Browsing by Author "Lu, Chang-tien"

Now showing 1 - 4 of 4
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    A Unified Approach to Spatial Outlier Detection
    (2001-12-10) Shekhar, Shashi; Lu, Chang-tien; Zhang, Pusheng
    Spatial outliers represent locations which are significantly different from their neighborhoods even though they may not be significantly different from the entire population. Identification of spatialoutliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability. In this paper, we first provide a general definition of $S$-outliers for spatial outliers. This definition subsumes the traditional definitions of spatial outliers. Second, we characterize the computation structure of spatial outlier detection methods andpresent scalable algorithms. Third, we provide a cost model of the proposed algorithms. Finally, we provide experimental evaluations of our algorithms using a Minneapolis-St. Paul(Twin Cities) traffic data set.
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    Data Mining and Visualization of Twin-Cities Traffic Data
    (2001-03-08) Shekhar, Shashi; Lu, Chang-tien; Chawla, Sanjay; Zhang, Pusheng
    Data Mining(DM) is the process of extracting implicit, valuable, and interesting information from large sets of data. As huge amounts of data have been stored in traffic and transportation databases, data warehouses, geographic information systems, and other information repositories, data mining is receiving substantial interest from both academia and industry. The Twin-Cities traffic archival stores sensor network measurements collected from the freeway system in the Twin-Cities metropolitan area. In this paper, we construct a traffic data warehousing model which facilitates on-line analytical processing(OLAP), employ current data mining techniques to analyze the Twin-Cities traffic data set, and visualize the discoveries on the highway map. We also discuss some research issues in mining traffic and transportation data.
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    Detecting Graph-based Spatial Outliers: Algorithms and Applications
    (2001-03-08) Shekhar, Shashi; Lu, Chang-tien; Zhang, Pusheng
    Identification of outliers can lead to the discovery of unexpected, interesting, and implicit knowledge. Existing methods are designed for detecting spatial outliers in multidimensional geometric data sets, where a distance metric is available. In this paper, we focus on detecting spatial outliers in graph structured data sets. We define tests for spatial outliers in graph structured data sets, analyze the statistical foundation underlying our approach, design a fast algorithm to detect spatial outliers, provide the cost model for outlier detection procedures. In addition, we provide experimental results from the application of our algorithm on a Minneapolis-St. Paul(Twin-Cities) traffic dataset to show its effectiveness and usefulness.
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    Efficient Join-Index-Based Join Processing: A Clustering Approach
    (1999-08-06) Shekhar, Shashi; Lu, Chang-tien; Chawla, Sanjay
    A Join Index is a data structure used for processing join queries in databases. Join indices usepre-computation techniques to speed up online query processing and are useful for data-sets which are updated infrequently. The cost of join computation using a join-index with limited buffer space depends primarily on the page-access sequence used to fetch the pages of the base relations. Given the join-index, we introduce a suite of methods based on clustering to compute the joins. We derive upper-bounds on the lengths of the page-access sequences. Experimental results with Sequoia 2000 data sets show that the clustering method outperforms the existing methods based on sorting and online-clustering heuristics.