In this article; the authors propose to use the grid file to store multi-dimensional data cubes and answer angesum queries. The grid file is enhanced with a dynamic splitting mechanism to accommodate insertions of data. It overcomes the drawback of the traditional grid file in storing uneven data while enjoying its advantages of simplicity and efficiency. The space requirement grows linearly with the dimension of the data cube; compared with the exponential growth of conventional methods that store pre-computed aggregate values for range-sum queries. The update cost is O (1); much faster than the pre-computed data cube approaches; which generally have exponential update cost. The grid file structure can also respond to range queries quickly. They compare it with an approach that uses the R*-tree structure to store the data cube. The experimental results show that the proposed method performs favorably in file size; update speed; construction time; and query response time for both evenly and unevenly distributed data.

XML structural joins, which evaluate the containment (ancestor-descendant) relationships between XML elements, are important operations of XML query processing. Estimating structural join size accurately and quickly is thus crucial to the success of XML query plan selection and the query optimization. XML structural joins are essentially complex unequal joins, which render well-known estimation techniques, such as cosine transform, wavelet transform, and sketch, not directly applicable. In this paper, we propose a relation model to capture the structural information of XML data such that the original complex unequal joins are converted to equal joins and those well-known estimation techniques become directly applicable to structural join size estimation. Theoretical analyses and extensive experiments have been performed on these estimation methods. It is shown that the cosine transform requires the least memory and yields the best estimates.

Caching can reduce expensive data transfers and improve the performance of mobile computing. In order to reuse caches after short disconnections, invalidation reports are broadcast to clients to identify outdated items. Detailed reports may not be desirable because they can consume too much bandwidth. On the other hand, false invalidations may set in if the accurate timing of updates is not provided. In this research, we aim to reduce the false invalidation rates of cached items. Based on our analysis, false invalidation rates are closely related to clients' reconnection patterns (i.e., the distribution of the time spans between disconnections and reconnections). We show that in theory for any given reconnection pattern, a report with a minimal false invalidation rate can be derived. For practical uses, we propose to capture the reconnection pattern by sampling and develop a method to compose a near-optimal invalidation report. This method is simple and fast. Experimental results have confirmed that our method is indeed more effective in reducing the false invalidation rate than others.

We develop efficient algorithms for problems in computational geometry-convex hull, smallest enclosing box, ECDF two-set dominance, maximal points, all-nearest neighbor and closest-pair-on the OTIS-Mesh optoelectronic computer We also demonstrate the algorithms for computing convex hull and prefix sum with condition on a multi-dimensional mesh, which are used to compute convex hull and ECDF respectively. We show that all these problems can be solved in O(/spl radic/N) time even with N/sup 2/ inputs.

We develop algorithms to multiply two vectors, a vector and a matrix, and two matrices on an OTIS-Mesh optoelectronic computer. Two mappings, group row and group submesh, of a matrix onto an OTIS-Mesh are considered and the relative merits of each compared. We show that our algorithms to multiply a column and row vector use an optimal number of data moves for both the group row and group submesh mappings, our algorithm to multiply a row vector and a column vector is optimal for the group row mapping, and our algorithm to multiply a matrix by a column vector is optimal for the group row mapping.

We develop algorithms for histogramming, histogram modification, Hough transform, and image shrinking and expanding on an OTIS-mesh optoelectronic computer. Our algorithm for the Hough transform is based upon a mesh algorithm for the Hough transform which is also developed in this paper. This new mesh algorithm improves upon the previous mesh Hough transform algorithms.

In this paper, we develop algorithms for some basic operations-broadcast, window broadcast, prefix sum, data sum, rank, shift, data accumulation, consecutive sum, adjacent sum, concentrate, distribute, generalize, sorting, random access read and write-on the OTIS-Mesh model. These operations are useful in the development of efficient algorithms for numerous applications.