Semantic Query Optimization in an Automata-Algebra Combined XQuery Engine over XML Streams

Su, Hong; Rundensteiner, Elke A.; Mani, Murali

doi:10.1016/b978-012088469-8.50130-3

Cited by 22 publications

(27 citation statements)

References 11 publications

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“…One alternative approach to using an automaton to detect PNO constraint at runtime is to introduce new patterns into the pattern retrieval itself [SRM05]. For instance, the appearance of any keyword can serve as source's ending mark in Example 1.…”

Section: Applying Ending Marksmentioning

confidence: 99%

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Semantic query optimization for processing XML streams with minimized memory footprint

Mani

Rundensteiner

2008

Proceedings of the 2008 EDBT Workshop on Database Technologies for Handling XML Information on the Web

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XML streams have become increasingly prevalent in modern applications, ranging from network traffic monitoring to real-time information publishing. XQuery evaluation over XML streams requires the temporary buffering of XML elements, which not only utilizes system buffer and CPU resources but also causes un-necessary output latency. This thesis presents a semantic query optimization solution to minimize memory footprint during XQuery evaluation by exploiting XML schema knowledge. In many practical applications, XML streams are generated conforming to pre-defined schema constraints typically expressed via a DTD or an XML schema specification. Utilizing such constraints enables us to on-the-fly predict the non-occurrence of a given pattern within a bound context. This helps us to release buffered data earlier or possibly avoid ever storing it, thus achieving a minimized memory footprint. In this work, we focus on one particular class of constraints, namely, the Pattern Non-Occurrence (PNO) constraint. We develop an automatonbased technique to detect PNO constraints at runtime. For a given query, optimization opportunities which can be triggered by runtime PNO detection are explored for memory footprint minimization. Optimization decisions are encoded using our proposed Condition-Action Graph (CAG). The optimization-embedded execution strategy is then proposed to execute an optimized plan by detecting PNO constraints at run-time and then triggering the corresponding encoded actions when certain predefined conditions are satisfied. To ensure the efficiency of such PNOtriggered optimization, we propose a method for shrinking the CAGs by utilizing constraint knowledge during the query plan compiling phase. We implement our optimization technique within the Raindrop XQuery engine. Our system implementation processes XQuery utilizing the Raindrop algebra. It is efficiently augmented by our optimization module, which uses Glushkov automaton technique to capture and monitor PNO constraints in parallel with the query-driven pattern retrieval. Finally, we conduct experimental studies using both real and synthetic data streams to illustrate that our techniques bring significant performance improvement in both memory and CPU usage as well as improved output latency over state-of-the-art solutions, with little overhead.2

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Section: Applying Ending Marksmentioning

confidence: 99%

“…Simply trying to detect the appearance of a certain pattern type [SRM05] cannot be a generic approach, as it doesn't fully use the semantic knowledge of the XML schema. Instead a runtime strategy is needed to detect such constraint knowledge about pattern completeness at runtime.…”

mentioning

confidence: 99%

Semantic query optimization for processing XML streams with minimized memory footprint

Mani

Rundensteiner

2008

Proceedings of the 2008 EDBT Workshop on Database Technologies for Handling XML Information on the Web

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“…This is very hard even for the simplest XQuery constructs, such as sequence concatenation and element construction. A notable exception that avoids the pitfalls of integrating these two incompatible models (automata and the functional paradigm) is the Raindrop project [17], which decomposes a query into two parts: one made out of automata that processes all the paths in a query and another based on algebraic techniques that processes the rest of the query. Another hybrid approach that incorporates streaming techniques into an existing stored XML database system (Galax) is by Fernandez, et al [5].…”

Section: Related Workmentioning

confidence: 99%

Efficient Processing of XML Update Streams

Fegaras

2008

2008 IEEE 24th International Conference on Data Engineering

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Abstract-This paper introduces a framework for processing continuous, exact queries over continuous update XML streams. Instead of eagerly performing the updates on cached portions of the stream, we propagate the updates through the query evaluation pipeline, all the way to the result display, which prints the query answers. That way, the result display prints the query results continuously, replacing old results with new. The novelty of our approach is in the use of this processing framework to unblock operations and reduce buffering by letting the operations themselves embed new updates into the stream that retroactively perform the blocking parts of the operation. Based on this framework, we present novel methods for unblocking a number of important blocking/unbounded stream operations in XQuery using a small memory footprint, such as concatenation, general predicates, descendant-or-self and backward axes, and sorting.

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“…Another XPath stream-querying system XAOS [6] evaluates XPath queries with the parent and ancestor axes (see Section 1.2.1), as well as the commonly used "/" and "//" axes. Recently, some XQuery stream-querying systems have also been developed such as XSM [80], BEA/XQRL [39], FluX [71], Raindrop [107], and FlowGraph [75]. Some of these systems, including XSM, FLuX, and Raindrop, also use the available DTD (Section 3.4) schema information to improve the stream-querying performance and reduce the size of the runtime buffering space.…”

Section: The Navigational Approachmentioning

confidence: 99%

Efficiently Querying Large XML Data Repositories: A Survey

Gou

Chirkova

2007

IEEE Trans. Knowl. Data Eng.

139

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Abstract-Extensible Markup Language (XML) is emerging as a de facto standard for information exchange among various applications on the World Wide Web. There has been a growing need for developing high-performance techniques to query large XML data repositories efficiently. One important problem in XML query processing is twig pattern matching, that is, finding in an XML data tree D all matches that satisfy a specified twig (or path) query pattern Q. In this survey, we review, classify, and compare major techniques for twig pattern matching.1 Specifically, we consider two classes of major XML query processing techniques: the relational approach and the native approach. The relational approach directly utilizes existing relational database systems to store and query XML data, which enables the use of all important techniques that have been developed for relational databases, whereas in the native approach, specialized storage and query processing systems tailored for XML data are developed from scratch to further improve XML query performance. As implied by existing work, XML data querying and management are developing in the direction of integrating the relational approach with the native approach, which could result in higher query processing performance and also significantly reduce system reengineering costs.Index Terms-XML query processing, twig pattern matching.

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Semantic Query Optimization in an Automata-Algebra Combined XQuery Engine over XML Streams

Cited by 22 publications

References 11 publications

Semantic query optimization for processing XML streams with minimized memory footprint

Semantic query optimization for processing XML streams with minimized memory footprint

Efficient Processing of XML Update Streams

Efficiently Querying Large XML Data Repositories: A Survey

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