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

作     者：Della Valle, Emanuele Schlobach, Stefan Kroetzsch, Markus Bozzon, Alessandro Ceri, Stefano Horrocks, Ian 

作者机构：Politecn Milan DEI I-20133 Milan Italy Vrije Univ Amsterdam Amsterdam Netherlands Univ Oxford Oxford England 

出 版 物：《SEMANTIC WEB》 (Semantic Web)

年 卷 期：2013年第4卷第2期

页      面：219-231页

核心收录：

基　　金：ERC Search Computing (SeCo) project Dutch national program COMMIT EU FP7 project SEALS EPSRC EPSRC [EP/H051511/1] Funding Source: UKRI 

主　　题：Massive data inference ordering streaming algorithms 

摘      要：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.