Abstract: In an embodiment, workflow for timeseries forecasting may be performed based on automated machine learning. Sensor data for measurement parameter is received from plurality of sensors installed in built environment and the received sensor data is stored in table of relational database. Cut-off record associated with previous training checkpoint is determined of the forecasting model for the measurement parameter. Records including new records are determined for which respective timestamps occur after the measurement timestamp of cut-off record. Size of the determined records are compared with threshold size and training dataset is prepared. The forecasting model is trained on the training dataset based on the comparison.

Abstract: A method may include obtaining a dataset of graph-structured data, the dataset including one or more subgraphs. The method may include determining a correlation between analyses of the dataset using graph explainable artificial intelligence (GXAI) techniques and using a plurality of graph analysis algorithms. A ranked list of the plurality of graph analysis algorithms may be generated based on the correlation. The method may further include determining general characteristic of the dataset of graph-structured data, the general characteristic indicative of similarities among the one or more subgraphs. A threshold number of the graph analysis algorithms may be assigned to the general characteristic based on the ranked list of the plurality of graph analysis algorithms. An assignment table may be generated including the general characteristic and the threshold number of the graph analysis algorithms. A display may be generated within a graphical user interface (GUI) that visualizes the assignment table.

Abstract: A method may include obtaining a dataset of graph-structured data, the dataset including one or more subgraphs. The method may include determining a correlation between analyses of the dataset using graph explainable artificial intelligence (GXAI) techniques and using a plurality of graph analysis algorithms. A ranked list of the plurality of graph analysis algorithms may be generated based on the correlation. The method may further include determining general characteristic of the dataset of graph-structured data, the general characteristic indicative of similarities among the one or more subgraphs. A threshold number of the graph analysis algorithms may be assigned to the general characteristic based on the ranked list of the plurality of graph analysis algorithms. An assignment table may be generated including the general characteristic and the threshold number of the graph analysis algorithms. A display may be generated within a graphical user interface (GUI) that visualizes the assignment table.

Abstract: A method may include obtaining a first result of a graph explainable artificial intelligence (GXAI) classification analysis of a dataset of graph-structured data and a second result of a graph analysis algorithm that represents relationships between elements of the dataset. The method may include determining a correlation between the first result and the second result and generating a display within a graphical user interface (GUI) that visualizes similarities between the first result and the second result based on the correlation. Determining the correlation between the first result and the second result may include generating a first vector of the first result of the classification analysis using GXAI techniques and a second vector of the second result of the graph analysis algorithm. A Pearson correlation coefficient or a cosine similarity coefficients may be computed based on the first vector and the second vector in which the computed coefficients are indicative of the correlation.