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: 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 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 Symbolic Job Expander enables people and programs to use convenient, symbolic job request expressions to perform complex operations on large numbers of collections with essentially no human effort involved. Collections are data-typed sets of computer files that can be manipulated as a set, rather than as individual files. In operation, a Collection Symbolic Job Expander receives a collection symbolic job request from a request originator, and performs a collection job expansion action on the request to produce a list of expanded job requests. Each expanded job request is comprised of a collection name, a user-defined computing platform name, and a processing dependency order ranking. Collection Symbolic Job Expanders improve human productivity by enabling people to perform operations on large sets of collections without being required to provide low-level processing details such as specific collection names, specific computing platform assignments, or processing dependency information.

Abstract: A Collection Processing System enables people to perform complex computational operations on large sets of collections, using a simple and convenient syntax for collection symbolic job requests. A collection symbolic job request is comprised of a user-defined symbolic task name, and a collection reference expression that can refer to large sets of collections. Collections are data-typed sets of computer files that can be manipulated as a set, rather than as individual files. In operation, a Collection Processing System receives a collection symbolic job request from a request originator, and expands the symbolic job request into a list of executable commands that carry out the computational intent of the symbolic job request. First-level symbolic task names are expanded into sequences of second-level task part statements, and then into third-level executable computer commands that are dynamically generated into customized, optimal makefiles.

Abstract: A wall mounted alarm switch assembly intended to be positioned at the site of an existing electric circuit includes a remote mechanical actuating mechanism for the switch assembly. The actuating mechanism comprises dual side-by-side manual interfaces or knobs which are positioned at a location immediately below the position of the switch assembly. The manual interfaces are connected to the switch assembly by rigid members. The distance between the manual interfaces and the switch assembly is adjustable. The alarm switch is activated when both interfaces are moved, each in a direction away from the other, thereby separating the two interfaces.