English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The trade-off between language expressiveness and system scalability (E&S) isa well-known problem in RDF stream reasoning. Higher expressiveness supportsmore complex reasoning logic, however, it may also hinder system scalability.Current research mainly focuses on logical frameworks suitable for streamreasoning as well as the implementation and the evaluation of prototypesystems. These systems are normally developed in a centralized setting whichsuffer from inherent limited scalability, while an in-depth study of applyingdistributed solutions to cover E&S is still missing. In this paper, we aim toexplore the feasibility of applying modern distributed computing frameworks tomeet E&S all together. To do so, we first propose BigSR, a technicaldemonstrator that supports a positive fragment of the LARS framework. For thesake of generality and to cover a wide variety of use cases, BigSR relies onthe two main execution models adopted by major distributed executionframeworks: Bulk Synchronous Processing (BSP) and Record-at-A-Time (RAT).Accordingly, we implement BigSR on top of Apache Spark Streaming (BSP model)and Apache Flink (RAT model). In order to conclude on the impacts of BSP andRAT on E&S, we analyze the ability of the two models to support distributedstream reasoning and identify several types of use cases characterized by theirlevels of support. This classification allows for quantifying the E&S trade-offby assessing the scalability of each type of use case \wrt its level ofexpressiveness. Then, we conduct a series of experiments with 15 queries from 4different datasets. Our experiments show that BigSR over both BSP and RATgenerally scales up to high throughput beyond million-triples per second (withor without recursion), and RAT attains sub-millisecond delay for statelessquery operators.

BigSR: an empirical study of real-time expressive RDF stream reasoning on modern Big Data platforms