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 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.

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: A Collection Knowledge System provides context-sensitive knowledge delivery services to application programs, thereby enabling application programs to effectively support variant computational processes. In operation, a Collection Knowledge System receives knowledge requests from application programs, performs local and remote lookups into structured trees of knowledge, and returns requested knowledge to requesting application programs. Collection Knowledge Systems can manage knowledge for entire variant computational processes, thereby enabling application programs to calculate and execute many variant computational processes in fully automated, scalable ways that were not previously possible. As a consequence of automated process construction and execution, associated human labor costs are effectively reduced to zero.

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 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: A Collection Knowledge System provides context-sensitive knowledge delivery services to application programs, thereby enabling application programs to effectively support variant computational processes. In operation, a Collection Knowledge System receives knowledge requests from application programs, performs local and remote lookups into structured trees of knowledge, and returns requested knowledge to requesting application programs. Collection Knowledge Systems can manage knowledge for entire variant computational processes, thereby enabling application programs to calculate and execute many variant computational processes in fully automated, scalable ways that were not previously possible. As a consequence of automated process construction and execution, associated human labor costs are effectively reduced to zero.

Abstract: Collection recognizers improve the productivity of knowledge workers by enabling automated systems to recognize interesting collections of arbitrary computer files for automated processing. In operation, a collection recognizer detects collections within a search space, selects interesting collections from the group of detected collections, and finally makes information about the selected collections available to software programs for subsequent automated processing. Collection recognizers help to enable the construction of fully automated collection processing systems.

Abstract: A Collection View Expander process improves human productivity by expanding collection views into detailed collection view information that can be used by application programs to manipulate collection views in advanced ways that were not previously possible. Collection views are groups of related collection references that can be processed by human workers using a single operation on a single set of collection references, rather than by performing a series of individual operations on individual collections. In operation, a Collection View Expander receives expansion requests from request originators, expands collection views into collection view information using detailed, user-defined collection view member reference type definitions, and returns aggregated collection view information to the request originators. Collection View Expanders improve human productivity by enabling people to represent and manipulate whole groups of collections as collection views.

Abstract: A Collection Shortcut Expander method expands shortcut collection reference expressions into complete reference expressions, thereby improving the productivity of people who work with collections. In operation, a Collection Shortcut Expander receives expansion requests from request originators, expands shortcut collection reference expressions into complete collection reference expressions, and returns expanded references to the request originators. Shortcut references to local collections are expanded using information from a current collection specifier file. Shortcut references to remote collections are expanded using information from a knowledge base. Collection Shortcut Expanders improve human productivity by providing convenient syntaxes, by reducing typing burdens, by reducing knowledge burdens, by reducing human retraining costs caused by collection name changes, and by enabling people to create customized alias names for frequently used collection names.

Abstract: A Collection Storage System helps software developers to manage the storage and evolution of collections of computer files, by performing collection storage operations on collections and collection views. Collections are sets of computer files that can be manipulated as a set, rather than as individual files. Collection views are groups of related collection references that can be processed by human workers using a single operation on a single set of collection references, rather than by performing a series of individual operations on individual collections. In operation, a Collection Storage System receives collection storage operation requests from request originators. It performs the operations, often in the presence of collection information and collection view information that describes the target collections, views, and operations in detail. Finally, it returns operation results to the request originators.

Abstract: A Collection Extensible Action GUI (graphical user interface) is constructed from user-defined, executable GUI actions, thereby making it possible for users to customize and extend the GUI to suit their precise computational needs.

Abstract: A Collection Adaptive Focus GUI adapts to changes in user work situations, thereby providing human workers with a responsive, customized graphical user interface. Changes in work situations are indicated by various focusing events such as changing current work contexts, work locations, work objects, and work roles.

Abstract: Collection recognizers improve the productivity of knowledge workers by enabling automated systems to recognize interesting collections of arbitrary computer files for automated processing. In operation, a collection recognizer detects collections within a search space, selects interesting collections from the group of detected collections, and finally makes information about the selected collections available to software programs for subsequent automated processing. Collection recognizers help to enable the construction of fully automated collection processing systems.

Abstract: A Collection Knowledge System provides context-sensitive knowledge delivery services to application programs, thereby enabling application programs to effectively support variant computational processes.

Abstract: A Collection Knowledge System provides context-sensitive knowledge delivery services to application programs, thereby enabling application programs to effectively support variant computational processes.

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 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 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.