Abstract: A method, computer program product, and system for human resource analytics using profile data is described. The method includes receiving historical project data, wherein the historical project data includes at least one profile of at least one historical project team member. Psychometric data associated with the at least one historical project team member is received. Updated historical project data is generated, in which, at least in part, psychometric data associated with the at least one historical project team member is added to the at least one profile included in the historical project data.

Abstract: A method, computer program product, and system for human resource analytics using profile data is described. The method includes receiving historical project data, wherein the historical project data includes at least one profile of at least one historical project team member. Psychometric data associated with the at least one historical project team member is received. Updated historical project data is generated, in which, at least in part, psychometric data associated with the at least one historical project team member is added to the at least one profile included in the historical project data.

Abstract: A method, computer program product, and system for human resource analytics using profile data is described. The method includes receiving historical project data, wherein the historical project data includes at least one profile of at least one historical project team member. Psychometric data associated with the at least one historical project team member is received. Updated historical project data is generated, in which, at least in part, psychometric data associated with the at least one historical project team member is added to the at least one profile included in the historical project data.

Abstract: A method, computer program product, and system for human resource analytics using profile data is described. The method includes receiving historical project data, wherein the historical project data includes at least one profile of at least one historical project team member. Psychometric data associated with the at least one historical project team member is received. Updated historical project data is generated, in which, at least in part, psychometric data associated with the at least one historical project team member is added to the at least one profile included in the historical project data.

Abstract: A method, computer program product, and system for human resource analytics using profile data is described. The method includes receiving historical project data, wherein the historical project data includes at least one profile of at least one historical project team member. Psychometric data associated with the at least one historical project team member is received. Updated historical project data is generated, in which, at least in part, psychometric data associated with the at least one historical project team member is added to the at least one profile included in the historical project data.

Abstract: A method, computer program product, and system for human resource analytics using profile data is described. The method includes receiving historical project data, wherein the historical project data includes at least one profile of at least one historical project team member. Psychometric data associated with the at least one historical project team member is received. Updated historical project data is generated, in which, at least in part, psychometric data associated with the at least one historical project team member is added to the at least one profile included in the historical project data.

Abstract: A mechanism is provided to optimize cooling of a data center having a plurality of computer systems. A dataset is classified into a plurality of data storage regions hosted by at least one of the plurality of computer systems. A plurality of cooling devices is grouped into groups. Each group of the plurality of cooling devices cools at least one of the plurality of data storage regions. The classified dataset is analyzed to identify one of the plurality of data storage regions having heat-intensive data. An operating parameter is selectively adjusted for one of the group of the plurality of cooling devices that corresponds to cooling the identified one of the plurality of data storage regions having the heat-intensive data.

Abstract: A method, computer program product, and system for human resource analytics using profile data is described. The method includes receiving historical project data, wherein the historical project data includes at least one profile of at least one historical project team member. Psychometric data associated with the at least one historical project team member is received. Updated historical project data is generated, in which, at least in part, psychometric data associated with the at least one historical project team member is added to the at least one profile included in the historical project data.

Abstract: A method, computer program product, and system for human resource analytics using profile data is described. The method includes receiving historical project data, wherein the historical project data includes at least one profile of at least one historical project team member. Psychometric data associated with the at least one historical project team member is received. Updated historical project data is generated, in which, at least in part, psychometric data associated with the at least one historical project team member is added to the at least one profile included in the historical project data.

Abstract: There is provided a method for providing a function for use in detecting a presence of an exception in multidimensional data. The method comprises the steps of (a) partitioning the multidimensional data into at least a first region and a second region; (b) assigning a first region-specific function to the first region and a second region-specific function to the second region; and (c) determining a combined function from the first region-specific function and the second region-specific function. The combined function is used to calculate an expected value of the multidimensional data for distinguishing the presence of an exception.

Abstract: Systems, methods, and computer products that interpret, explain, and manipulate exceptions in multidimensional data. The present invention assists the data analyst by providing a simplified view of the multidimensional data that enables analysis of the important results of data exception exploration. Further, the preferred embodiment of the present invention incorporates the effect of density of the data along each dimension. The preferred embodiment of the present invention also provides the framework necessary to assign linguistic meaning to the exception for each dimension. This enables data analysis to obtain information about the value of the data that is present.

Abstract: There is provided a method for selecting a function for use in detecting a presence of an exception in multidimensional data. The method comprises the steps of (a) quantifying a characteristic of the multidimensional data, (b) determining a weighting factor of a function based on the quantified characteristic, and (c) selecting the function from a plurality of functions based on the weighting factor. The function is used for distinguishing the presence of an exception.

Abstract: Systems, methods, and computer products that interpret, explain, and manipulate exceptions in multidimensional data. The present invention assists the data analyst by providing a simplified view of the multidimensional data that enables analysis of the important results of data exception exploration. Further, the preferred embodiment of the present invention incorporates the effect of density of the data along each dimension. The preferred embodiment of the present invention also provides the framework necessary to assign linguistic meaning to the exception for each dimension. This enables data analysts to obtain information about the value of the data that is present.

Abstract: There is provided a method for selecting a function for use in detecting a presence of an exception in multidimensional data. The method comprises the steps of (a) quantifying a characteristic of the multidimensional data, (b) determining a weighting factor of a function based on the quantified characteristic, and (c) selecting the function from a plurality of functions based on the weighting factor. The function is used for distinguishing the presence of an exception.

Abstract: There is provided a method for providing a function for use in detecting a presence of an exception in multidimensional data. The method comprises the steps of (a) partitioning the multidimensional data into at least a first region and a second region; (b) assigning a first region-specific function to the first region and a second region-specific function to the second region; and (c) determining a combined function from the first region-specific function and the second region-specific function. The combined function is used to calculate an expected value of the multidimensional data for distinguishing the presence of an exception.