A problem of increasing importance in the design of large multiprogramming systems is the, so-called, deadlock or deadly-embrace problem. In this arliele we survey the work that has been done on the treatment of deadlocks from bolh the theoretical and practical points of view.
Bitmap indices are efficient for answering queries on low-cardinality attributes. In this article, we present a new compression scheme called Word-Aligned Hybrid (WAH) code that makes compressed bitmap indices efficient even for high-cardinality attributes. We further prove that the new compressed bitmap index, like the best variants of the B-tree index, is optimal for one-dimensional range queries. More specifically, the time required to answer a one-dimensional range query is a linear function of the number of hits. This strongly supports the well-known observation that compressed bitmap indices are efficient for multidimensional range queries because results of one-dimensional range queries computed with bitmap indices can be easily combined to answer multidimensional range queries. Our timing measurements on range queries not only confirm the linear relationship between the query response time and the number of hits, but also demonstrate that WAH compressed indices answer queries faster than the commonly used indices including projection indices, B-tree indices, and other compressed bitmap indices.
This paper presents two new strategies that can be used to greatly improve the speed of connected component labeling algorithms. To assign a label to a new object, most connected component labeling algorithms use a scanning step that examines some of its neighbors. The first strategy exploits the dependencies among them to reduce the number of neighbors examined. When considering 8-connected components in a 2D image, this can reduce the number of neighbors examined from four to one in many cases. The second strategy uses an array to store the equivalence information among the labels. This replaces the pointer based rooted trees used to store the same equivalence information. It reduces the memory required and also produces consecutive final labels. Using an array instead of the pointer based rooted trees speeds up the connected component labeling algorithms by a factor of 5 ∼ 100 in our tests on random binary images.
It is well established that bitmap indices are efficient for read-only attributes with a small number of distinct values. For an attribute with a large number of distinct values, the size of the bitmap index can be very large. To overcome this size problem, specialized compression schemes are used. Even though there is empirical evidence that some of these compression schemes work well, there has not been any systematic analysis of their effectiveness. In this paper, we analyze the time and space complexities of the two most efficient bitmap compression techniques known, the Byte-aligned Bitmap Code (BBC) and the Word-Aligned Hybrid (WAH) code, and study their performance on high cardinality attributes. Our analyses indicate that both compression schemes are optimal in time. The time and space required to operate on two compressed bitmaps are proportional to the total size of the two bitmaps. We demonstrate further that an in-place OR algorithm can operate on a large number of sparse bitmaps in time linear in their total size. Our analyses also show that the compressed indices are relatively small compared with commonly used indices such as B-trees. Given these facts, we conclude that bitmap index is efficient on attributes of low cardinalities as well as on those of high cardinalities. We also verify the analytical results with extensive tests, and identify an optimal way to combine different options to achieve the best performance. The test results confirm the linearity in the total size of the compressed bitmaps, and that WAH outperforms BBC by about a factor of two.
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