Browsing by Author "TAN, Kian-Lee"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    ASSIST: Access Controlled Ship Identification Streams
    (2013-02-08T04:32:24Z) CAO, Jianneng; KISTER, Thomas; XIANG, Shili; MALHOTRA, Baljeet; TAN, Wee-Juan; TAN, Kian-Lee; BRESSAN, Stéphane
    The International Maritime Organization (IMO) requires a majority of cargo and passenger ships to use the Automatic Identification System (AIS) for navigation safety and traffic control. Distributing live AIS data on the Internet can offer a global view for both operational and analytical purposes to port authorities, shipping and insurance companies, cargo owners and ship captains and other stakeholders. Yet, uncontrolled, this distribution can seriously undermine navigation safety and security and the privacy of the various stakeholders. In this paper we present ASSIST, an application system based on our recently proposed access control framework, to protect streaming data from unauthorized access. Furthermore, we have implemented ASSIST on top of StreamInsight, a commercial stream engine. The extensive experimental results show that our solution is more effective and efficient than existing approaches.
  • No Thumbnail Available
    Item
    Finding Time-lagged 3D Clusters
    (2008-06-19) XU, Xin; LU, Ying; TAN, Kian-Lee; TUNG, Anthony K. H.
    Existing 3D clustering algorithms on $gene\times sample\times time$ expression data do not consider the \emph{time lags} between correlated gene expression patterns. Besides, they either ignore the correlation on \emph{time subseries}, or disregard the \emph{continuity} of the time series, or only validate pure shifting or pure scaling coherent patterns instead of the general \emph{shifting-and-scaling patterns}. In this paper, we propose a novel 3D cluster model, $S^2D^3$ Cluster, to address these problems, where $S^2$ reflects the shifting-and-scaling correlation and $D^3$ the 3-Dimensional $gene\times sample\times time$ data. Within the $S^2D^3$ Cluster model, expression levels of genes are shifting-and-scaling coherent in both sample subspace and time subseries with arbitrary time lags. We develop a 3D clustering algorithm, $LagMiner$, for identifying interesting $S^2D^3$ Clusters that satisfy the constraints of regulation ($\gamma$), coherence ($\epsilon$), minimum gene number ($MinG$), minimum sample subspace size ($MinS$) and minimum time periods length ($MinT$). Experimental results on both synthetic and real-life datasets show that $LagMiner$ is effective, scalable and parameter-robust. While we use gene expression data in this paper, our model and algorithm can be applied on any other data where both spatial and temporal coherence are pursued.