Salahadin Mohammed

Improved selectivity estimator for XML queries based on structural synopsis

With the increasing popularity of XML database applications, the use of efficient XML query optimizers is becoming very essential. The performance of an XML query optimizer depends heavily on the query selectivity estimators it uses to find the best possible query execution plan. In this work, we propose and evaluate a novel selectivity estimator, based on a structural synopsis, called SynopTech. The main idea of SynopTech is the generation of a summary tree by labeling the nodes of the source XML data tree using a fingerprint function and merging subtrees with similar structures. The generated summary tree is then used by SynopTech to estimate the selectivity of given queries. We experimented the proposed approach with four benchmark datasets of different structural characteristics and using different types of queries. Comparing with the Sampling algorithm, one of the state-of-the-art algorithms for selectivity estimations, SynopTech achieved lower selectivity estimation error rates, yet with very low memory budget. For example, for linear and existential queries, SynopTech had perfect estimations whereas the Sampling algorithm had an error rate of up to 70 %. For regular twig queries, SynopTech had a maximum error rate of 4.12 % whereas the Sampling algorithm had more than 55 %.

XHQE: A hybrid system for scalable selectivity estimation of XML queries

With the increasing popularity of XML applications in enterprise and big data systems, the use of efficient query optimizers is becoming very essential. The performance of an XML query optimizer depends heavily on the query selectivity estimators it uses to find the best possible query execution plan. In this work, we propose a novel selectivity estimator which is a hybrid of structural synopsis and statistics, called XHQE. The structural synopsis enhances the accuracy of estimation and the structural statistics makes it scalable to the allocated memory space. The structural synopsis is generated by labeling the nodes of the source XML dataset using a fingerprint function and merging subtrees with similar fingerprints (i.e. having similar structures). The generated structural synopsis and structural statistics are then used to estimate the selectivity of given queries. We studied the performance of the proposed approach using different types of queries and four benchmark datasets with different structural characteristics. We compared XHQE with existing algorithms such as Sampling, TreeSketch and one histogram-based algorithm. The experimental results showed that the XHQE is significantly better than other algorithms in terms of estimation accuracy and scalability for semi-uniform datasets. For non-uniform datasets, the proposed algorithm has comparable estimation accuracy to TreeSketch as the allocated memory size is highly reduced, yet the estimation data generation time of the proposed approach is much lower (e.g., TreeSketch took more than 50 times longer than that of the proposed approach for XMark dataset). Comparing to the histogram-based algorithm, our approach supports regular twig quires in addition to having higher accuracy when both run under similar memory constraints.

Selectivity estimation of extended XML query tree patterns based on prime number labeling and synopsis modeling

Abstract With the new era of big data and the proliferation of XML documents for representing and exchanging data over the web, selectivity estimation of XML query patterns has become a crucial component of database optimizers. It helps the optimizer choose the best possible plan for query evaluation. Existing selectivity estimators for XML queries can only support basic Query Tree Patterns (QTPs) with logical AND operator. In this paper, we propose a novel approach, called XQuest, for selectivity estimation that supports extended QTPs that may contain logical operators or wildcards. This approach is based on a modified implementation of prime number labeling to construct a structural summary model of the XML data. Subsequently, a simulator of an XML query evaluator runs on the resulting model from the previous stage and aggregates the estimate for each target QTP. We conducted several experiments to study the performance of the proposed approach on three XML benchmark datasets; in terms of synopsis generation time, storage requirements, and estimation accuracy. The results show that the proposed approach can have more accurate estimates with low memory and time requirements. For example, when compared to a Sampling algorithm with the same allocated memory budget, the error rate of the proposed approach never reached 5% whereas it reached 98.5% for the Sampling algorithm.

Novel scheme for labeling XML trees based on bits-masking and logical matching

The eXtensible Mark-up Language rapidly has become a very powerful standard for the data exchange. Labeling schemes have been introduced to optimize data retrieval and query processing on XML database documents. This is done by providing labels that hold information about XML tree nodes. In this paper we introduce a novel labeling scheme XDAS whose labeling technique is inspired by IP addressing and subnetting technique used in computer networks. This technique is used when dividing a network into several sub-networks. Each sub-network is assigned a subnet mask that helps in identifying the parent network. So, this labeling scheme treats XML documents as a network with sub-networks and assigns labels for XML tree nodes using the masking technique. Experimental results show that XDAS, when compared to Dewey and Range labeling schemes, provides an efficient label size, disk space required to store labels and matching time required to identify relationships between nodes.

A modulo-based labeling scheme for dynamically ordered XML trees

XML is becoming the de facto standard for exchanging and querying documents over the Web. Many XML query languages such as XQuery and XPath use label paths to traverse the irregularly structured XML data. Several labeling schemes have been proposed to identify the structural relationships in the tree, as well as to support the incremental updates at a low cost. In this paper, we conduct a comprehensive survey for labeling XML trees, and classify these schemes according to their labeling mechanism. We also propose a novel modulo-based labeling scheme that uses modular arithmetic operations and numbering theory to label the XML tree. Our algorithm labels nodes in the tree in a way, similar to the encryption-decryption function using modular multiplication and a prime modulo. We show that our algorithm supersedes other XML labeling schemes by having a smaller space size for the node label regardless of the fan-out or the depth of the tree, and completely eliminates the need to re-label the whole XML tree in case of future insertions.