Abstract: An analytical computing environment for large data sets comprises a software platform for data management. The platform provides various automation and self-service features to enable those users to rapidly provision and manage an agile analytics environment. The platform leverages a metadata repository, which tracks and manages all aspects of the data lifecycle. The repository maintains various types of platform metadata including, for example, status information (load dates, quality exceptions, access rights, etc.), definitions (business meaning, technical formats, etc.), lineage (data sources and processes creating a data set, etc.), and user data (user rights, access history, user comments, etc.). Within the platform, the metadata is integrated with all platform services, such as load processing, quality controls and system use. As the system is used, the metadata gets richer and more valuable, supporting additional automation and quality controls.

Abstract: An analytical computing environment for large data sets comprises a software platform for data management. The platform provides various automation and self-service features to enable those users to rapidly provision and manage an agile analytics environment. The platform leverages a metadata repository, which tracks and manages all aspects of the data lifecycle. The repository maintains various types of platform metadata including, for example, status information (load dates, quality exceptions, access rights, etc.), definitions (business meaning, technical formats, etc.), lineage (data sources and processes creating a data set, etc.), and user data (user rights, access history, user comments, etc.). Within the platform, the metadata is integrated with all platform services, such as load processing, quality controls and system use. As the system is used, the metadata gets richer and more valuable, supporting additional automation and quality controls.

Abstract: An analytical computing environment for large data sets comprises a software platform for data management. The platform provides various automation and self-service features to enable those users to rapidly provision and manage an agile analytics environment. The platform leverages a metadata repository, which tracks and manages all aspects of the data lifecycle. The repository maintains various types of platform metadata including, for example, status information (load dates, quality exceptions, access rights, etc.), definitions (business meaning, technical formats, etc.), lineage (data sources and processes creating a data set, etc.), and user data (user rights, access history, user comments, etc.). Within the platform, the metadata is integrated with all platform services, such as load processing, quality controls and system use. As the system is used, the metadata gets richer and more valuable, supporting additional automation and quality controls.

Abstract: A data structure has a value field for storing values and a lock field for indicating a lock state of the resource associated with the data structure. This data structure is used to implement a locking protocol amongst multiple processes competing for shared resources. If the lock field is a locked state, the process that locked the lock field has exclusive access to the resource. If, however, the lock field is in an unlocked state, any process may gain access to the resource. The lock field also has deferred states corresponding to the locked and unlocked states wherein multiple requests to lock or unlock the resource may be stored until serviced. The locking protocol is useful in a data processing system having multiple processing elements and utilizes a locking means that includes a local memory controller.

Abstract: A system and method for data processing in which data processing tasks are arranged as separate processes, each of which can be executed by a dedicated central processing unit or by one or more shared processing units. All communication between processes takes place via streams of data items. Each stream receives data items from a single source, for example from an input to the processing system or from one of the processes. Every data item appended to a stream is associated with a unique index value--even if physical memory locations are re-used, index values never are. A process can request a data item in terms of the stream where it is stored and the index value associated with it. Within constraints imposed by physical storage capacity, any index value can be included in a request. If the data item is not yet available, execution of the process is suspended until the data item becomes available.

Abstract: A method and system for computer programming provides a graphical editor function for creation and editing of a computer program by manipulation of graphical images on a high-resolution display. The program is specified in terms of a definition language which can be executed by a simulation function. This function simulates execution of the program on a different, target processor as if the program were expressed in a different language which can be executed by the target processor; in addition the simulation function incorporates a simulation of the external environment of the target processor and of the interaction of the program with that environment. The graphical display of the program is animated during execution so that the user can observe and check the program's operation. When the simulated execution is satisfactory the definition-language version of the program is tanslated into the language executable on the target processor.