Abstract: Examples of techniques for performing a skill gap comparison between a first individual and a second individual (or group) are disclosed. In one example, a computer-implemented method includes receiving first data associated with the first individual and receiving second data associated with the second individual. The method further includes identifying preferences for performing the skill gap comparison and assigning a weight for each of a plurality of elements of the first data and the second data. The method further includes performing the skill gap comparison between the first individual and the second individual by comparing the first data to the second data. The method further includes generating a list of one or more skills for which there is a skill gap between the first individual and the second individual.

Abstract: In one example implementation according to embodiments of the invention, a computer-implemented method includes identifying a first discrete element of the composite object from a first image. The method further includes identifying the composite object based at least in part on the first discrete element of the composite object. The method further includes determining whether a category associated with the composite object is a category of interest. The method further includes, based at least in part on determining that the category associated with the composite object is a category of interest, increasing a discrete element count. The method further includes determining whether the discrete element count meets a threshold. The method further includes, based at least in part on determining that the discrete element count exceeds the threshold, performing a mitigation action.

Abstract: Examples of techniques for performing a skill gap comparison between a first individual and a second individual (or group) are disclosed. In one example, a computer-implemented method includes receiving first data associated with the first individual and receiving second data associated with the second individual. The method further includes identifying preferences for performing the skill gap comparison and assigning a weight for each of a plurality of elements of the first data and the second data. The method further includes performing the skill gap comparison between the first individual and the second individual by comparing the first data to the second data. The method further includes generating a list of one or more skills for which there is a skill gap between the first individual and the second individual.

Abstract: In one example implementation according to embodiments of the invention, a computer-implemented method includes identifying a first discrete element of the composite object from a first image. The method further includes identifying the composite object based at least in part on the first discrete element of the composite object. The method further includes determining whether a category associated with the composite object is a category of interest. The method further includes, based at least in part on determining that the category associated with the composite object is a category of interest, increasing a discrete element count. The method further includes determining whether the discrete element count meets a threshold. The method further includes, based at least in part on determining that the discrete element count exceeds the threshold, performing a mitigation action.