Abstract: Systems and methods for automated or computer assisted planning of pipe replacement projects includes searching a database of pipe segments each having assigned or calculated failure risk parameters. The searching algorithm looks for connected pipe segments falling within a customer's specified project size and identifies potential projects having a relatively high combined high failure risk.

Abstract: An improved solution accurately predicts of an underground pipe's likelihood of leaking. A data-driven approach uses a combination of information acquisition, classification, regression and/or machine learning. The replacement of underground pipes can be prioritized. Pipe data is inputted and processed. Potential features within the cleaned data is used in pipe life of failure prediction models. The importance of the potential features is ranked. The most important features are extracted and applied to a likelihood of failure model that is created based on historical data and machine learning. Future likelihood of failure for each pipe in the network of pipes can be predicted using the model.

Abstract: Collaborative skills management includes techniques for maintaining up-to-date skills data. Skill data for users is updated by both the users themselves as well as other users. Various rules can be applied depending on the application. Skill data can also be updated based on analysis of collaboration data.

Abstract: Skill data management techniques include extracting keywords for skill data obtained from experiences of individuals. Skill data management includes representing skill data in graph format including edges and nodes, suppressing duplicate data, and managing aliases. Managed skill data can be used for hiring, recruiting, internal placement, career planning, training, and issue solving.

Abstract: A platform for skill data management includes a search engine with recommendation algorithm to search for experts, a platform to rate advisors, report hidden contributions, and endorse skills for other users, a function to evaluate work efficiency and advisor's help quantitatively, and audio/video chat functionality that is optimized for a short and verbal conversation.

Abstract: Systems and methods for automated or computer assisted planning of pipe replacement projects includes searching a database of pipe segments each having assigned or calculated failure risk parameters. The searching algorithm looks for connected pipe segments falling within a customer's specified project size and identifies potential projects having a relatively high combined high failure risk.

Abstract: Systems and methods calculate consequence of failure values for pipe segments in a network. The calculation can be based on an estimated repair cost, a monetary value associated with the loss of service to customers and also a monetary value associated with the interruption of transportation such as traffic interruption. The calculation can also take into account a pipe segments proximity to a critical facility.

Abstract: A system receives and automatically transforms utility pipe attribute data and pipe break data. The missing and/or incorrect entries in the pipe attributes and/or break data is automatically identified and correct values for these entries are is automatically imputed to generate improved datasets of the pipe attribute data and break data. The improved data can be used to build a model with machine learning. Predictions of future likelihood of failure for pipe sections in a network of pipes can be made based on the model. A national database can be created that is filled with environmental data that has been transformed, optimized, merged, and imputed. The national database can be used for many customers to save computational costs. The national database can be used to build the failure prediction model for utility companies thereby saving computational costs.

Abstract: An improved solution accurately predicts of an underground pipe's likelihood of leaking. A data-driven approach uses a combination of information acquisition, classification, regression and/or machine learning. The replacement of underground pipes can be prioritized. Pipe data is inputted and processed. Potential features within the cleaned data is used in pipe life of failure prediction models. The importance of the potential features is ranked. The most important features are extracted and applied to a likelihood of failure model that is created based on historical data and machine learning. Future likelihood of failure for each pipe in the network of pipes can be predicted using the model.