Category Archives: Blog

HOBBIT @ ISWC 2018

We are very pleased to announce that HOBBIT members presented 7 papers and organized a workshop at ISWC 2018, which was held on 8-12 October, 2018 in Monterey, USA. The International Semantic Web Conference (ISWC) is the premier international forum where Semantic Web / Linked Data researchers, practitioners, and industry specialists come together to discuss, […]

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HOBBIT Highlights: Summary of HOBBIT challenges (1st & 2nd period)

The HOBBIT project has successfully organized two series of challenges in order to measure the performance of implemented systems in processing Big Linked Data. In total, we organized ten (10) challenges, five (5) in each period. In particular, during the first period, HOBBIT organized: the MOCHA challenge at ESWC 2017 (https://project-hobbit.eu/challenges/mighty-storage-challenge/) the OKE challenge at […]

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OpenLink & SAGE Project: Creating a GeoSPARQL Benchmark

The success of the HOBBIT benchmarking platform, along with the fact that many RDF / SPARQL systems are readily available for benchmarking by the platform, made us look at the options of using it as part of another R&D project, called SAGE, in which OpenLink is involved. The project “SAGE: Semantic Geospatial Analytics” aims to […]

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HOBBIT Spatial Benchmark V2.0

A number of real and synthetic benchmarks have been proposed for evaluating the performance of link discovery systems. So far, only a limited number of link discovery benchmarks target the problem of linking geo-spatial entities. However, some of the largest knowledge bases of the Linked Open Data Cloud, such as LinkedGeoData contain vast amounts of […]

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SQA Challenge

Question answering (QA) systems have recently become commercially viable (and lucrative) products, thanks to increasing investments and research and the development of intuitive and easy-to-use interfaces. Regular interactions with QA systems have become increasingly frequent and natural and the consumers’ expectations around their capabilities keep growing. Such systems are now available in various settings, devices […]

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HOBBIT @ DEBS Conference 2018

The DEBS Grand Challenge 2018 was successfully executed on the HOBBIT online platform. Analytical and machine learning algorithms running in a distributed streaming manner are in the focus of Grand Challenge series, which is a  joint event with the annual Distributed Event-Based Systems (DEBS) Conference. During the competition participating  teams have to demonstrate their capabilities […]

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Virtuoso development progress evaluated with the Data Storage benchmark

After Virtuoso took part in the MOCHA 2017 and MOCHA 2018 challenges, and won both, we at OpenLink wanted to benchmark and evaluate a set of different Virtuoso versions using the same benchmark setup, in order to get a comparative analysis and showcase the progress of its development. The benchmark which we set out to […]

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Katana & HOBBIT

USU Software AG is a mid-tier business competing in many areas of IT. Based on the insights of several research projects, we are currently improving our big data analytics and machine learning expertise. In the past, we were able to provide solutions for industrial big data analysis, applying machine learning and complex-event-processing technologies on sensor […]

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ESWC 2018 Challenges

The HOBBIT project organized three benchmarking challenges at the ESWC 2018 conference, which took place in Heraklion, Crete in Greece from June 3rd to June 7th, as it aims to bring together and inspire people from academia and industry to come up with fresh and innovative ideas on solving real-life problems concerning Big Linked Data. […]

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Generating Transport Data

The growing amount of navigation services’ users provide many opportunities to improve driving conditions by making a better use of the road network. However, collecting large quantities of such data can be difficult due to both costs and privacy concerns. TomTom collects large amounts of anonymized traffic data, respecting the user’s right to privacy: https://www.tomtom.com/en_gb/privacy/. […]

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