• Efficient Differential Timeslice Computation 

      Torp, Kristian; Mark, Leo; Jensen, Christian S. (Georgia Institute of Technology, 1994)
      Transaction-time databases record all previous database states and are ever-growing, leading to potentially huge quantities of data. For that reason, efficient query processing is of particular importance. Due to the large ...
    • Evolution in Data Streams 

      Huang, Weiyun; Omiecinski, Edward Robert; Mark, Leo (Georgia Institute of Technology, 2003)
      Conventional data mining deals with static data stored on disk, for example, using the current state of a data warehouse. In addition, the data may be read muliple times to accomplish the mining task. Recently, the data ...
    • A Fast Randomized Method for Local Density-based Outlier Detection in High Dimensional Data 

      Nguyen, Minh Quoc; Omiecinski, Edward; Mark, Leo (Georgia Institute of Technology, 2010)
      Local density-based outlier (LOF) is a useful method to detect outliers because of its model free and locally based property. However, the method is very slow for high dimensional datasets. In this paper, we introduce a ...
    • A Feature Model of Coupling Technologies for Earth System Models 

      Dunlap, Rocky; Rugaber, Spencer; Mark, Leo (Georgia Institute of Technology, 2010)
      Couplers that link together two or more numerical simulations are well-known abstractions in the Earth System Modeling (ESM) community. In the past decade, reusable software assets have emerged to facilitate scientists in ...
    • A Feature-based Sampling Method to Detect Anomalous Patterns in High Dimensional Datasets 

      Nguyen, Minh Quoc; Mark, Leo; Omiecinski, Edward (Georgia Institute of Technology, 2008)
      We introduce a feature-based sampling method to detect anomalous patterns. By recognizing that an observation is considered normal because there are many observations similar to it, we formally define the problem of ...
    • Local Region Caching to Support Object-Centered Constraints 

      Arnold, Stephen; Mark, Leo (Georgia Institute of Technology, 1995)
      Object-centered constraints are a computationally practical type of constraint that can be defined on any network of data. If a current-element in the database can be identified, a local region about the current element ...
    • Monitoring Object-Centered Constraints on Views Through Evaluating Queries as Search 

      Arnold, Stephen; Mark, Leo (Georgia Institute of Technology, 1995)
      Object-centered constraints are a computationally practical type of constraint that can be defined on any network of data. These constraints can be incrementally maintained by searching for constraint violations about ...
    • Specification and Efficient Monitoring of Local Graph-based Constraints in Hypermedia Systems 

      Arnold, Stephen; Mark, Leo; Navathe, Shamkant B. (Georgia Institute of Technology, 1994)
      The concept of hypermedia has existed for about fifty years. It became a practical technology in the seventies, and widely available in the eighties. The concept has proven quite useful as a paradigm for information ...
    • Subspace Outlier Detection in Data with Mixture of Variances and Noise 

      Nguyen, Minh Quoc; Mark, Leo; Omiecinski, Edward (Georgia Institute of Technology, 2008)
      In this paper, we introduce a bottom-up approach to discover clusters of outliers in any m-dimensional subspace from an n-dimensional space. First, we propose a method to compute the outlier score for all points in each ...