Sas Institute Inc.

Charles Michael Cavalier

Steven William Enck

Sarah Jeanette Gauby

Scott Joseph Kolodzieski

Glenn Daniel Sidle

Daniel Kelly

Edward Lew Rowe

James Edward Georges

John Michael Kichak

An event stream processing (ESP) model is read that describes computational processes. (A) An event block object is received. (B) A new measurement value, a timestamp value, and a sensor identifier are extracted. (C) An in-memory data store is updated with the new measurement value, the timestamp value, and the sensor identifier. (A) through (C) are repeated until an output update time is reached. When the output update time is reached, data stored in the in-memory data store is processed and updated using data enrichment windows to define enriched data values that are output. The data enrichment windows include a gate window before each window that uses values computed by more than one window. The gate window sends a trigger to a next window when each value of the more than one window has been computed. The enrichment windows are included in the ESP model.

Automated streaming data model generation with parallel processing capability

A computing device automatically generates an event stream processing (ESP) model to receive and process events. A mapping dataset is created from configuration and device information that includes a measurement processing description for a measurement and a measurement identifier that uniquely identifies a sensor and a device to which the sensor is associated and describe a computational process to be performed by an ESP window. An update macro is executed with an update template to create update code to parse the measurement identifier and a measurement value from the event block object. A transpose macro is executed with a transpose template to create transpose code to perform the described computational process. An ESP model is created that includes the created update code, a source window defined to execute the created update code, the created transpose code, and a transpose window defined to execute the created transpose code.

Automated streaming data model generation

Various embodiments are generally directed to techniques for job flow processing, such as by ordering the performance of parallel tasks in a job flow to minimize a makespan for the job flow, for instance. Some embodiments are particularly directed to ordering the performance of tasks in a job flow based on computation of one or more independent and dependent metrics for tasks in a job flow. In many embodiments, tasks along a critical path of a job flow may be identified and prioritized using the one or more metrics computed for tasks in the job flow. For example, computing a time remaining until end and/or a longest path to end for each task in a job flow may enable a listing of tasks in the job flow to be ordered in a manner that prioritizes tasks to optimize the makespan for the job flow to be executed.

Techniques for job flow processing

An apparatus includes a processor and a storage storing instructions causing the processor to: receive a request for data concerning a target node and neighboring nodes; multiply an identifier of the target node with an index multiplier to determine a location of an index entry of the target node within an index data; access the index entry to retrieve an identifier of a window and an indication of a location within the window at which a node entry of the target node may be accessed, and to retrieve an indication of data size of the node entry; access the node entry within the identified window to retrieve an amount of the node entry up to the data size to retrieve at least one identifier of at least one neighboring node within a single degree of separation of the target node; and output the at least one identifier.

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Apex, NC, United States of America

Charles Michael Cavalier