Abstract: Computing systems, methods and collaboration tracking tools for predicting, tracking, and reporting the occurrence of unscheduled collaborations between two or more contributors to a project using computer systems equipped capabilities to transmit and/or receive location information of each individual contributor. An analytics module receiving the location information may use the location data to identify when two or more contributors to a project are in the same location together, and further predict whether the proximity of location and timing of the contributors crossing paths will predictably lead to an undocumented project-related collaboration determined as a function of location information, historical data and input collected. The collaboration tracking tools predict, report and/or record collaborations of project-related topics.

Abstract: A set of internal information technology (IT) architecture is received and the internal IT architecture is controlled by a service provider. A set of exogenous IT architecture is received and the exogenous IT architecture is not controlled by the service provider. A set of service level agreement (SLA) requirements, agreed upon by the service provider and a service user, are received. A set of sensor data, from sensors monitoring environmental conditions which may affect at least one of the internal IT architecture or the exogenous IT architecture, is received. A set of heuristic data relating to the internal IT architecture and the exogenous IT architecture is received. A risk metric based on the set of internal IT architecture, the set of exogenous IT architecture, the set of SLA requirements, the set of sensor data, and the set of heuristic data is determined.