@article {RN166,
	title = {The Virtual Data Collaboratory: A Regional Cyberinfrastructure for Collaborative Data-Driven Research},
	journal = {Computing},
	volume = {22},
	number = {3},
	year = {2020},
	pages = {79-92},
	type = {Journal Article},
	abstract = {The Virtual Data Collaboratory is a federated data cyberinfrastructure designed to drive data-intensive, interdisciplinary, and collaborative research that will impact researchers, educators, and entrepreneurs across a broad range of disciplines and domains as well as institutional and geographic boundaries.},
	keywords = {big data, Cloud computing, Collaboration, Computer architecture, Education, Software engineering, Virtual reality},
	doi = {10.1109/mcse.2019.2908850},
	url = {https://app.dimensions.ai/details/publication/pub.1113521625},
	author = {Parashar, Manish and Simonet, Anthony and Rodero, Ivan and Ghahramani, Forough and Agnew, Grace and Jantz, Ron and Honavar, Vasant}
}
@article {RN94,
	title = {Submarine: A subscription-based data streaming framework for integrating large facilities and advanced cyberinfrastructure},
	journal = {Concurrency and Computation Practice and Experience},
	year = {2019},
	pages = {e5256},
	type = {Journal Article},
	abstract = {Large scientific facilities provide researchers with instrumentation, data, and data products that can accelerate scientific discovery. However, increasing data volumes coupled with limited local computational power prevents researchers from taking full advantage of what these facilities can offer. Many researchers looked into using commercial and academic cyberinfrastructure (CI) to process these data. Nevertheless, there remains a disconnect between large facilities and CI that requires researchers to be actively part of the data processing cycle. The increasing complexity of CI and data scale necessitates new data delivery models, those that can autonomously integrate large-scale scientific facilities and CI to deliver real-time data and insights. In this paper, we present our initial efforts using the Ocean Observatories Initiative project as a use case. In particular, we present a subscription-based data streaming service for data delivery that leverages the Apache Kafka data streaming platform. We also show how our solution can automatically integrate large-scale facilities with CI services for automated data processing.},
	doi = {10.1002/cpe.5256},
	url = {https://app.dimensions.ai/details/publication/pub.1113381084},
	author = {Zamani, Ali Reza and AbdelBaky, Moustafa and Balouek-Thomert, Daniel and Villalobos, J. J. and Rodero, Ivan and Parashar, Manish}
}
@article {RN144,
	title = {Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows},
	journal = {The International Journal of High Performance Computing Applications},
	volume = {33},
	number = {6},
	year = {2019},
	pages = {1159-1174},
	type = {Journal Article},
	abstract = {Dramatic changes in the technology landscape marked by increasing scales and pervasiveness of compute and data have resulted in the proliferation of edge applications aimed at effectively processing data in a timely manner. As the levels and fidelity of instrumentation increases and the types and volumes of available data grow, new classes of applications are being explored that seamlessly combine real-time data with complex models and data analytics to monitor and manage systems of interest. However, these applications require a fluid integration of resources at the edge, the core, and along the data path to support dynamic and data-driven application workflows, that is, they need to leverage a computing continuum. In this article, we present our vision for enabling such a computing continuum and specifically focus on enabling edge-to-cloud integration to support data-driven workflows. The research is driven by an online data-driven tsunami warning use case that is supported by the deployment of large-scale national environment observation systems. This article presents our overall approach as well as current status and next steps.},
	keywords = {Continuum computing, data-driven workflows, edge-to-cloud integration, programming systems, streaming data-analytics},
	doi = {10.1177/1094342019877383},
	url = {https://app.dimensions.ai/details/publication/pub.1121117342},
	author = {Balouek-Thomert, Daniel and Renart, Eduard Gibert and Zamani, Ali Reza and Simonet, Anthony and Parashar, Manish}
}
