System RX

Beyer, Kevin; Cochrane, Roberta; Josifovski, Vanja; Kleewein, Jim; Lapis, George; Lohman, Guy M.; Lyle, Bob; Özcan, Fatma; Pirahesh, Hamid; Seemann, Normen; Truong, T. V.; Linden, Bart van der; Vickery, Brian; Zhang, Chun

doi:10.1145/1066157.1066197

Cited by 91 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notice that each subquery is evaluated from the root of the XML tree, and that therefore its leading axis should be ''//''. 7 Each resulting subquery is a suffix path query and is evaluated as a selection on node P-labels. Suppose the evaluation of Notice that we have pEntry.level ¼ refinfo.level -2 in the where clause, which is different from the general D-join where no level predicate is specified.…”

Section: Split Algorithmmentioning

confidence: 99%

“…Another related approach is to record the ''reversed'' representation of the source path of each XML node and process a suffix path query using ''prefix match'' of the reversed source paths [7,39]. The results of the subqueries are joined based on node ancestor-descendant relationships using Dewey labeling or its variant [38].…”

Section: Related Workmentioning

confidence: 99%

“…One general approach, which has been proposed and studied in academia [8,13,15,20,21,24,26,34,41,44] as well as by major database vendors [7,35,39], is to design a framework for XML data processing that leverages relational database technology. In this framework, XML nodes are stored in table form with B+-tree indexes built on it, and XPath expressions are logically rewritten to SQL queries with joins implementing axis traversals.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A bi-labeling based XPath processing system

Chen

Davidson

Zheng

2010

Information Systems

View full text Add to dashboard Cite

We present BLAS, a Bi-LAbeling based XPath processing System. BLAS uses two labeling schemes to speed up query processing: P-labeling for processing consecutive child (or parent) axis traversals, and D-labeling for processing descendant (or ancestor) axis traversals. XML data are stored in labeled form and indexed. Algorithms are presented for translating XPath queries to SQL expressions. BLAS reduces the number of joins in the SQL query translated from a given XPath query and reduces the number of disk accesses required to execute the SQL query compared with the traditional XPath processing using D-labeling alone. We also propose an approximate P-labeling scheme and the corresponding query translation algorithm to handle XML data trees that contain a large number of distinct tag names, and/or are very deep. This extension captures a spectrum of XPath-to-SQL query translation schemes, ranging from existing schemes that do not use P-labels to the one that uses exact P-labels. Experimental results demonstrate the efficiency of the BLAS system. t r a c tWe present BLAS, a Bi-LAbeling based XPath processing System. BLAS uses two labeling schemes to speed up query processing: P-labeling for processing consecutive child (or parent) axis traversals, and D-labeling for processing descendant (or ancestor) axis traversals. XML data are stored in labeled form and indexed. Algorithms are presented for translating XPath queries to SQL expressions. BLAS reduces the number of joins in the SQL query translated from a given XPath query and reduces the number of disk accesses required to execute the SQL query compared with the traditional XPath processing using D-labeling alone. We also propose an approximate P-labeling scheme and the corresponding query translation algorithm to handle XML data trees that contain a large number of distinct tag names, and/or are very deep. This extension captures a spectrum of XPath-to-SQL query translation schemes, ranging from existing schemes that do not use P-labels to the one that uses exact P-labels. Experimental results demonstrate the efficiency of the BLAS system.

show abstract

Section: Split Algorithmmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A bi-labeling based XPath processing system

Chen

Davidson

Zheng

2010

Information Systems

View full text Add to dashboard Cite

show abstract

“…Such a transformation falls in an active area of research (e.g., [40,41]), and is implemented as an additional component within the query compiler. Even though, the transformation into OQL does not limit the scalability of the system that it is inherently scalable due to the P2P nature of XMAP and the OGSA-DQP's support for parallelism.…”

Section: System Functionalitiesmentioning

confidence: 99%

A service-oriented system for distributed data querying and integration on Grids

Comito

Gounaris

Sakellariou

et al. 2009

Future Generation Computer Systems

View full text Add to dashboard Cite

a b s t r a c tData Grids rely on the coordinated sharing of and interaction across multiple autonomous database management systems. They provide transparent access to heterogeneous and autonomous data resources stored on Grid nodes. Data sharing tools for Grids must include both distributed query processing and data integration functionality. This paper presents the implementation of a data sharing system that (i) is tailored to data Grids, (ii) supports well established and widely spread relational DBMSs, and (iii) adopts a hybrid architecture by relying on a peer model for query reformulation to retrieve semantically equivalent expressions, and on a wrapper-mediator integration model for accessing and querying distributed data sources. The system builds upon the infrastructure provided by the OGSA-DQP distributed query processor and the XMAP query reformulation algorithm. The paper discusses the implementation methodology, and presents empirical evaluation results.

show abstract

“…These strategies do not use native layout of semi-structured data and are limited to the generic optimization strategies built into relational databases and file systems. The problem of native storage of semi-structured data has been addressed in Natix [Kanne and Moerkotte 1999;Kanne et al 2005] and in System RX [Beyer et al 2005], where the tree-structured data is split into pages and each page is stored in a disk block, thereby reducing the number of read accesses while traversing the tree. OrientStore [Meng et al 2003] uses schema information to make a storage plan for the semi-structured data.…”

Section: Related Workmentioning

confidence: 99%

Storing semi-structured data on disk drives

et al. 2009

View full text Add to dashboard Cite

Applications that manage semi-structured data are becoming increasingly commonplace. Current approaches for storing semi-structured data use existing storage machinery -they either map the data to relational databases, or use a combination of flat files and indexes. While employing these existing storage mechanisms provide readily available solutions, there is a need to more closely examine their suitability to this class of data. Particularly, retrofitting existing solutions for semi-structured data can result in a mismatch between the tree structure of the data and the access characteristics of the underlying storage device (disk drive). This study explores various possibilities in the design space of native storage solutions for semi-structured data by exploring alternative approaches that match application data access characteristics to those of the underlying disk drive. For evaluating the effectiveness of the proposed native techniques in relation to the existing solution, we experiment with XML data using the XPathMark benchmark. Extensive evaluation reveals the strengths and weaknesses of the proposed native data layout techniques. While the existing solutions work really well for deep-focused queries into a semi-structured document (those that result in retrieving entire subtrees), the proposed native solutions substantially outperform for the non-deep-focused queries, which we demonstrate are at least as important as the deep-focused. We believe that native data layout techniques offer a unique direction for improving the performance of semi-structured data stores for a variety of important workloads. However, given that the proposed native techniques require circumventing current storage stack abstractions, further investigation is warranted before they can be applied to general purpose storage systems.

show abstract

System RX

Cited by 91 publications

References 28 publications

A bi-labeling based XPath processing system

A bi-labeling based XPath processing system

A service-oriented system for distributed data querying and integration on Grids

Storing semi-structured data on disk drives

Contact Info

Product

Resources

About