Abstract: A data warehouse for historic projects can be maintained and can include different artifacts per project, human and organizational resources consumed, intra-artifact temporal dependencies, and timelines. A set of parameters for a new project can be received which can define a scope of the new project at a level of abstraction above an artifact level. Key artifacts and stages needed for completing the project can be established which can be consistent with the set of parameters. Two or more historic projects can be determined to have artifacts/stages similar to the key artifacts/stages of the project. A data driven heuristic algorithms can estimate timelines for producing the key artifacts and the stages based on artifact level data for the historic projects can be executed. A schedule for the project can be generated which can break down the project by the stages and the key artifacts and provide the estimated timelines.

Abstract: A data warehouse for historic projects can be maintained and can include different artifacts per project, human and organizational resources consumed, intra-artifact temporal dependencies, and timelines. A set of parameters for a new project can be received which can define a scope of the new project at a level of abstraction above an artifact level. Key artifacts and stages needed for completing the project can be established which can be consistent with the set of parameters. Two or more historic projects can be determined to have artifacts/stages similar to the key artifacts/stages of the project. A data driven heuristic algorithms can estimate timelines for producing the key artifacts and the stages based on artifact level data for the historic projects can be executed. A schedule for the project can be generated which can break down the project by the stages and the key artifacts and provide the estimated timelines.