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- item-2 hasContent taha-osman.
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- item-4 hasContent phil-lakin.
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- 10 creator daniele-braga.
- 10 creator davide-francesco-barbieri.
- 10 creator emanuele-della-valle.
- 10 creator michael-grossniklaus.
- 10 creator stefano-ceri.
- 10 type InProceedings.
- 10 label "Incremental Reasoning on Streams and Rich Background Knowledge".
- 10 sameAs 10.
- 10 abstract "Innovative context aware mobile applications require performing complex reasoning tasks that combine streaming information with rich background knowledge. For instance, from a stream of quantitative information about latitude and longitude such applications need to derive qualitative information about places like home, office, or gym. Then, rising up the level of abstraction, they need to reason about concrete problems, such as deciding how to reach such places, which means of transportation to choose, how to attend ongoing events nearby, or how to skip traffic jams. This article presents a technique for Stream Reasoning consisting in incremental maintenance of materializations of ontological entailments in presence of streaming information. In this article we elaborate on previous papers that extend to logic programming results from incremental maintenance of materialized views in deductive databases. Our contribution is a new technique that takes in explicit consideration the order in which streaming information arrives to the Stream Reasoner. By adding time validity information to each RDF statement, we show that is possible to compute a new complete and correct materialization by (a) simply dropping explicit statements and entailments that are no longer valid, and (b) evaluating a maintenance program that propagates changes in explicit RDF statements to the stored implicit entailments. In this paper, we also provide experimental evidence that our approach significantly reduces the time required to compute the new materialization, and that our approach opens up for several further optimizations.".
- 10 hasAuthorList authorList.
- 10 isPartOf proceedings.
- 10 keyword "Context-aware".
- 10 keyword "Incremental Maintenance".
- 10 keyword "Materialization".
- 10 keyword "Reasoning".
- 10 keyword "Stream".
- 10 title "Incremental Reasoning on Streams and Rich Background Knowledge".
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- item-1 hasContent davide-francesco-barbieri.
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- item-2 hasContent daniele-braga.
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- 12 creator eugenio-di-sciascio.
- 12 creator floriano-scioscia.
- 12 creator michele-ruta.
- 12 type InProceedings.
- 12 label "Mobile Semantic-based Matchmaking: a fuzzy DL approach".
- 12 sameAs 12.
- 12 abstract "Novel wireless handheld devices allow the adoption of revised and adapted discovery approaches originally devised for the Semantic Web in mobile ad-hoc networks. Nevertheless, resource constraints of such devices require an accurate re-design of frameworks and algorithms to efficiently support mobile users. The paper focuses on an implementation of concept abduction and contraction algorithms in (fuzzy) ALND DL settings to perform semantic matchmaking and provide logical explanation services. OWL-DL Knowledge Bases have been properly exploited to enable standard and non-standard inference services. The proposed framework has been implemented and tested in a fire hazards prevention case study: early experimental results are reported.".
- 12 hasAuthorList authorList.
- 12 isPartOf proceedings.
- 12 keyword "Description Logics".
- 12 keyword "Matchmaking".
- 12 keyword "Mobile Ad-hoc Networks".
- 12 keyword "Semantic Web".
- 12 keyword "Ubiquitous computing".
- 12 title "Mobile Semantic-based Matchmaking: a fuzzy DL approach".
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- item-1 hasContent michele-ruta.
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- item-2 hasContent floriano-scioscia.
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- 13 creator christine-parent.
- 13 creator dipanjan-chakraborty.
- 13 creator stefano-spaccapietra.
- 13 creator zhixian-yan.
- 13 type InProceedings.
- 13 label "A Hybrid Model and Computing Platform for Spatio-Semantic Trajectories".
- 13 sameAs 13.
- 13 abstract "Research in spatio-temporal data mangement has progressed significantly towards efficient storage and indexing of mobility data. Typically such mobility data analytics is assumed to follow the model of a stream of (x,y,t) points, usually coming from GPS-enabled mobile devices. With large-scale adoption of GPS-driven systems in several application sectors (shipment tracking to geo-social networks), there is a growing demand from applications to understand the {\it spatio-semantic} behavior of mobile entities. {\it Spatio-semantic} behavior essentially means a semantic (and preferably contextual) abstraction of raw spatio-temporal location feeds. The core contribution of this paper lies in presenting a Hybrid Model and Computing Platform for developing a semantic overlay - analyzing and transforming raw mobility data (GPS) to meaningful semantic abstractions, starting from raw feeds to semantic trajectories. Secondly, we analyze large-scale GPS data using our computing platform and present results of extracted spatio-semantic trajectories. This impacts a large class of mobile applications requiring such semantic abstractions over streaming location feeds in real systems today.".
- 13 hasAuthorList authorList.
- 13 isPartOf proceedings.
- 13 keyword "GPS feeds".
- 13 keyword "computing platform".
- 13 keyword "mobility data".
- 13 keyword "semantic trajectory".
- 13 title "A Hybrid Model and Computing Platform for Spatio-Semantic Trajectories".