Abstract: Collection content classifiers classify computer files and other collection content in accordance with predetermined classification specifications. In operation, collection content classifiers dynamically discover collection content members, assign data types to content members, associate symbolic actions with content members, determine processing dependencies among content members, and return organized classification information to calling programs for subsequent use in processing content members. Collection content classifiers thereby improve the productivity of human knowledge workers by enabling the construction of smart, collection-aware application programs that can perform more extensive, more complex, and more automated collection processing operations than were previously possible.

Abstract: Collection command applicators improve the productivity of human knowledge workers by enabling them to automatically process of large numbers of collections of arbitrary computer files. In operation, collection command applicators dynamically recognize interesting collections, calculate an optimal execution dependency visit order, determine an optimal parallel execution order, and apply arbitrary computer commands to the recognized collections in accordance with the calculated execution orderings. Collection command applicators thereby make it possible to process large numbers of collections in automated, scalable ways that were not previously possible.

Abstract: Collection makefile generators generate comprehensive makefiles for processing collections of computer files. In operation, the present collection makefile generator dynamically discovers collection content files, classifies them according to content type and required processing actions, and then generates a makefile for performing those actions. Importantly, all build order dependencies are properly maintained among multiple collection products and among individual collection content files. Automated collection makefile generators drastically improve the productivity of human workers by effectively reducing makefile creation and maintenance costs to zero. Collection makefile generators thus enable humans to easily generate complex makefiles in an automated, scalable way that was not previously possible.

Abstract: Collection information managers improve the productivity of knowledge workers by organizing information about arbitrary collections of computer files into collection data structures, for use by automated collection processing programs. Three kinds of knowledge are obtained and organized by collection information managers: collection instance information, collection content information, and collection processing information. Software programs can use information in collection data structures to precisely understand and process collections in useful ways that were not previously possible.