Imam Machdi

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The advancement of multi-core processors technology has led to changing course of computing and enabled us to maximize the computing performance. In this study, we present a parallel TwigStack algorithm executed on a shared-memory multi-core system for achieving scalable query performance against large XML data. Our proposed scheme explores the following(More)
Parallel XML query processing systems that process numerous queries over large heterogeneous XML documents often experience under-performance due to workload imbalance and low CPU/system utilization, because conventional partitioning strategies cannot serve well for state-of-the-art query processing algorithms, such as holistic twig joins. Consequently,(More)
As traditional partitioning strategies do not serve well for semistructured data, partitioning and distributing heterogeneous XML documents onto a parallel cluster system have lead to such an intricacy issue for maintaining good query processing performance. In this paper, we propose a grid metadata model for XML that gives a conceptual view to partition(More)
The advancement of multi-core processor technology has changed the course of computing and enabled us to maximize the computing performance. In this study, we present an approach of task parallelism for the TwigStack algorithm on a multi-core system to find all occurrences of an XML query twig pattern in a large XML database. The TwigStack agorithm(More)
To achieve good performance of processing queries on huge XML data in cluster machines, data partitioning and placement strategy is one of the key factors. In this paper we propose a multidimensional data structure for maintaining XML data partitions, specifically for holistic twig join processing. Initially, we construct the multi-dimensional data(More)
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