Abstract: Recommendations for a media item associated with a primary entity are based on co-interaction information gathered from other media content items of several different media types that are also associated with the primary entity. Co-interaction information can include, for example, co-click data for websites, co-watch data for videos, or co-purchase data for purchases. The co-interaction data is processed to determine a co-interaction score between primary media items and secondary media items. From the co-interaction scores, secondary entities associated with the secondary media items are determined. A relatedness score is determined for these secondary entities based on the aggregation of the co-interaction scores of the secondary media items they are associated with. The relatedness score indicates a determination of how related one entity is to another. The secondary entities are ranked according to relatedness score in order to determine secondary entities most relevant to the primary entity.

Abstract: Recommendations for a media item associated with a primary entity are based on co-interaction information gathered from other media content items of several different media types that are also associated with the primary entity. Co-interaction information can include, for example, co-click data for websites, co-watch data for videos, or co-purchase data for purchases. The co-interaction data is processed to determine a co-interaction score between primary media items and secondary media items. From the co-interaction scores, secondary entities associated with the secondary media items are determined. A relatedness score is determined for these secondary entities based on the aggregation of the co-interaction scores of the secondary media items they are associated with. The relatedness score indicates a determination of how related one entity is to another. The secondary entities are ranked according to relatedness score in order to determine secondary entities most relevant to the primary entity.

Abstract: A computer implemented method and system for providing price updates is provided. Data is received by a system. The received data is filtered by the system to identify triggers in the received data. Actions related to identified triggers are performed by the system. Business rule compliance is performed according to a business rule hierarchy. Updated prices are provided.

Abstract: A computer implemented method and system for providing price updates is provided. Data is received by a system. The received data is filtered by the system to identify triggers in the received data. Actions related to identified triggers are performed by the system. Business rule compliance is performed according to a business rule hierarchy. Updated prices are provided.

Abstract: A scheduling engine and an associated workflow engine may be configured to build workflows that describe sequences of tasks to be performed in a dynamically changing environment. The workflow engine may be configured to monitor the execution of the tasks within the environment and to provide the scheduling engine with information regarding deviations of task executions from the workflows. When such deviations are detected, the workflow engine may trigger the scheduling engine to rebuild the workflows for as yet uncompleted tasks. The scheduling engine may receive the information regarding the deviations via a declarative model of the physical environment. In general, the tasks may be a set of job requests to be processed in a manufacturing environment (e.g., a print shop). Also, the scheduling engine may be made up of an aggregate planner and a detailed scheduler. Aggregate plans for the jobs may be first made for the jobs and then passed to the detailed scheduler for timelining, etc.

Abstract: An order promising scheme includes the ability to query a hierarchical scheduling module configured to generate workflows for a series of jobs to be processed in a manufacturing environment to determine the earliest reasonable completion time for one or more additional jobs to be processed in the manufacturing environment. The querying may involve first querying an aggregate planning level of the hierarchical scheduling module to determine whether the one or more additional jobs may be processed. Further, the querying may require querying a detailed scheduling level of the hierarchical scheduling module to determine whether the one or more additional jobs may be processed in the event that the aggregate planning level returns a negative reply to the first query by the order promising module. The querying of the hierarchical scheduling module may be performed in response to user inquiries submitted via a user interface.

Abstract: An aggregate plan for a set of job requests to be processed in a manufacturing environment may be produced given capacities for resources of the manufacturing environment, the capacities being represented by bins in multiple dimensions, some of which may be used by the jobs on an alternative basis. In such a scheme, producing the aggregate plan may be accomplished by selecting a subset of the job requests according to a procedure for packing the bins. For example, packing the bins may be accomplished by selecting from among ordered lists of the job requests grouped according to costs for one or more of the resources. The ordered lists of job requests may be ordered in terms of value. In some cases, at least some of the job requests may have different values versus cost for different ones of the resources. Each alternative be represented as a separate bin.

Abstract: A workflow generated from a declarative model of a real world process represents the procedural steps to accomplish the process. In one embodiment, the declarative model may include a first number of state nodes and a second number of task nodes interconnected with the state nodes. In such cases, generating a workflow may be accomplished by choosing one of a number of paths through the model, each of the paths comprising an alternating series of one or more of the state nodes and one or more of the task nodes with any predecessor state node in one of the paths representing a precondition for a subsequent task node along the one of the paths and any following state node of the subsequent task node along the one of the paths representing a result of applying one or more actions that correspond to that task node. As part of a computer assisted scheduling system, the model may be configured to receive updates reflecting changes in the real-world manufacturing environment.