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Matches in ScholarlyData for { ?s ?p ?o. }

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• item-1 hasContent rafael-s-goncalves.
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• item-2 hasContent nicolas-matentzoglu.
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• item-3 hasContent bijan-parsia.
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• item-4 hasContent uli-sattler.
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• paper-67 type InProceedings.
• paper-67 label "Network-Aware Workload Scheduling for Scalable Linked Data Stream Processing".
• paper-67 sameAs paper-67.
• paper-67 abstract "In order to cope with the ever-increasing data volume, distributed stream processing systems have been proposed. To ensure scalability most distributed systems partition the data and distribute the workload among multiple machines. This approach does, however, raise the question how the data and the workload should be partitioned and distributed. A uniform scheduling strategy---a uniform distribution of computation load among available machines---typically used by stream processing systems, disregards network-load as one of the major bottlenecks for throughput resulting in an immense load in terms of inter-machine communication. We propose a graph-partitioning based approach for workload scheduling within stream processing systems.We implemented a distributed triple-stream processing engine on top of the Storm realtime computation framework and evaluate its communication behavior using two real-world datasets. We show that the application of graph partitioning algorithms can decrease inter-machine communication substantially (by 40% to 99%) whilst maintaining an even workload distribution, even using very limited data statistics. We also find that processing RDF data as single triples at a time rather than graph fragments (containing multiple triples), may decrease throughput indicating the usefulness of semantics.".
• paper-67 hasAuthorList authorList.
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• paper-67 title "Network-Aware Workload Scheduling for Scalable Linked Data Stream Processing".
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• item-1 hasContent lorenz-fischer.
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• item-2 hasContent thomas-scharrenbach.
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• item-3 hasContent abraham-bernstein.
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• paper-68 type InProceedings.
• paper-68 label "Semantic Enrichment of Mobile Phone Data Records Using Linked Open Data".
• paper-68 sameAs paper-68.
• paper-68 abstract "The pervasivity of mobile phones opens an unprecedented opportunityof deepening into the human dynamics through the analysis of the data they generate.This enables a novel human-driven approach to service creation in a wideset of domains such as health-care, transportation and urban safety. The telecomoperators own and manage billions of mobile network events (like the Call DetailedRecords - CDR) per day: the interpretation of such a big stream of dataneeds a deep understanding of the context where the events have occurred. Theexploitation of available background knowledge is a key element in this scenario.In this paper we introduce a novel method for the semantic interpretation of humanbehavior in mobility based on the merge of the mobile network data streamand the geo-referred available background knowledge. We modeled the humanbehavior making use of the geo and time-referenced knowledge available on theweb (e.g., geo-tagged resources, info on weather forecast, social events, etc.)matching it with the mobile network coverage map. The model is intended tocharacterize the contexts where the mobile network events occur in order to helpin interpreting the behavioral traits that generated by them. This will allow us toachieve a set of predictive tasks such as the prediction of human activities in certain contextual conditions (e.g., when an accident occurs on highway before theworking time starts, etc.), or the characterization of exceptional events detectedfrom anomalies in mobile network data.We created an ontological and stochastic high-level representation behavioralmodel (HRBModel) that maps the human activities to the different contexts.Given the mobile phone network and the geo-tagged resource Openstreetmap,the model is used to rank the activities associated to a particular network event(e.g. a sudden call amount peak) according to their probability. We also describethe design of an experimental evaluation and the preliminary evaluation resultsto measure the performance of the model and to improve the activity predictiontask.".
• paper-68 hasAuthorList authorList.
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• paper-68 title "Semantic Enrichment of Mobile Phone Data Records Using Linked Open Data".
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• item-1 hasContent zolzaya-dashdorj.
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• item-2 hasContent luciano-serafini.
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• paper-69 type InProceedings.
• paper-69 label "SemantEco Annotator".
• paper-69 sameAs paper-69.
• paper-69 abstract "Generating useful RDF linked data is not a straightforward process for scientists using today’s tools. In this paper we introduce the SemantEco Annotator, a semantic web application that leverages community-based vocabularies and ontologies during the translation process itself to ease the process of drawing out implicit relationships in tabular data so that they may be immediately available for use within the LOD cloud. Our goal for the SemantEco Annotator is to make advanced RDF translation techniques available to the layperson.".
• paper-69 hasAuthorList authorList.
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• paper-69 title "SemantEco Annotator".
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• item-1 hasContent patrice-seyed.
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• item-2 hasContent timothy-lebo.
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• item-3 hasContent evan-patton.
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• item-4 hasContent katherine-chastain.
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• item-5 hasContent brendan-ashby.
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• item-6 type ListItem.
• item-6 hasContent deborah-mcguinness.
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• paper-70 type InProceedings.

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