20.500.12556/RUL-175501 Automated Workflow Recommendations in Orange by Learning from a Collection of Workflow Screenshots Priporočila delotokov za Orange z učenjem iz zbirke posnetkov zaslonov delotokov This thesis presents a recommender system that suggests relevant widgets for Orange Data Mining workflows by learning from a curated collection of workflow screenshots. The dataset consists of 819 screenshots sourced from 25 chapters of Data Mining tutorials, created by various authors over time. We detect widgets in these screenshots using a two-stage computer vision pipeline: the Circle Hough Transform locates circular widget regions, and the Scale Invariant Feature Transform (SIFT) matches these regions against the Orange Widget Catalogue. The detection results — counts of each widget present in each screenshot — form a widget–screenshot matrix, which serves as the input for collaborative filtering. We train a Biased Regularized Incremental Simultaneous Matrix Factorization (BRISMF) model to predict the most suitable next widget for a partially built workflow. In evaluation, the BRISMF-based recommender achieved an average reconstructed position of 5.04, outperforming a frequency-based baseline (5.76) by 12.5%. These findings suggest the potential for automated recommendations in workflow design within visual programming environments. V diplomskem delu je predstavljen priporočilni sistem za predlaganje ustreznih komponent v delotokih okolja Orange DataMining na podlagi analize obstoječih vzorcev. Sistem preverja možnost samodejnega prepoznavanja logiˇcnih naslednjih korakov v še nedokončanih delotokih. Vhodni podatki vsebujejo 819 posnetkov zaslona iz 25 poglavij teorije podatkovnega rudarjenja. Komponente smo zaznavali s Houghovo transformacijo za kroge in transformacijo značilk z invarianco glede na merilo (metoda SIFT), pri čemer so rezultati zaznavanja oblikovali matriko komponenta–posnetek za kolaborativno filtriranje. Priporočilni model temelji na pristranski regularizirani inkrementalni simultani matrični faktorizaciji (BRISMF). Vrednotenje je pokazalo 12,5% boljšo uspešnost v primerjavi z osnovnim modelom, ki temelji na pogostosti, kar potrjuje potencial avtomatiziranih priporočil pri načrtovanju delotokov v okoljih za vizualno programiranje. Orange Data Mining Houghova transformacija transformacijaznačilk z invarianco glede na merilo pristranska regularizirana inkrementalnasimultana matriˇcna faktorizacija Orange Data Mining Hough Transform Scale Invariant FeatureTransform Biased Regularized Incremental Simultaneous Matrix Factorization true false false Angleški jezik Slovenski jezik Diplomsko delo/naloga 2025-10-29 15:30:02 2025-10-29 15:30:08 2025-12-11 04:03:51 0000-00-00 00:00:00 2025 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 38132 257364483 Eltsova_Maria_-_Priporocila_delotokov_za_Orange_z_ucenjem_iz_zbirke_posnetkov_zaslonov_delotokov.pdf Eltsova_Maria_-_Priporocila_delotokov_za_Orange_z_ucenjem_iz_zbirke_posnetkov_zaslonov_delotokov.pdf 1 474489671115AC503451A4EC091A222D 05e481d83e1592971f8d1f469c1e627fd3ed31d4cd92dfd216ef30825a31d001 77a62148-b4d3-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=221124 Fakulteta za računalništvo in informatiko 0 0 0