A Novel Spatial Data Pipeline for Orchestrating Apache NiFi/MiNiFi

Author:

Carthen Chase D.1ORCID,Zaremehrjardi Araam1,Le Vinh1,Cardillo Carlos1,Strachan Scotty2,Tavakkoli Alireza1,Harris Jr. Frederick C.1ORCID,Dascalu Sergiu M.1

Affiliation:

1. University of Nevada, Reno, USA

2. Nevada System of Higher Education, USA

Abstract

In many smart city projects, a common choice to capture spatial information is the inclusion of lidar data, but this decision will often invoke severe growing pains within the existing infrastructure. In this article, the authors introduce a data pipeline that orchestrates Apache NiFi (NiFi), Apache MiNiFi (MiNiFi), and several other tools as an automated solution to relay and archive lidar data captured by deployed edge devices. The lidar sensors utilized within this workflow are Velodyne Ultra Puck sensors that produce 6-7 GB packet capture (PCAP) files per hour. By both compressing the file after capturing it and compressing the file in real-time; it was discovered that GZIP and XZ both saved considerable file size being from 2-5 GB, 5 minutes in transmission time, and considerable CPU time. To evaluate the capabilities of the system design, the features of this data pipeline were compared against existing third-party services, Globus and RSync.

Publisher

IGI Global

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Networks and Communications,Computer Science Applications,Software

Reference17 articles.

1. Apache Software Foundation. (2023). Apache MiNiFi. Apache Software Foundation. https://nifi.apache.org/minifi/

2. Apache Software Foundation. (2023). Apache NiFi. Apache Software Foundation. https://nifi.apache.org/

3. Cooper, T. (2022). Municipal broadband 2022: Barriers remain an issue in 17 states. Broadband Now. https://broadbandnow.com/report/municipal-broadband-roadblocks/

4. Davison, W. (2023). RSync. Samba. https://rsync.samba.org/

5. Big Data Geospatial Processing for Massive Aerial LiDAR Datasets

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