Order matters! Harnessing a world of orderings for reasoning over massive data

Emanuele Della Valle, Stefan Schlobach, Markus Krötzsch, Alessandro Bozzon, Stefano Ceri, Ian Horrocks

Research output: Contribution to journalArticleScientificpeer-review

19 Citations (Scopus)


More and more applications require real-time processing of massive, dynamically generated, ordered data; order is an essential factor as it reflects recency or relevance. Semantic technologies risk being unable to meet the needs of such applications, as they are not equipped with the appropriate instruments for answering queries over massive, highly dynamic, ordered data sets. In this vision paper, we argue that some data management techniques should be exported to the context of semantic technologies, by integrating ordering with reasoning, and by using methods which are inspired by stream and rank-aware data management. We systematically explore the problem space, and point both to problems which have been successfully approached and to problems which still need fundamental research, in an attempt to stimulate and guide a paradigm shift in semantic technologies.
Original languageUndefined/Unknown
Pages (from-to)219-231
Number of pages13
JournalSemantic Web: interoperability, usability, applicability
Issue number2
Publication statusPublished - 2013
Externally publishedYes


  • Massive data
  • inference
  • ordering
  • streaming algorithms

Cite this