LDBC 6th TUC Meeting conclusions

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LDBC council

LDBC 6th TUC Meeting conclusions by Peter Boncz

Technology

LDBC
Linked Data Benchmark Council
Conclusions & Future Perspectives
Peter Boncz

• EU project on track
– All deliverables accepted
• Benchmark Innovation
– Choke-point-based development
– Correlated social graph generation (datagen)
– Advanced graph-dependency-aware driver
– TPCTC, CIKM, SIGMOD, WWW papers
• Four benchmarks developed
– SPB, SNB-Interactive
– SNB-BI, SNB-Graphalytics
LDBC So Far

• 14 LDBC presentations
• 36 User Scenarios
– Roughly 50/50 between PG/RDF
– 2x: Jerven Bolleman, Andreas Both
• 12 Academic Presentations
– Skew towards RDF
• 15 System/Vendor Presentations
– Neo Technologies, Computer Associates, MarkLogic, Metaphacts/Systap,
HP Labs, IBM, Openlink, Ontotext, Sparsity, SparlCity, Oracle, LogicBlox,
Giraph
TUC

• New Industry Participants
– Members Oracle, IBM, SparqlCity/Actian, Systap
– HP Labs, MarkLogic, LogicBlox, Computer Associates, GraphLab
• Expansion to Graph Programming Frameworks
– Graphalytics group (TU Delft)
– Giraph contributors
• Social Network Companies
– Google, FaceBook LinkedIn
Growing LDBC

LDBC Future
• Transition to Independent Council
– TUC7, November 2015, at TJ Watson
• Additional Activities
– “Big Graph” Conference?
• New Benchmark Development
– Graph programming
– Temporal graphs
– OWL Reasoning
– Entity Matching
– …

Neo4j has long been recognised as the world's leading graph database, and now is expanding to be a true platform for Enterprise Graph Applications. Now is a better time than ever to explore which version of Neo4j you should be using considering the product features and/or services you want to benefit from. In this webinar, we'll walk through the different versions of Neo4j, discuss and show their remits, and present what the alternative (open source or commercial) licensing options may be. Specifically, we'll dig into the free open-source, free commercial (for individuals, startups, and academics) and paid commercial options and answer any questions that you may have.

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SPEAKER 1 - RAIL "Crossrail: Future-Proofing Railway Asset Management" Daniel Irwin GIS Manager Crossrail Daniel Irwin is the Geospatial Lead for Crossrail, a £15B railway construction project in London and one the largest infrastructure projects in Europe over the last 50 years. With over 15 years of geospatial experience he has worked in sectors ranging from environmental, utilities, transport modelling and construction through to asset and facilities management. At Crossrail Daniel is leading the development of geospatial solutions to integrate and support the BIM principles that underpin the project for the entire lifecycle of a world class railway.

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he convergence of technology and infrastructure has the ability to transform our communities and economy, reduce emissions as well provide an opportunity for business leaders to optimise asset performance and reduce cost. To realise the benefits of next generation intelligence, a unified vision and strategy is required to ensure we are understanding how to best leverage the information we are receiving, and how we are managing the vast amount of data associated with new systems. Asset information and data management will allow more precise decisions to be made to balance cost, risk and performance, supporting operational effectiveness and efficiency. We will be addressing how the European rail sector are developing and implementing asset information strategies, managing data across multiple disparate systems and leveraging new technologies to succeed.

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Netflix runs on hundreds of interconnected microservices that, as a whole system, is too complex for any one person to completely understand. We are developing a tool, Vizceral, that helps distill the most useful bits of information and present it to the user in a way that they can 'feel' the state of the system without needing to understand all the moving parts. There is still a lot of work to be done to get more and more intuition about the system, but it has already proven vital internally and has been open sourced.

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Traditional security tools like security information and event managers (SIEMs) are struggling to keep up with the terabytes of event data (250M to 2B events) being generated each day from an ever-growing number of devices. Cybersecurity has become a data problem, and enterprises need to reply with scalable solutions to enable effective hunting and combat evolving attacks. Rethinking the cybersecurity problem as a data-centric problem led Accenture Labs’s Cybersecurity team to use emerging big data tools along with new approaches such as graph databases and analysis to exploit the connected nature of the data to its advantage. Joshua Patterson, Michael Wendt, and Keith Kraus explain how Accenture Labs’s Cybersecurity team is using Apache Kafka, Spark, and Flink to stream data into Blazegraph and Datastax Graph to accelerate cyber defense. Leveraging Datastax Graph and Blazegraph allows Accenture Labs to greatly accelerate query and analysis performance compared to traditional security tools like SIEM. Josh, Michael, and Keith share the challenges of fitting cybersecurity data into each of the graph structures, as well as the ways they exploited the connectedness of events to discover new threats that would have been missed in traditional SIEM tools. In addition, they explain how they use GPUs to accelerate graph analysis by using Blazegraph DASL. Josh, Michael, and Keith end by demonstrating how to efficiently and effectively stream data into these graph databases using best-in-breed technologies such as Apache Kafka, Spark, and Flink and touch on why Kudu is becoming an integral part of Accenture’s technology stack. Utilizing these technologies, clients have supercharged their security analysts’ cyber-hunting abilities and are uncovering threats faster.

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(http://lod2.eu/BlogPost/webinar-series) In this Webinar Michael Martin presents CubeViz - a facetted browser for statistical data utilizing the RDF Data Cube vocabulary which is the state-of-the-art in representing statistical data in RDF. This vocabulary is compatible with SDMX and increasingly being adopted. Based on the vocabulary and the encoded Data Cube, CubeViz is generating a facetted browsing widget that can be used to filter interactively observations to be visualized in charts. Based on the selected structure, CubeViz offer beneficiary chart types and options which can be selected by users. If you are interested in Linked (Open) Data principles and mechanisms, LOD tools & services and concrete use cases that can be realised using LOD then join us in the free LOD2 webinar series!

Test Automation with Grad-CAM Heatmaps - A Future Pipe Segment in MLOps for V...

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Machine Learning (ML) is a fundamental part of modern perception systems. In the last decade, the performance of computer vision using trained deep neural networks has outperformed previous approaches based on careful feature engineering. However, the opaqueness of large ML models is a substantial impediment for critical applications such as in the automotive context. As a remedy, Gradient-weighted Class Activation Mapping (Grad-CAM) has been proposed to provide visual explanations of model internals. In this paper, we demonstrate how Grad-CAM heatmaps can be used to increase the explainability of an image recognition model trained for a pedestrian underpass. We argue how the heatmaps support compliance to the EU's seven key requirements for Trustworthy AI. Finally, we propose adding automated heatmap analysis as a pipe segment in an MLOps pipeline. We believe that such a building block can be used to automatically detect if a trained ML-model is activated based on invalid pixels in test images, suggesting biased models.

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Avogadro is being rewritten and architected to put semantic chemical meaning at the center of its internal data structures in order to fully support data-centric workflows. Computational and experimental chemistry both suffer when semantic meaning is lost; through the use of expressive formats such as CML, along with lightweight data-exchange formats such as JSON, workflows that previously demanded manual intervention to retain semantic meaning can be used. Integration with projects like JUMBO and Open Babel when conversion is required, coupled with codes such as NWChem where direct support for CML is being added, allow for much richer storage, analysis, and indexing of data. As web-based data sources add more semantic structure to their data, Avogadro will take advantage of those resources.

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