Drexel researchers have developed an integrated software system that disambiguates, classifies, enriches, and categorizes digital informational objects, such as manuscripts and authors.  Indexing big data troves are computationally expensive endeavors.  With the developed system, the organization and linking of granular data elements of electronic information is optimized.  Computational time is reduced, as the number of iterations to reach convergence is lower with the implementation of incremental affinity propagation than with existing methods.  From applying machine learning algorithms, the system can automatically adjust the timing, scope, and scale of data indexing.  While this software has been applied to journal articles, additional real-world datasets are being tested and accuracy benchmarking is being performed.