Abstract: A predictive modeling approach to managing cellular network infrastructure is disclosed. In an embodiment, a method can include receiving raw data from a plurality of data sources populated while operating a cellular network. The method can then generate per-logical cell site data by normalizing the raw data based on a set of LCSs in the cellular network to generate per-LCS data. The method can then generate an example from the per-LCS data and generate a predicted energy consumption value for the given LCS by inputting the example into a predictive model (e.g., a decision tree-based model, such as an XGBoost model). From this output, the method can determine if the predicted energy consumption value is higher than an expected energy consumption value (e.g., a historical range of consumption). If so, the method can then label the given LCS as an outlier.

Abstract: A predictive modeling approach to managing cellular network infrastructure is disclosed. In an embodiment, a method can include receiving raw data from a plurality of data sources populated while operating a cellular network. The method can then generate per-logical cell site data by normalizing the raw data based on a set of LCSs in the cellular network to generate per-LCS data. The method can then generate an example from the per-LCS data and generate a predicted energy consumption value for the given LCS by inputting the example into a predictive model (e.g., a decision tree-based model, such as an XGBoost model). From this output, the method can determine if the predicted energy consumption value is higher than an expected energy consumption value (e.g., a historical range of consumption). If so, the method can then label the given LCS as an outlier.

Abstract: Scheduling a given event, in one aspect, may include obtaining a set of one or more business rules, obtaining analytics, and obtaining an event specification. The event specification may include at least one content specification and two or more transmission modes. The event may be scheduled based on the event specification, the analytics and the business rules. The scheduling may be indicated by a tuple comprising event content, one or more communication modes selected from the two or more transmission modes, and timing of the scheduled event.

Abstract: Reverse metadata viewing by multiple parties, in one aspect, determines one or more second users that share one or more concerns of the first user, enable the first user to select said one or more second users. One or more concerns of said selected one or more second users are determined based on metadata information in electronic artifacts associated with said selected one or more second users. The first user is enabled to explore said one or more concerns of said selected one or more second users.

Abstract: Reverse metadata viewing by multiple parties, in one aspect, determines one or more second users that share one or more concerns of the first user, enable the first user to select said one or more second users. One or more concerns of said selected one or more second users are determined based on metadata information in electronic artifacts associated with said selected one or more second users. The first user is enabled to explore said one or more concerns of said selected one or more second users.