This article provides a systematic literature review on applying different algorithms to municipal data processing, aiming
to understand how the data were collected, stored, pre-processed, and analyzed, to compare various methods, and to select
feasible solutions for further research. Several algorithms and data types are considered, finding that clustering, classification,
correlation, anomaly detection, and prediction algorithms are frequently used. As expected, the data is of several types,
ranging from sensor data to images. It is a considerable challenge, although several algorithms work very well, such as Long
Short-Term Memory (LSTM) for timeseries prediction and classification.Open access funding provided by FCT|FCCN (b-on).info:eu-repo/semantics/publishedVersio
