Building XML statistics for the hidden web

Aboulnaga, Ashraf; Naughton, Jeffrey F.

doi:10.1145/956927.956930

Cited by 5 publications

(11 citation statements)

References 3 publications

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“…Aboulnaga et al [7,8] present a technique that estimates the selectivity of individual steps within a path expression based on a compact structural synopsis of the data. While this technique is primarily aimed at finding the best centralized query plan for evaluating a given query (making it useful for optimizing the sub-queries encountered by distributed query evaluation), it can also be used to obtain cardinality estimates for the overall query result.…”

Section: Cardinalitymentioning

confidence: 99%

“…In this work, the logical properties of an LQP are the properties that are the same for any LQP p To obtain card(p u k ), existing cardinality estimation techniques for the centralized evaluation of XML queries can be applied directly (e.g., [143,38,7,135,136,8,55,131]). An overview of these techniques is provided in Section 3.3.1.2.…”

Section: Logical Lqp Propertiesmentioning

confidence: 99%

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Distributed XML Query Processing

Özsu

Kling

2010

Database and XML Technologies

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While centralized query processing over collections of XML data stored at a single site is a well understood problem, centralized query evaluation techniques are inherently limited in their scalability when presented with large collections (or a single, large document) and heavy query workloads. In the context of relational query processing, similar scalability challenges have been overcome by partitioning data collections, distributing them across the sites of a distributed system, and then evaluating queries in a distributed fashion, usually in a way that ensures locality between (sub-)queries and their relevant data. This thesis presents a suite of query evaluation techniques for XML data that follow a similar approach to address the scalability problems encountered by XML query evaluation.Due to the significant differences in data and query models between relational and XML query processing, it is not possible to directly apply distributed query evaluation techniques designed for relational data to the XML scenario. Instead, new distributed query evaluation techniques need to be developed. Thus, in this thesis, an end-to-end solution to the scalability problems encountered by XML query processing is proposed.Based on a data partitioning model that supports both horizontal and vertical fragmentation steps (or any combination of the two), XML collections are fragmented and distributed across the sites of a distributed system. Then, a suite of distributed query evaluation strategies is proposed. These query evaluation techniques ensure locality between each fragment of the collection and the parts of the query corresponding to the data in this fragment. Special attention is paid to scalability and query performance, which is achieved by ensuring a high degree of parallelism during distributed query evaluation and by avoiding access to irrelevant portions of the data.For maximum flexibility, the suite of distributed query evaluation techniques proposed in this thesis provides several alternative approaches for evaluating a given query over a given distributed collection. Thus, to achieve the best performance, it is necessary to predict and compare the expected performance of each of these alternatives. In this work, this is accomplished through a query optimization technique based on a distribution-aware cost model. The same cost model is also used to fine-tune the way a collection is fragmented to the demands of the query workload evaluated over this collection. v To evaluate the performance impact of the distributed query evaluation techniques proposed in this thesis, the techniques were implemented within a production-quality XML database system. Based on this implementation, a thorough experimental evaluation was performed. The results of this evaluation confirm that the distributed query evaluation techniques introduced here lead to significant improvements in query performance and scalability both when compared to centralized techniques and when compared to existing distributed query evaluation tec...

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Section: Cardinalitymentioning

confidence: 99%

Section: Logical Lqp Propertiesmentioning

confidence: 99%

Distributed XML Query Processing

Özsu

Kling

2010

Database and XML Technologies

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“…There are many approaches dealing with cardinality estimation for path queries (e.g., [5,4,1,6,11,2,8,10]). Some of them [5,1,11,10] focus only on a subset of the possible path expressions, e.g., simple paths (linear chain of steps that are connected by /-axis) or linear paths containing //-axes.…”

Section: Related Workmentioning

confidence: 99%

“…The hyper-edge table has been inspired by previous proposals [2,10]. In [2], the actual statistics of previous queries are recorderd into a table and reused later.…”

Section: Related Workmentioning

confidence: 99%

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XSEED: Accurate and Fast Cardinality Estimation for XPath Queries

Zhang

Özsu

Aboulnaga

et al. 2006

22nd International Conference on Data Engineering (ICDE'06)

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We propose XSEED, a synopsis of path queries for cardinality estimation that is accurate, robust, efficient, and adaptive to memory budgets. XSEED starts from a very small kernel, and then incrementally updates information of the synopsis. With such an incremental construction, a synopsis structure can be dynamically configured to accommodate different memory budgets. Cardinality estimation based on XSEED can be performed very efficiently and accurately. Extensive experiments on both synthetic and real data sets show that even with less memory, XSEED could achieve accuracy that is an order of magnitude better than that of other synopsis structures. The cardinality estimation time is under 2% of the actual querying time for a wide range of queries in all test cases.

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

2015

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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 nonuniform 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.

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Building XML statistics for the hidden web

Cited by 5 publications

References 3 publications

Distributed XML Query Processing

Distributed XML Query Processing

XSEED: Accurate and Fast Cardinality Estimation for XPath Queries

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

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