The design of distributed databases is an optimization problem requiring solutions to several interrelated problems including: data fragmentation, allocation, and local optimization. Each problem can be solved with several different approaches thereby making the distributed database design a very difficult task. Although there is a large body of work on the design of data fragmentation, most of them are either ad hoc solutions or formal solutions for special cases (e. g., binary vertical partitioning). In this paper, we address the general vertical partitioning problem formally. We first provide a comparison of work in the area of data clustering and distributed databases to highlight the thrust of this work. We derive an objective function that generalizes and subsumes earlier work on vertical partitioning in databases. The objective function developed in this paper provides a basis for developing heuristic algorithms for vertical partitioning. The objective function also facilitates ...

The design of distributed databases is an optimization problem requiring solutions to several interrelated problems: data fragmentation, allocation, and local optimization. Each problem can be solved with several different approaches thereby making the distributed database design a very difficult task. Although there is a large body of work on the design of data fragmentation, most of them are either ad hoc solutions or formal solutions for special cases (e. g., binary vertical partitioning). In this paper, we address the problem of n-ary vertical partitioning problem and derive an objective function that generalizes and subsumes earlier work. The objective function derived in this paper is being used for developing heuristic algorithms that can be shown to satisfy the objective function. The objective function is also being used for comparing previously proposed algorithms for vertical partitioning. We first derive an objective function that is suited to distributed transaction proces...

We describe an effective approach to object or feature detection in point patterns via noise modeling. This is based on use of a redundant or non-pyramidal wavelet transform. Noise modeling is based on a Poisson process. We illustrate this new method with a range of examples. We use the close relationship between image (pixelated) and point representations to achieve the result of a clustering method with constant-time computational cost. Keywords: Cluster analysis, point pattern, `a trous wavelet transform, noise modeling, Poisson distribution, minefield detection. 1 Introduction Point pattern clustering has constituted one of major strands in cluster analysis. We will briefly describe some of this work, in order to motivate the need for (i) a multiscale approach which 1 is computationally very efficient, and (ii) a direct treatment of noise and clutter which leads to improved cluster detection. In the following the first few categories of work proceed in the direction of a m...