Titlebook: Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI; 17th Smoky Mountains Jeffrey Nichols,Becky

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Automated Integration of Continental-Scale Observations in Near-Real Time for Simulation and Analysirvations that will operate for multiple decades. To maximize the utility of NEON data, we envision edge computing systems that gather, calibrate, aggregate, and ingest measurements in an integrated fashion. Edge systems will employ machine learning methods to cross-calibrate, gap-fill and provision

corporate

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Unsupervised Anomaly Detection in Daily WAN Traffic Patternss of observed traffic. Network providers need intelligent solutions that can help quickly identify and understand anomalous behaviors at the network edge, allowing reactions to unexpected traffic or attacks on facilities and their peerings. However, due to lack of labeled data in network traffic ana

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1865-0929 tation: on the road to a converged ecosystem;?scientific data challenges..*The conference was held virtually due to the COVID-19 pandemic..978-3-030-63392-9978-3-030-63393-6Series ISSN 1865-0929 Series E-ISSN 1865-0937

AGOG

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Christoph H?nnige,Sascha Kneip,Astrid Lorenzdata pipelines from multiple months of network flow records. Once trained, individual classifiers quickly observe and flag alerts in hourly behaviors. Our work describes building the data pipeline as well as addressing issues of false positives and workflow integration.

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Performance Improvements on SNS and HFIR Instrument Data Reduction Workflows Using Mantidduction workflows. We propose a more disruptive domain-specific solution: the No Cost Input Output (NCIO) framework, we provide an overview, the risks and challenges in NCIO’s adoption by HFIR and SNS stakeholders.