Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/362E9BA8-E76F-4976-ACB5-3FCC49422C48 | ||
| Year | 2022 | ||
| Type of Item | Diploma Work | ||
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| Bibliographic Citation | Athanasios Rentzepopoulos, "A platform to benchmark inference algorithms based on sensor network data", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2022 https://doi.org/10.26233/heallink.tuc.93693 | ||
| Appears in Collections |
This work offers a means of accessing a database with sensor network (e.g. soil moisture) measurements/data for use with inference algorithms. The design problem is approached by creating a web application based on microservices hosting a user interface (UI), a scalable back-end that runs the algorithms, and database storage. Algorithms are expected to be Python scripts developed offline and tested either offline or online. This multi-modal operation poses a compatibility concern. We create a library that exclusively handles data input and output for the scripts that import it, with different implementations depending on the context of the user scripts. We also explored an efficient way to execute scripts, in the back-end of the web-app, allowing for robust interruption of running scripts and parallel execution. This service also includes a queueing platform that handles the input and output of the user scripts and takes care of graceful process start up and shutdown. The user experience is also accounted for, with responsive web UI and source code analysis to automatically find out the number and type of input streams. Finally, we test the system by utilizing the library to parse and pre-process data and implement an inference algorithm to run on the pre-processed data.
Copyright © DIAS 2013
