Computing graphical queries over XML data

Author:

Comai Sara1,Damiani Ernesto2,Fraternali Piero1

Affiliation:

1. Politecnico di Milano, Milano, Italy

2. Universitá di Milano---Polo di Crema, Italy

Abstract

The rapid evolution of XML from a mere data exchange format to a universal syntax for encoding domain-specific information raises the need for new query languages specifically conceived to address the characteristics of XML. Such languages should be able not only to extract information from XML documents, but also to apply powerful transformation and restructuring operators, based on a well-defined semantics. Moreover, XML queries should be natural to write and understand, as nontechnical persons also are expected to access the large XML information bases supporting their businesses. This article describes XML-GL, a graphical query language for XML data. XML-GL's uniqueness is in the definition of a graph-based syntax to express a wide variety of XML queries, ranging from simple selections to expressive data transformations involving grouping, aggregation, and arithmetic calculations. XML-GL has an operational semantics based on the notion of graph matching, which serves as a guideline both for the implementation of native processors, and for the adoption of XML-GL as a front-end to any of the XML query languages that are presently under discussion as the standard paradigm for querying XML data.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Science Applications,General Business, Management and Accounting,Information Systems

Cited by 33 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. FLAG: Towards Graph Query Autocompletion for Large Graphs;Data Science and Engineering;2022-04-16

2. GFocus: User Focus-based Graph Query Autocompletion;IEEE Transactions on Knowledge and Data Engineering;2020

3. Human Interaction with Graphs: A Visual Querying Perspective;Synthesis Lectures on Data Management;2018-08-08

4. Visual Interaction;Encyclopedia of Database Systems;2018

5. AutoG: a visual query autocompletion framework for graph databases;The VLDB Journal;2017-01-27

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