A platform to benchmark inference algorithms based on sensor network dataA platform to benchmark inference algorithms based on sensor network dataΠλατφόρμα αξιολόγησης αλγορίθμων συμπερασμού με δεδομένα από δίκτυα αισθητήρων Διπλωματική Εργασία Diploma Work 2022-10-172022enThis 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.http://creativecommons.org/licenses/by/4.0/Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών ΥπολογιστώνRentzepopoulos_Athanasios_Dip_2022.pdfChania [Greece]Library of TUC2022-10-17application/pdf20.0 MBfree Rentzepopoulos Athanasios Ρεντζεποπουλος Αθανασιος Bletsas Aggelos Μπλετσας Αγγελος Karystinos Georgios Καρυστινος Γεωργιος Samoladas Vasilis Σαμολαδας Βασιλης Πολυτεχνείο Κρήτης Technical University of Crete Microservices Inference