Abstract: Enterprise organizations may use observational data to gather information about user experiences with their products and tools. For instance, patients with kidney failure may undergo dialysis treatment in order to remove toxins and excess fluids from their blood. The dialysis treatment may be performed at a hospital or clinic, or in a user's home, and the enterprise organization may use gathered information to gain understanding of user experiences with their dialysis machines and services. A user experience computing system gathers and processes user experience information from across the enterprise organization. Using stored observation data (e.g., surveys, studies etc.) in its smallest common form, the computing system may use this data as building blocks for creating more complex data objects (e.g. journey matrices and/or empathy gardens) using inputs from multiple different sources, and to facilitate presenting this information in an effective and empathetic way to product developers.

Abstract: A block storage service analyzes customer behaviors as external signals to the health of the block storage service. In one example, using rules set by a customer for a health monitoring service, the block storage service can gain insights into what the customer cares about. The customer rules can then be used to setup internal monitors within the block storage service. If the internal monitors are triggered, the block storage service can proactively correct problems before the customer alarm thresholds are reached. For example, customer volumes can be moved to increase performance characteristics. In some cases, if the customer has too much performance capability, the customer volume can be moved to a less costly alternative.

Abstract: Aspects of the present disclosure generally relates to a method including receiving user data and identifying at least a domain target for the at least a domain as a function of the domain-specific data. Also, the method may include generating a plurality of candidate schedules. Further, the method may include selecting an optimal user schedule from the plurality of candidate schedules. Moreover, the method may include presenting, at a remote device, the optimal user schedule to a user, and tracking, by the computing device, a user's progress with regard to the optimal user schedule.

Abstract: Arrangements for synchronously tracking and controlling events across multiple computer systems are provided. In some examples, a user may register with a system and user data may be received. In some arrangements, historical data associated with the user may also be received. Machine learning may be used to analyze the historical data and/or user data and a first recommendation for an item may be generated and transmitted to the user. Upon receiving acceptance of the recommendation, the system may request data from one or more entities. For instance, entity data associated with current inventory, availability of items, layout of locations, and the like, may be received. Based on the received data, a list of items for capture and/or an item capture route may be generated. In some examples, the item capture route may include step-by-step or map-based instructions to capture the items on the list.

Abstract: Systems and methods for calculating and displaying implicit popularity of products are disclosed. In embodiments, a computer-implemented method, comprises: determining, by a computing device, a set of consumer actions related to a product based on recorded consumer interaction data, wherein the set of consumer actions comprise a plurality of different physical actions of multiple consumers over a span of time; aggregating, by the computing device, the set of consumer actions by type of physical action over time to produce aggregated action event data; calculating, by the computing device, an implicit popularity number for the product based on the aggregated action event data; and sending, by the computing device, a display category to a product display device associated with the product based on the implicit popularity number, wherein the display category represents a measure of consumer popularity of the product.

Abstract: A crew planning system includes a demand forecasting module and an optimization module. The system forecasts anticipated reserve demand levels, and determines suitable reserve staffing approaches to meet anticipated reserve demand. The forecasting is based upon probabilistic distribution models which take into account variability associated with reserve demand for a particular day. Via use of the crew planning system, reserve staffing expenses may be reduced and/or reserve demand may be met with a higher degree of probability.

Abstract: A method of determining a microbiological risk level in food, comprising determining zero-inflated binomial (ZIB) distribution parameters (?, p); i) determining cumulative relative frequencies (fo, fi, f2, . .. , fx) for a number of occurrences (0, 1, 2, x) of defective samples; ii) calculating a vector of a sub-set of zero-inflation parameters (?) of k+1 elements according to; o/o=[0, 1* fo/k, 2*fo/ k, k*fo/k]; iii) calculating a vector of a sub-set of first parameters (?) based on the sub-set of zero-inflation parameter (?); iv) for the vector pairs (p, ?) in the sub-set of first parameters and the sub¬set of zero-inflation parameters, determining a square error between said cumulative relative frequencies and cumulative theoretical probabilities Px of having ?x occurrences over N samples for a ZIB distribution; v) determining the zero-inflation parameter lt) and the first parameter (p) as the vector pair providing the least square error.

Abstract: Provided is a process for selecting candidates for participating in a market-research survey, or receiving other offers, based on location history while protecting privacy of the candidates.

Abstract: In one embodiment, a method includes identifying a plurality of period combinations for a metric of interest. Each period combination comprises one or more time periods and each of the one or more time periods comprises one or more segments. The method further includes, for each period combination of the plurality of period combinations and each historical value of a plurality of historical values of the metric of interest, incrementally inserting the historical value into corresponding segments of the one or more time periods. Moreover, the method includes ranking the plurality of period combinations based on comparisons of the plurality of historical values to sets of corresponding predicted values. The method also includes selecting a highest-ranked period combination as best characterizing the metric of interest.

Abstract: A method and system for automatically reversing a process is provided. The method includes defining a forward process comprising a set of forward activities for automatically performing an automated task. First undo activities and associated data structures, associated with undoing a first forward activity of the set of forward activities, first interdependencies between the first undo activities are defined. Additionally, first triggers comprising a first link between the first forward activity and the first undo activities are defined. Second undo activities and associated data structures, associated with undoing a second forward activity as well as second interdependencies between the second undo activities are defined. Additional second triggers comprising a second link between the second forward activity and the second undo activities are defined.

Abstract: A crew planning system includes a demand forecasting module and an optimization module. The system forecasts anticipated reserve demand levels, and determines suitable reserve staffing approaches to meet anticipated reserve demand. The forecasting is based upon probabilistic distribution models which take into account variability associated with reserve demand for a particular day. Via use of the crew planning system, reserve staffing expenses may be reduced and/or reserve demand may be met with a higher degree of probability.

Abstract: In one embodiment, a method includes executing at least one application. The method further includes collecting, at periodic intervals as the least one application executes, an application load of each of the at least one application. In addition, the method includes generating, based at least in part on the collecting, an application-behavior baseline for each of the at least one application. The application-behavior baseline includes a plurality of projected application loads over a future period of time. The application-behavior baseline has a configurable time resolution. The method also includes automatically determining, for a subperiod of the future period, a quantity of compute resources required to manage the at least one application in satisfaction of at least one performance criterion. Moreover, the method includes causing the determined quantity of compute resources to be proactively provisioned to the at least one application in advance of a start of the subperiod.

Abstract: A crew planning system includes a demand forecasting module and an optimization module. The system forecasts anticipated reserve demand levels, and determines suitable reserve staffing approaches to meet anticipated reserve demand. The forecasting is based upon probabilistic distribution models which take into account variability associated with reserve demand for a particular day. Via use of the crew planning system, reserve staffing expenses may be reduced and/or reserve demand may be met with a higher degree of probability.

Abstract: Systems and methods for creating and using a workflow template for analyzing data entities stored in the one or more databases. After a desired workflow is identified, one or more series, data source, and/or entity restrictions based upon the workflow may be received. In addition, one or more chart configuration settings based upon the workflow may also be received. The restrictions and configuration settings may be saved as the workflow template. The workflow may then be performed by loading the saved workflow template, in order to automatically generate charts in accordance with the configuration settings of the template, based upon a received selection of series, data sources, and entities that adheres to the restrictions defined in the template.