Tapis v3 Streams API: Time‐series and data‐driven event support in science gateway infrastructure. (25th November 2020)

• Record Type: Journal Article
• Authors: Cleveland, Sean B.; Jamthe, Anagha; Padhy, Smruti; Stubbs, Joe; Terry, Steven; Looney, Julia; Cardone, Richard; Packard, Michael; Dahan, Maytal; Jacobs, Gwen A.
• Other Names: Ogiela Marek R. guestEditor.; Rahayu Wenny guestEditor.; Palmieri Francesco guestEditor.; Kalyanam Rajesh guestEditor.; Stankovski Vlado guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 19(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-25
• Subjects: Abaco -- API -- CHORDS -- Kapacitor -- streaming data -- Tapis; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6103
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The explosion of IoT devices and sensors in recent years has led to a demand for efficiently storing, processing and analyzing time‐series data. Geoscience researchers use time‐series data stores such as Hydroserver, Virtual Observatory and Ecological Informatics System (VOEIS), and Cloud‐Hosted Real‐time Data Service (CHORDS). Many of these tools require a great deal of infrastructure to deploy and expertise to manage and scale. The Tapis framework, an NSF funded project, provides science as a service APIs to allow researchers to achieve faster scientific results, by eliminating the need to set up a complex infrastructure stack. The University of Hawai'i (UH) and Texas Advanced Computing Center (TACC) have collaborated to develop an open source Tapis Streams API that builds on the concepts of the CHORDS time series data service to support research. This new hosted service allows storing, processing, annotating, archiving, and querying time‐series data in the Tapis multi‐user and multi‐tenant collaborative platform. The Streams API provides a hosted production level middleware service that enables new data‐driven event workflows capabilities that may be leveraged by researchers and Tapis powered science gateways for handling spatially indexed time‐series datasets.