DOI: https://doi.org/10.15368/theses.2015.124
Available at: https://digitalcommons.calpoly.edu/theses/1496
Date of Award
6-2015
Degree Name
MS in Computer Science
Department/Program
Computer Science
Advisor
Alexander Dehktyar
Abstract
Today’s economy and infrastructure is dependent on raw natural resources, like crude oil and natural gases, that are optimally transported through a net- work of hundreds of thousands of miles of pipelines throughout America[28]. A damaged pipe can negatively a↵ect thousands of homes and businesses so it is vital that they are monitored and quickly repaired[1]. Ideally, pipeline operators are able to detect damages before they occur, but ensuring the in- tegrity of the vast amount of pipes is unrealistic and would take an impractical amount of time and manpower[1].
Natural disasters, like earthquakes, as well as construction are just two of the events that could potentially threaten the integrity of pipelines. Due to the diverse collection of data sources, the necessary geospatial data is scat- tered across di↵erent physical locations, stored in di↵erent formats, and owned by di↵erent organizations. Pipeline companies do not have the resources to manually gather all input factors to make a meaningful analysis of the land surrounding a pipe.
Our solution to this problem involves creating a single, centralized system that can be queried to get all necessary geospatial data and related informa- tion in a standardized and desirable format. The service simplifies client-side computation time by allowing our system to find, ingest, parse, and store the data from potentially hundreds of repositories in varying formats. An online web service fulfills all of the requirements and allows for easy remote access to do critical analysis of the data through computer based decision support systems (DSS).
Our system, REST API for Pipeline Integrity Data (RAPID), is a multi- tenant REST API that utilizes HTTP protocol to provide a online and intuitive set of functions for DSS. RAPID’s API allows DSS to access and manage data stored in a geospatial database with a supported Django web framework. Full documentation of the design and implementation of RAPID’s API are detailed in this thesis document, supplemented with some background and validation of the completed system.