skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: https://doi.org/10.5281/zenodo.7201342
The Tapis Streams API is a production grade quality service that provides REST APIs for storing, processing and analyzing real-time streaming data. This paper focuses on improvements made to Tapis 1.0 Streams API for making it up-to-date and easily accessible. The newer version, Tapis 1.2 Streams API adopts the latest version of InfluxDB, InfluxDB 2.X, which has built-in security features and supports next generation data analytics and processing with a data processing language Flux. This paper also discusses the measures implemented in the Tapis 1.2 Streams API to mitigate potential security risks involved in unauthorized data stream access by users who do not own it. Additionally, new data Channel Actions supporting 3rd Party notification and web-hooks has been released. Lastly a tool, Tapis UI, which is a self contained server less application to access Tapis Services via rest calls is discussed in the paper. Tapis UI is a lightweight browser only client application which allows interactive access to Streams resources and real-time streaming data.  more » « less
Award ID(s):
1931575
PAR ID:
10462241
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Science Gateways 2022
Page Range / eLocation ID:
4
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; ; (, Practice and Experience in Advanced Research Computing)
    In the last decade, the rise of hosted Software-as-a-Service (SaaS) application programming interfaces (APIs) across both academia and industry has exploded, and simultaneously, microservice architectures have replaced monolithic application platforms for the flexibility and maintainability they offer. These SaaS APIs rely on small, independent and reusable microservices that can be assembled relatively easily into more complex applications. As a result, developers can focus on their own unique functionality and surround it with fully functional, distributed processes developed by other specialists, which they access through APIs. The Tapis framework, a NSF funded project, provides SaaS APIs to allow researchers to achieve faster scientific results, by eliminating the need to set up a complex infrastructure stack. In this paper, we describe the best practices followed to create Tapis APIs using Python and the Stream API as an example implementation illustrating authorization and authentication with the Tapis Security Kernel, Tenants and Tokens APIs, leveraging OpenAPI v3 specification for the API definitions and docker containerization. Finally, we discuss our deployment strategy with Kubernetes, which is an emerging orchestration technology and the early adopter use cases of the Streams API service. 
    more » « less
  2. ; ; ; ; ; ; ; ; ; (, Concurrency and Computation: Practice and Experience)
    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. 
    more » « less
  3. ; ; ; ; ; ; ; (, Conference: Science Gateways 2019At: San Diego, CA)
    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, VOEIS and CHORDS. Many of these tools require a great deal of infrastructure to deploy and expertise to manage and scale. Tapis's (formerly known as Agave) platform as a service provides a way to support researchers in a way that they are not responsible for the infrastructure and can focus on the science. The University of Hawaii (UH) and Texas Advanced Computing Center (TACC) have collaborated to develop a new API integration that combines Tapis with the CHORDS time series data service to support projects at both institutions for storing, annotating and querying time-series data. This new Streams API leverages the strengths of both the Tapis platform and CHORDS service to enable capabilities for supporting time-series data streams not available in either tool alone. These new capabilities may be leveraged by Tapis powered science gateways with needs for handling spatially indexed time-series data-sets for their researchers as they have been at UH and TACC. 
    more » « less
  4. ; ; ; (, Zenodo)
    The adaptation of machine learning (ML) in scientific and medical research in recent years has heralded a new era of innovation, catalyzing breakthroughs that were once deemed unattainable. In this paper, we present the Machine Learning Hub (ML Hub) – a web application offering a single point of access to pre-trained ML models and datasets, catering to users across varying expertise levels. Built upon the NSF-funded Tapis v3 Application Programming Interface (API) and Tapis User Interface (TapisUI), the platform offers a user-friendly interface for model discovery, dataset exploration, and inference server deployment. 
    more » « less
  5. ; ; ; (, Zenodo)
    The adaptation of machine learning (ML) in scientific and medical research in recent years has heralded a new era of innovation, catalyzing breakthroughs that were once deemed unattainable. In this paper, we present the Machine Learning Hub (ML Hub) – a web application offering a single point of access to pre-trained ML models and datasets, catering to users across varying expertise levels. Built upon the NSF-funded Tapis v3 Application Programming Interface (API) and Tapis User Interface (TapisUI), the platform offers a user-friendly interface for model discovery, dataset exploration, and inference server deployment. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email