Abstract: Architecture that decomposes of one or more monolithic data concepts into atomic concepts and related atomic concept dependencies, and provides streaming data processing that processes individual or separate (atomic) data concepts and defined atomic dependencies. The architecture can comprise data-driven data processing that enables the plug-in of new data concepts with minimal effort. Efficient processing of the data concepts is enabled by streaming only required data concepts and corresponding dependencies and enablement of the seamless configuration of data processing between stream processing systems and batch processing systems as a result of data concept decomposition. Incremental and non-incremental metric processing enables realtime access and monitoring of operational parameters and queries.

Abstract: Architecture that decomposes of one or more monolithic data concepts into atomic concepts and related atomic concept dependencies, and provides streaming data processing that processes individual or separate (atomic) data concepts and defined atomic dependencies. The architecture can comprise data-driven data processing that enables the plug-in of new data concepts with minimal effort. Efficient processing of the data concepts is enabled by streaming only required data concepts and corresponding dependencies and enablement of the seamless configuration of data processing between stream processing systems and batch processing systems as a result of data concept decomposition. Incremental and non-incremental metric processing enables realtime access and monitoring of operational parameters and queries.

Abstract: A natural language query may be transformed to a transformed natural language while keeping sufficient semantic meaning such that the query may be transformed. A natural language query may be received by a computing device and sent to a natural language transformation model for transformation. The transformation may use a variety of techniques including stop word removal, stop structure removal, noun phrase/entity detection, key concept detection, dependency filtering. The techniques may be sequenced.

Abstract: Architecture that decomposes of one or more monolithic data concepts into atomic concepts and related atomic concept dependencies, and provides streaming data processing that processes individual or separate (atomic) data concepts and defined atomic dependencies. The architecture can comprise data-driven data processing that enables the plug-in of new data concepts with minimal effort. Efficient processing of the data concepts is enabled by streaming only required data concepts and corresponding dependencies and enablement of the seamless configuration of data processing between stream processing systems and batch processing systems as a result of data concept decomposition. Incremental and non-incremental metric processing enables realtime access and monitoring of operational parameters and queries.

Abstract: A workflow scripting system is described herein that combines the features of workflows and scripts by automatically translating between the two models. Using the system, a script author can create workflows on the fly using familiar scripting language, and a workflow author can use scripting steps to perform actions. Workflows run in this manner can be setup to execute in their own process to improve robustness or efficiency. Operations in an enterprise environment frequently take a long time and are subject to interruptions. By adding reliability concepts of workflows to a shell environment, users of the system can write scripts to address common needs of large-scale computing environments. Thus, the workflow scripting system blends the available resources provided by workflow and scripting environments to provide a host of powerful, advanced capabilities to IT personnel.

Abstract: A workflow scripting system is described herein that combines the features of workflows and scripts by automatically translating between the two models. Using the system, a script author can create workflows on the fly using familiar scripting language, and a workflow author can use scripting steps to perform actions. Workflows run in this manner can be setup to execute in their own process to improve robustness or efficiency. Operations in an enterprise environment frequently take a long time and are subject to interruptions. By adding reliability concepts of workflows to a shell environment, users of the system can write scripts to address common needs of large-scale computing environments. Thus, the workflow scripting system blends the available resources provided by workflow and scripting environments to provide a host of powerful, advanced capabilities to IT personnel.