Ruben Verborgh’s data

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• 978-3-319-98192-5_28 topic World_Wide_Web.
• 978-3-319-98192-5_28 subject World_Wide_Web.
• 978-3-319-98192-5_28 authorList b0_b2231.
• 978-3-319-98192-5_28 topic World_Wide_Web.
• 978-3-319-98192-5_28 abstract "The road to publishing public streaming data on the Web is paved with trade-offs that determine its viability. The cost of unrestricted query answering on top of data streams, may not be affordable for all data publishers. Therefore, public streams needs to be funded in a sustainable fashion to remain online. In this paper we introduce an overview of possible query answering features for live time series as multidimensional interfaces. For example, from a live parking availability data stream, pre-calculated time constrained statistical indicators or geographically classified data can be provided to clients on demand. Furthermore, we demonstrate the initial developments of a Linked Time Series server that supports such features through an extensible modular architecture. Benchmarking the costs associated to each of these features allows to weigh the trade-offs inherent to publishing live time series and establishes the foundations to create a decentralized and sustainable ecosystem for live data streams on the Web.".
• 978-3-319-98192-5_28 datePublished "2018".
• 978-3-319-98192-5_28 mainEntityOfPage rojas_eswc_demo_2018.
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• 978-3-319-98192-5_28 page rojas_eswc_demo_2018.
• 978-3-319-98192-5_28 number "11155".
• 978-3-319-98192-5_28 number "152".
• 978-3-319-98192-5_28 number "148".
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• 978-3-319-98192-5_40 author me.
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• 978-3-319-98192-5_40 about R_RML.
• 978-3-319-98192-5_40 about RML.
• 978-3-319-98192-5_40 about Annotation.
• 978-3-319-98192-5_40 about rules.
• 978-3-319-98192-5_40 about Linked_Data.
• 978-3-319-98192-5_40 author me.
• 978-3-319-98192-5_40 author me.
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• 978-3-319-98192-5_40 type PublicationVolume.
• 978-3-319-98192-5_40 type ScholarlyArticle.
• 978-3-319-98192-5_40 type Article.
• 978-3-319-98192-5_40 type Document.
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• 978-3-319-98192-5_40 title "Declarative Rules for Linked Data Generation at your Fingertips!".
• 978-3-319-98192-5_40 isPartOf proceedings_of_the_15th_eswc_posters_and_demos.
• 978-3-319-98192-5_40 name "Declarative Rules for Linked Data Generation at your Fingertips!".
• 978-3-319-98192-5_40 label "Declarative Rules for Linked Data Generation at your Fingertips!".
• 978-3-319-98192-5_40 name "Declarative Rules for Linked Data Generation at your Fingertips!".
• 978-3-319-98192-5_40 topic R_RML.
• 978-3-319-98192-5_40 topic RML.
• 978-3-319-98192-5_40 topic Annotation.
• 978-3-319-98192-5_40 topic rules.
• 978-3-319-98192-5_40 topic Linked_Data.
• 978-3-319-98192-5_40 subject R_RML.
• 978-3-319-98192-5_40 subject RML.
• 978-3-319-98192-5_40 subject Annotation.
• 978-3-319-98192-5_40 subject rules.
• 978-3-319-98192-5_40 subject Linked_Data.
• 978-3-319-98192-5_40 authorList b0_b2238.
• 978-3-319-98192-5_40 topic R_RML.
• 978-3-319-98192-5_40 topic RML.
• 978-3-319-98192-5_40 topic Annotation.
• 978-3-319-98192-5_40 topic rules.
• 978-3-319-98192-5_40 topic Linked_Data.
• 978-3-319-98192-5_40 abstract "Linked Data is often generated based on a set of declarative rules using languages such as R2RML and RML. These languages are built with machine-processability in mind. It is thus not always straightforward for users to define or understand rules written in these languages, preventing them from applying the desired annotations to the data sources. In the past, graphical tools were proposed. However, next to users who prefer a graphical approach, there are users who desire to understand and define rules via a text-based approach. For the latter, we introduce an enhancement to their workflow. Instead of requiring users to manually write machine-processable rules, we propose writing human-friendly rules, and generate machine-processable rules based on those human-friendly rules. At the basis is YARRRML: a human-readable text-based representation for declarative generation rules. We propose a novel browser-based integrated development environment called “Matey”, showcasing the enhanced workflow. In this work, we describe our demo. Users can experience first hand how to generate triples from data in different formats by using YARRRML’s representation of the rules. The actual machine-processable rules remain completely hidden when editing. Matey shows that writing human-friendly rules enhances the workflow for a broader range of users. As a result, more desired annotations will be added to the data sources which leads to more desired Linked Data.".
• 978-3-319-98192-5_40 datePublished "2018".
• 978-3-319-98192-5_40 mainEntityOfPage heyvaert_eswc_demo_2018.
• 978-3-319-98192-5_40 sameAs 978-3-319-98192-5_40.
• 978-3-319-98192-5_40 isPrimaryTopicOf heyvaert_eswc_demo_2018.
• 978-3-319-98192-5_40 page heyvaert_eswc_demo_2018.
• 978-3-319-98192-5_40 number "213".
• 978-3-319-98192-5_40 number "11155".
• 978-3-319-98192-5_40 number "217".
• 978-3-319-98192-5_40 volume "11155".
• 978-3-319-98192-5_40 locator "213".
• 978-3-319-98192-5_40 locator "11155".
• 978-3-319-98192-5_40 locator "217".
• 978-3-319-98192-5_40 volume "11155".
• 978-3-319-98192-5_40 position "11155".

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