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M. McInerney

Researcher at Goddard Space Flight Center

Publications -  11
Citations -  241

M. McInerney is an academic researcher from Goddard Space Flight Center. The author has contributed to research in topics: Cloud computing & Big data. The author has an hindex of 4, co-authored 11 publications receiving 215 citations.

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Journal ArticleDOI

MERRA Analytic Services: Meeting the Big Data challenges of climate science through cloud-enabled Climate Analytics-as-a-Service

TL;DR: The MERRA Analytic Services (MERRAAS) as mentioned in this paper is an example of cloud-enabled CAaaS built on this principle, which enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications data collection.

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science through Cloud-Enabled Climate Analytics-as-a-Service

TL;DR: The MERRA Analytic Services (MERRAAS) as mentioned in this paper is an example of cloud-enabled CAaaS built on this principle, which enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications data collection.
Journal ArticleDOI

Big Data Challenges in Climate Science: Improving the next-generation cyberinfrastructure

TL;DR: An overview of some of climate science's big data problems and the technical solutions being developed to advance data publication, climate analytics as a service, and interoperability within the Earth System Grid Federation (ESGF), which is the primary cyberinfrastructure currently supporting global climate research activities.
Book ChapterDOI

Climate Analytics as a Service

TL;DR: A concept of climate analytics as a service (CAaaS) that represents a convergence of data analytics and archive management, with several advantages, not the least of which is orders-of-magnitude time reduction in the data-assembly task common to many scientific workflows.

Archive Management of NASA Earth Observation Data to Support Cloud Analysis

TL;DR: Storing EO data in cloud WOS has a ripple effect throughout the NASA archive system with unexpected challenges and opportunities, and refactoring of the archive software to a cloud-native architecture; virtualizing data products by computing on demand; and reorganizing data to be more analysis-friendly.