Abstract: An apparatus for extended horizon scheduling that includes a time-sequence interpretation circuit, a target interpretation circuit, a horizon target analysis circuit, and a promotive action provisioning circuit. The time-sequence interpretation circuit interprets time-sequence data, the target interpretation circuit interprets target data, and the time-sequence trend analysis circuit extracts a trend from the time-sequence data. The horizon target analysis circuit: determines whether the extracted trend furthers or impedes a target defined, in part, by the target data, and responsive to a determination that the extracted trend impedes the target, generates a promotive action command value structured to trigger an adjustment to the time-sequence data. The adjustment is structured to mitigate the extracted trend from impeding the target. The promotive action provisioning circuit transmits the promotive action command value.

Abstract: Networks, methods, and apparatus to ensure schedule conformance are described. An example apparatus may include a schedule interpretation circuit to interpret schedule data, and a warden circuit to generate scores for the schedule data with respect to schedule properties. The warden circuit retrieves baseline values corresponding to schedule properties and determines whether the schedule data is out of alignment with the baseline value for any of the schedule properties. If so, a corrective action circuit generates a corrective action command value to trigger an adjustment to the schedule data to effect a change to a schedule property that is out of alignment. A corrective action provisioning circuit transmits the corrective action command value.

Abstract: Data associated with actions performed by workers of an organization is identified from multiple sources associated with the workers or the organization. The identified data is provided as input to a machine learning model that is trained to predict, based on data indicating actions of workers, a benefaction level associated with the workers relative to other workers. One or more outputs are obtained from the machine learning model, which indicate a respective benefaction level for each of the workers. A determination is made of whether the respective benefaction level for a worker satisfies criteria associated with a goal defined by the worker and/or the organization. A database associated with the organization is updated to indicate whether the respective benefaction level for a worker satisfies the criteria associated with the goal defined by the worker and/or the organization.

Abstract: A request for one or more digital badges associated with a particular worker of an organization is received. A team of the organization associated with the particular worker during a tenure of the particular worker at the organization is determined. One or more entries of a digital badge database that are associated with the team are identified. The entries indicate one or more digital badges issued to the team based on a benefaction level determined for workers of the team. A set of digital badges issued to the team during a time period that corresponds to the tenure of the particular worker is extracted from the one or more entries. An indication of the extracted set of digital badges is provided in response to the request.

Abstract: An apparatus that includes a scenario interpretation circuit, a scenario analysis circuit, a data analysis circuit, a data source locator circuit, and a data retrieval circuit. The scenario interpretation circuit is structured to interpret time-sequence scenario data. The scenario analysis circuit is structured to extract a scenario element from the time-sequence scenario data. The data analysis circuit is structured to determine, based at least in part on the extracted scenario element, a type of data for inclusion in the generation of time-sequence data corresponding to the scenario data. The data source locator circuit is structured to identify a source of the type of data for inclusion in the generation of the time-sequence data. The data retrieval circuit structured to retrieve data from the identified source. The data provisioning circuit structured to transmit the retrieved data.

Abstract: An apparatus and method for adjusting a time sequence in response to a predicted or detected austere event. The apparatus and method include a time sequence interpretation circuit to interpret time sequence data and provide it to a mitigation circuit. The mitigation circuit processes the interpreted time sequence data along with austere event data to determine if an austere event is occurring or is likely to occur. Upon detection of an austere event, the mitigation circuit generates a mitigation action command value structured to trigger a change to the time sequence sufficient to mitigate the effect of the austere event on the time sequence. In certain aspects, the apparatus and method further include an external event interpretation circuit and an austere event detection circuit which monitor external event data to generate austere event data. In certain aspects, the austere event detection circuit and the mitigation circuit include machine learning elements.

Abstract: Embodiments of the current disclosure provide for an apparatus for sequence mimicking. The apparatus includes a historic sequence interpretation circuit, a mimicking circuit, and a sequence data provisioning circuit. The historic sequence interpretation circuit is structured to interpret historical sequence data corresponding to a sequence designed, in part, by an entity. The mimicking circuit is structured to: extract a sequence trend from the historical sequence data; identify a portion of the historical sequence data corresponding to the extracted sequence trend; and generate sequence data based at least in part on the identified portion. The sequence data provisioning circuit is structured to transmit the sequence data.

Abstract: An apparatus including a historic schedule interpretation circuit structured to: interpret historical schedule data; and extract difficult schedule features from the historical schedule data; an incentive determination circuit structured to identify incentives compatible with the difficult schedule features; a schedule experimentation circuit structured to: receive schedule modification parameters; and generate, based at least in part on the schedule modification parameters, a set of experimental schedules each with different incentives of the set of incentives.

Abstract: A method, including: generating a plurality of initial schedules for an agglomerate network, including a first number of schedules, and determining a threshold number of schedules, including: evaluating the plurality of initial schedules to select a first schedule, identifying a first place number of the first schedule, if the first place number is greater than or equal to a first threshold number, the first threshold number being within two of the threshold number: increasing the threshold number of schedules to a second threshold number, greater than the first threshold number, and generating a second plurality of schedules equal to the second threshold number, evaluating the second plurality of schedules to select a second schedule, identifying a second place number of the second schedule, and if the second place number is ‘1’ or ‘2’, decreasing the threshold number of schedules to a third threshold number, less than the first threshold number.

Abstract: Networks, methods, and apparatus to ensure schedule conformance are described. An example apparatus may include a schedule interpretation circuit to interpret schedule data, and a warden circuit to generate scores for the schedule data with respect to schedule properties. The warden circuit retrieves baseline values corresponding to schedule properties and determines whether the schedule data is out of alignment with the baseline value for any of the schedule properties. If so, a corrective action circuit generates a corrective action command value to trigger an adjustment to the schedule data to effect a change to a schedule property that is out of alignment. A corrective action provisioning circuit transmits the corrective action command value.

Abstract: An apparatus for extended horizon scheduling that includes a time-sequence interpretation circuit, a target interpretation circuit, a horizon target analysis circuit, and a promotive action provisioning circuit. The time-sequence interpretation circuit interprets time-sequence data, the target interpretation circuit interprets target data, and the time-sequence trend analysis circuit extracts a trend from the time-sequence data. The horizon target analysis circuit: determines whether the extracted trend furthers or impedes a target defined, in part, by the target data, and responsive to a determination that the extracted trend impedes the target, generates a promotive action command value structured to trigger an adjustment to the time-sequence data. The adjustment is structured to mitigate the extracted trend from impeding the target. The promotive action provisioning circuit transmits the promotive action command value.

Abstract: In embodiments, an apparatus includes a user data interpretation circuit structured to interpret user data corresponding to a first user; a bootstrap circuit structured to: match the first user to a second user via querying one or more databases based at least in part on the user data; retrieve historical time sequence data associated with the second user via querying the one or more databases; extract a time sequence trend from the historical time sequence data; identify a portion of the historical time sequence data corresponding to the extracted time sequence trend; and generate, based at least in part on the identified portion, time sequence data corresponding to the first user; and a time sequence data provisioning circuit structured to transmit the time sequence data.

Abstract: An apparatus for timekeeping and scheduling is disclosed. The apparatus includes a user surveyor circuit structured to interpret user data and an embedding generator circuit structured to determine user embeddings based at least in part on the user data. The apparatus further includes an artificial intelligence circuit structured to generate a model based at least in part on the user embeddings. Further, the apparatus has a scheduling circuit structured to generate time sequence data via the model. The generated time sequence data is transmitted via a time sequence provisioning circuit.

Abstract: Apparatus and methods for enabling workers to compete for currently upcoming shifts are described. In an example, an apparatus may include a schedule display circuit to display a customized view of upcoming shifts where the customization is based on the identify of a worker, their skills/properties and shift requirements. All the workers may have a common employer. A bidding interface circuit may enable the workers to bid on upcoming shifts shown in their customized view. A bid evaluation circuit may evaluate the submitted bids, determine a quality of each bid and determine a winning bid for each shift. A shift allocation circuit then assigns upcoming shifts to workers based on the winning bid.

Abstract: Embodiments of the invention provide techniques for generating predicted cardinality statistics for grouped aggregation functions included in database queries. In general, characteristics of a database query are determined, and are then supplied to a probability function configured to generate a predicted cardinality statistic. The generated statistic represents a prediction of the probable cardinality of the results of a grouped aggregation function in the event that the query is executed. The predicted cardinality statistic may be used by a query optimizer to determine an efficient query plan for executing the database query.

Abstract: A database query optimizer allows specifying a performance specification for a query. When an access plan is created, the actual performance for executing the query using the access plan is determined and compared to the performance specification for the query. If the actual performance does not satisfy the performance specification for the query, the query optimizer autonomically generates a different access plan to see if the performance improves. If an access plan that meets the performance specification is generated, the new access plan is stored in the access plan cache for the query. If no access plan can be generated that meets the performance specification for the query, the access plan that provided the best performance is selected and stored in the access plan cache for the query.

Abstract: Methods, systems, and computer program products are provided for improving the processing of database queries. Some embodiments include detecting an overly long running query execution plan, stopping execution of the plan, and utilizing the query optimizer to select an alternate plan for execution. Embodiments may utilize system timers which are set with a time limit for the first selected plan, with instructions to re-optimize and initiate selection of an alternate plan when the timer times out. Risky constructs may be located in the first plan and removed from future consideration for executing the query. In some embodiments, the additional overhead for non-problem queries consists of little more than creating and sending a message or setting a timer when starting query execution, and canceling a timer upon successful query processing completion. A secondary thread may be created responsive to detecting a long running query, to supervise selecting an alternate execution plan.

Abstract: A self-healing database engine and optimizer framework support automatically responding to execution errors to allow continued execution of a query plan. Upon encountering an execution error, the database engine automatically initiates a rebuilding of the query plan and executes the rebuilt execution plan. If an error is encountered in the rebuilt query plan then the query implementation methods are analyzed. If a query function is identified for which an alternative implementation method is available, then this alternative implementation method is substituted to create a new query plan. The new query plan is then executed to determine if the error is corrected.

Abstract: An enhanced mechanism for intelligently managing an open query cursor built from a query access plan (AP) by a query optimizer of a database management system (DBMS). In an embodiment, a cursor management system (CMS) detects whether an open query cursor was previously requested for use within another job. If the open query cursor was previously requested, the CMS intelligently manages the query cursor for subsequent use within a different job. For example, the open query cursor may be recycled for subsequent use within the different job. Hence, system resources are not wasted in building and destroying the same cursors for multiple jobs. Alternatively, the query cursor may be pre-built before the cursor is requested for subsequent use within the different job. The CMS may also obtain, release and re-obtain locks for cursors being managed. Alternatively, the job that uses the cursor may obtain the lock.

Abstract: Embodiments of the invention provide techniques for generating statistics for optimizing database queries containing user-defined functions (UDFs). In general, the statistics may be generated based on output values produced during past executions of a UDF. The statistics may also be generated based on input values received during past executions of the UDF. Additionally, the statistics may include input and output value pairs, such that a UDF output may be determined based on a UDF input. The generated statistics may be used by a query optimizer to determine an efficient query plan for executing the database query.