A number of techniques have been developed to facilitate powerful data retrieval on the Web and Semantic Web. Three categories of Web query languages can be distinguished, according to the format of the data they can retrieve: XML, RDF and Topic Maps. This article introduces the spectrum of languages falling into these categories and summarises their salient aspects. The languages are introduced using common sample data and query types. Key aspects of the query languages considered are stressed in a conclusion.

/pdf/web-and-semantic-web-query-languages-a-survey-3uzduf3p8e.pdf

Web and semantic web query languages: a survey

Graph processing has become an important part of multiple areas of computer science, such as machine learning, computational sciences, medical applications, social network analysis, and many others. Numerous graphs such as web or social networks may contain up to trillions of edges. Often, these graphs are also dynamic (their structure changes over time) and have domain-specific rich data associated with vertices and edges. Graph database systems such as Neo4j enable storing, processing, and analyzing such large, evolving, and rich datasets. Due to the sheer size of such datasets, combined with the irregular nature of graph processing, these systems face unique design challenges. To facilitate the understanding of this emerging domain, we present the first survey and taxonomy of graph database systems. We focus on identifying and analyzing fundamental categories of these systems (e.g., triple stores, tuple stores, native graph database systems, or object-oriented systems), the associated graph models (e.g., RDF or Labeled Property Graph), data organization techniques (e.g., storing graph data in indexing structures or dividing data into records), and different aspects of data distribution and query execution (e.g., support for sharding and ACID). 45 graph database systems are presented and compared, including Neo4j, OrientDB, or Virtuoso. We outline graph database queries and relationships with associated domains (NoSQL stores, graph streaming, and dynamic graph algorithms). Finally, we describe research and engineering challenges to outline the future of graph databases.

pdf/demystifying-graph-databases-analysis-and-taxonomy-of-data-16st3m75kt.pdf

Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries.

A system, method, and a computer program product for dynamically generating rules for XSL transformations by operating on an XML/XSL DOM representing a tree structure of an XML document in memory. User generated properties are received and transformed into XML/XSL nodes in memory. XML/XSL DOM is modified with the transformed XML/XSL nodes containing XSL rules of transformation with a variable dynamically generated by the user. The modified XML/XSL DOM is processed to generate a document in a specified format. Newly generated dynamic XSL rules override imported rules in the XML/XSL DOM.

XSL dynamic inheritance

In this paper, we present an incremental transformation framework called incXSLT. This framework has been experimented for the XSLT language defined at the World Wide Web Consortium. For the currently available tools, designing the XML content and the transformation sheets is an inefficient, a tedious and an error prone experience. Incremental transformation processors such as incXSLT represent a better alternative to help in the design of both the content and the transformation sheets. We believe that such frameworks are a first step toward fully interactive transformation-based authoring environments.

/pdf/an-incremental-xslt-transformation-processor-for-xml-1gs4h38ku1.pdf

An incremental XSLT transformation processor for XML document manipulation

A global education system, as a key area in future IT, has fostered developers to provide various learning systems with low cost. While a variety of e-learning advantages has been recognized for a long time and many advances in e-learning systems have been implemented, the needs for effective information sharing in a secure manner have to date been largely ignored, especially for virtual university collaborative environments. Information sharing of virtual universities usually occurs in broad, highly dynamic network-based environments, and formally accessing the resources in a secure manner poses a difficult and vital challenge. This paper aims to build a new rule-based framework to identify and address issues of sharing in virtual university environments through role-based access control (RBAC) management. The framework includes a role-based group delegation granting model, group delegation revocation model, authorization granting, and authorization revocation. We analyze various revocations and the impact of revocations on role hierarchies. The implementation with XML-based tools demonstrates the feasibility of the framework and authorization methods. Finally, the current proposal is compared with other related work.

Effective Collaboration with Information Sharing in Virtual Universities

From the Publisher:
"This book is for programmers who want to learn how to use the XSLT language for developing web applications. The book is in four parts: a detailed introduction to the concepts of the language, a reference section giving comprehensive specifications and working examples of every feature, a development guide giving design advice and case studies for the advanced user, and a product reference detailing the features and usage of the latest versions of Microsoft MSXML, Apache Xalan, Oracle XML, open source Saxon, the TRAX API and other processors and tools."--BOOK JACKET.

XSLT programmer's reference

Acknowledgments. Introduction. List of Examples. Chapter 1: XSLT in Context. Chapter 2: The XSLT Processing Model. Chapter 3: Stylesheet Structure. Chapter 4: Stylesheets and Schemas. Chapter 5: XSLT Elements. Chapter 6: Patterns. Chapter 7: XSLT Functions. Chapter 8: Extensibility. Chapter 9: Stylesheet Design Patterns. Chapter 10: Case Study: XMLSpec. Chapter 11: Case Study: A Family Tree. Chapter 12: Case Study: Knight's Tour. Appendix A: XPath 2.0 Syntax Summary. Appendix B: XPath Function Library. Appendix C: Microsoft XSLT Processors. Appendix D: JAXP: The Java API for Transformation. Appendix E: Saxon. Appendix F: Backwards Compatibility. Glossary. Index.

XSLT 2.0 Programmer's Reference

A Streaming XSLT Processor

This paper describes the internal features of the Saxon XQuery processor that make the most significant contribution to its speed of execution. For each of the features, an attempt is made to quantify the contribution, in most cases by comparing performance achieved when the feature is enabled or disabled.

http://sites.computer.org/debull/A08dec/saxonica.pdf

Ten Reasons Why Saxon XQuery is Fast.

You Pull, I’ll Push: on the Polarity of Pipelines

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Michael Kay

Author Tools

Chat about Author