Abstract: Various methods, apparatuses/systems, and media for data management are disclosed. A processor integrates internal data, third party data, user-generated content data, and historical data into key data that relates to management of one or more stores and branches in a network of stores and branches. The key data is stored into a single centralized database. The processor generates analytical insights data from analysis of the key data and historical data. The analytical insights data is displayed onto a graphical user interface (GUI) for considering and analyzing the analytical insights data by a user. User's feedback data is received that corresponds to the user's response based on analyzing the analytical insights data. The processor apples machine learning (ML) algorithms for continuously analyzing all available data including the user's feedback data and extracting and classifying contents of available data including the feedback data to provide targeted recommendations data onto the GUI.

Abstract: A method for non-intrusively monitoring a load including a plurality of appliances includes retrieving a plurality of mathematical models for modeling operation of a respective subset of the appliances. A value of a respective operational parameter is predicted for each of the subsets of appliances. An output of the load is measured. A respective value of each of the operational parameters is calculated based on the mathematical models and the outputs of the load. The predicting, measuring and calculating steps are repeated until a metric pertaining to a difference error between the measured output and the predicted output calculated from the operational parameter and the mathematical models is equal to or below a threshold for one of the subsets of appliances. It is decided that the one subset of appliances is currently operating whose metric pertaining to a difference error is equal to or below the threshold.

Abstract: A method for non-intrusively monitoring a load including a plurality of appliances includes retrieving a plurality of mathematical models for modeling operation of a respective subset of the appliances. A value of a respective operational parameter is predicted for each of the subsets of appliances. An output of the load is measured. A respective value of each of the operational parameters is calculated based on the mathematical models and the outputs of the load. The predicting, measuring and calculating steps are repeated until a metric pertaining to a difference error between the measured output and the predicted output calculated from the operational parameter and the mathematical models is equal to or below a threshold for one of the subsets of appliances. It is decided that the one subset of appliances is currently operating whose metric pertaining to a difference error is equal to or below the threshold.