Towards an enterprise XML architecture

Murthy, Ravi; Liu, Zhen Hua; Krishnaprasad, Muralidhar; Chandrasekar, Sivasankaran; Tran, Anh-Tuan; Sedlar, Eric; Florescu, Daniela; Kotsovolos, Susan; Agarwal, Nipun; Arora, Vikas; Krishnamurthy, Viswanathan

doi:10.1145/1066157.1066300

Cited by 29 publications

(32 citation statements)

References 4 publications

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“…System RX [11] uses XML-specific storage, an XQuery compiler, and XML indexes. XML DB [41] calibrates XML storage between CLOBs and objects "shredded" to rows. Oracle [38] and SAP [16] also discuss extending an RDBMS with a JSON datatype.…”

Section: Related Workmentioning

confidence: 99%

Fast queries over heterogeneous data through engine customization

2016

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Industry and academia are continuously becoming more data-driven and data-intensive, relying on the analysis of a wide variety of heterogeneous datasets to gain insights. The different data models and formats pose a significant challenge on performing analysis over a combination of diverse datasets. Serving all queries using a single, general-purpose query engine is slow. On the other hand, using a specialized engine for each heterogeneous dataset increases complexity: queries touching a combination of datasets require an integration layer over the different engines.This paper presents a system design that natively supports heterogeneous data formats and also minimizes query execution times. For multi-format support, the design uses an expressive query algebra which enables operations over various data models. For minimal execution times, it uses a code generation mechanism to mimic the system and storage most appropriate to answer a query fast. We validate our design by building Proteus, a query engine which natively supports queries over CSV, JSON, and relational binary data, and which specializes itself to each query, dataset, and workload via code generation. Proteus outperforms state-of-the-art opensource and commercial systems on both synthetic and real-world workloads without being tied to a single data model or format, all while exposing users to a single query interface.

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Section: Related Workmentioning

confidence: 99%

Fast queries over heterogeneous data through engine customization

2016

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“…Oracle XMLDB addresses both data centric and document centric XML [7] use cases. On one end of the spectrum, data centric XML is very structured and bound with a rigid XML Schema.…”

Section: Overview Of Xml Storage and Index In Oracle Xmldbmentioning

confidence: 99%

“…The other approach is to build a hybrid XML/SQL database. Major RDBMS vendors have built hybrid XQuery and SQL/XML engines in their RDBMS products so that relational data and XML data can be managed and queried on one platform [7,8,9,30] with both XQuery and SQL/XML as query languages.…”

Section: Introductionmentioning

confidence: 99%

Towards a physical XML independent XQuery/SQL/XML engine

et al. 2008

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There has been a lot of research and industrial effort on building XQuery engines with different kinds of XML storage and index models. However, most of these efforts focus on building either an efficient XQuery engine with one kind of XML storage, index, view model in mind or a general XQuery engine without any consideration of the underlying XML storage, index and view model. We need an underlying framework to build an XQuery engine that can work with and provide optimization for different XML storage, index and view models. Besides XQuery, RDBMSs also support SQL/XML, a standard language that integrates XML and relational processing. There are industrial efforts for building hybrid XQuery and SQL/XML engines that support both languages so that users can manage and query both relational and XML data on one platform. However, we need a theoretical framework to optimize both SQL/XML and XQuery languages in one RDBMS. In this paper, we show our industrial work of building a combined XQuery and SQL/XML engine that is able to work and provide optimization for different kinds of XML storage and index models in Oracle XMLDB. This work is based on XML extended relational algebra as the underlying tuple-based logical algebra and incorporates tree and automata based physical algebra into the logical tuple-based algebra so as to provide optimization for different physical XML formulations. This results in logical and physical rewrite techniques to optimize XQuery and SQL/XML over a variety of physical XML storage, index and view models, including schema aware object relational XML storage with relational indexes, binary XML storage with schema agnostic path-value-order key XMLIndex, SQL/XML view over relational data and relational view over XML. Furthermore, we show the approach of leveraging cost based XML physical rewrite strategy to evaluate different physical rewrite plans.

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“…We used XML data types (Krishnaprasad et al, 2005;Murthy et al, 2005;Rys, 2005;Pal et al, 2005Pal et al, , 2006Ozcan et al, 2006;ISO/IEC, 2003Eisenberg & Melton, 2004) within our hybrid model to represent the semi-structured part because structural irregularities can cause structure mapping to generate large complex schemas, and schema inflexibility becomes problematic for structurally volatile XML data. Other problems avoided by using XML data types are the need to resolve naming and type ambiguities and contentions, and, more generally, XML structural directives, such as Or ('|'), cannot be mapped naturally into the relational model (Yoshikawa & Amagasa, 2001).…”

Section: Experimental Designmentioning

confidence: 99%

An Analysis of Relational Storage Strategies for Partially Structured XML

Kader¹,

Eaglestone²,

North³

2008

Proceedings of the Fourth International Conference on Web Information Systems and Technologies

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Abstract:This paper presents a performance analysis of strategies for storing XML data sets in relational databases, focusing on XML datasets that are a combination of structured and semi-structured data. The analysis demonstrates advantages of a hybrid approach combining structure mapping and XML data type instances.However problems remain with current technology with regards to scaling of the approach for large data sets. Also, anomalous results are identified and a threshold at which the cost of data shredding out weighs the advantages of structure mapping.

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Towards an enterprise XML architecture

Cited by 29 publications

References 4 publications

Fast queries over heterogeneous data through engine customization

Fast queries over heterogeneous data through engine customization

Towards a physical XML independent XQuery/SQL/XML engine

An Analysis of Relational Storage Strategies for Partially Structured XML

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