https://github.com/constraintautomaton/introducing-collaborative-link-traversal-query-processing

Doctoral project proposal where I introduce Collaborative Link Data Query Processing, a paradigm where multiple query engines collaborate to improve query completeness and execution time in Link Traversal Query Processing

https://github.com/constraintautomaton/introducing-collaborative-link-traversal-query-processing

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distributed-computing linked-data p2p paper rdf solid sparql-query
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Doctoral project proposal where I introduce Collaborative Link Data Query Processing, a paradigm where multiple query engines collaborate to improve query completeness and execution time in Link Traversal Query Processing

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distributed-computing linked-data p2p paper rdf solid sparql-query
Created almost 3 years ago · Last pushed 11 months ago
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README.md

Introducing Collaborative Link Traversal Query Processing in the Context of Structured Decentralized Environments

Available as a web page at https://constraintautomaton.github.io/Introducing-Collaborative-Link-Traversal-Query-Processing/

Abstract

Decentralized web environments aim to give users data autonomy and control. Data sovereignty focus on two aspects: privacy and provider choice. However, the concept remains incomplete if it fails to incorporate the actual utilization of the data. Specifically, in the context of application functionality, data sovereignty can be relinquished to the owner of the computational units or applications. The exploration and retrieval of information are core functionalities of web-based social applications because it is from those mechanisms that shared experiences to foster interactions are created. A promising example of web discovery techniques is Link Traversal Query Processing (LTQP), a SPARQL query paradigm that aims at exploring the web to answer queries by following the links between documents. In my doctoral research, I introduce Collaborative Link Data Query Processing, a paradigm where multiple query engines collaborate to improve query result completeness and execution performance in LTQP. I divide the research on the cooperation of query engines into two parts: 1) Improving the completeness of results, by exploring more of the search space, and 2) reducing the potentially long query execution time by caching results. To validate this proposal, I will develop a prototype and evaluate it using existing benchmarks. Based on my analysis of the state of the art, previous studies have made contributions to collaborative SPARQL query execution and RDF peer-to-peer caching. However, there is currently a research gap regarding the investigation of such systems in the context of LTQP within a structured decentralized environment.

Editing the article

Development mode

bundle install bundle exec guard

Build

bundle install bundle exec nanoc compile

View on http://localhost:3000/

License

The code is licensed under the CC-BY-4.0 license. See the LICENSE file for details.

This article makes use of the ScholarMarkdown framework.

Owner

  • Name: Bryan-Elliott Tam
  • Login: constraintAutomaton
  • Kind: user
  • Location: Ghent, Belgium
  • Company: imec - Ghent University - IDLab

PhD Student working on querying for semantic web technologies

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