database are missed completely. In this paper, we propose the BROAD system which allows users to perform diverse, hierarchical browsing on keyword search results. Our system partitions the answer trees in the keyword search results by selecting k diverse representatives from the trees, separating the answer trees into k groups based on their similarity to the representatives and then recursively applying the partitioning for each group. By constructing summarized result for the answer trees in each of the k groups, we provide a way for users to quickly locate the results that they desire. Technically, our solution consists of three components. First, a new distance metric is used to capture both semantic and structural dissimilarity between answer trees. Second, based on this metric, we propose a tree-based algorithm to efficiently achieve result diversification. Finally, by coupling our partitioning solution with result summarization techniques, we allow users to decide which partition to drill down in order to obtain their intended answers. Extensive experiments were conducted and the results validate the feasibility and the efficiency of our system.
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    Large Scale Cohesive Subgraphs Discovery for Social Network Visual Analysis
    (2012-04-03T09:41:45Z) ZHAO, Feng; TUNG, Anthony K. H.
    Graphs are widely used in large scale social network analysis nowadays. Not only analysts need to focus on cohesive subgraphs to study patterns among social actors, but also normal users are interested in discovering what happening in their neighborhood. However, e®ectively storing large scale social network and e±ciently identifying cohesive subgraphs is challenging. In this work we introduce a novel subgraph concept to capture the cohesion in social interactions, and propose an I/O e±cient approach to discover cohesive sub- graphs. Besides, we propose an analytic system which allows users to perform intuitive, visual browsing on large scale social networks. Our system stores the network as a social graph in the graph database, retrieves a local cohesive subgraph based on the input keywords, and then visualizes the sub-graph out on orbital layout, in which more important social actors are located in the center. By summarizing textual interactions between social actors as tag cloud, we provide a way to quickly locate active social communities and their interactions in a uni¯ed view.