Abstract: An example method for multi-tenant adaptive monitoring comprises detecting occurrence of a trigger event and modifying a selection of metrics included in a plurality of monitored metrics that are monitored using available resources of a plurality of tenants. The method further comprises assigning a respective monitoring frequency for each metric; computing respective weights for each metric in the modified selection of metrics; performing a feasibility check to find a solution to a mathematical model for monitoring the modified selection of metrics at the respective assigned monitoring frequency for each metric; and, in response to determining that a solution to the mathematical model cannot be found, adjusting the respective monitoring frequency for one or more metrics. The method further comprises, in response to finding a first solution to the mathematical model, allocating processing associated with monitoring each metric among the available resources of the plurality of tenants.

Abstract: An example method for multi-tenant adaptive monitoring comprises detecting occurrence of a trigger event and modifying a selection of metrics included in a plurality of monitored metrics that are monitored using available resources of a plurality of tenants. The method further comprises assigning a respective monitoring frequency for each metric; computing respective weights for each metric in the modified selection of metrics; performing a feasibility check to find a solution to a mathematical model for monitoring the modified selection of metrics at the respective assigned monitoring frequency for each metric; and, in response to determining that a solution to the mathematical model cannot be found, adjusting the respective monitoring frequency for one or more metrics. The method further comprises, in response to finding a first solution to the mathematical model, allocating processing associated with monitoring each metric among the available resources of the plurality of tenants.

Abstract: Systems, methods, and computer program products for providing personalized recommendations of devices for monitoring and/or managing a health condition are disclosed, and generally include receiving first structured information regarding a patient and a first set of one or more patient populations; receiving unstructured information regarding at least the patient and a second set of one or more patient populations; analyzing the unstructured information to derive second structured information; determining one or more health metrics to be monitored for the patient based on analyzing each of the first structured information and the second structured information, using a classification model; and determining an optimum set of devices to be used for monitoring the one or more health metrics. In some embodiments, metrics may be continuously monitored to detect a change exceeding an event trigger threshold, and a new set of recommended devices may be generated.

Abstract: A monitoring system for metric data may include devices, each device capable of generating respective metric data. The monitoring system may also include a processor and a memory cooperating therewith. The processor may be configured to monitor the devices via a network to obtain the respective metric data, generate predicted trigger events based on monitoring the devices, and generate a respective adapted monitoring for the devices based upon each predicted trigger event. The processor may also be configured to, upon occurrence of one of the predicted trigger events, implement the respective adapted monitoring to obtain new respective metric data.

Abstract: A system for cognitive prioritization for report generation may include a processor and a memory cooperating therewith. The processor may be configured to accept a request for a new report from a user, the request having a user profile importance associated therewith and generate a predicted completion time for the new report based upon a historical completion time prediction model based upon historical data for prior reports. The processor may be configured to generate a predicted importance of the new report based upon a historical importance prediction model based upon the historical data for prior reports and determine a combined predicted importance based upon the user profile importance and the predicted importance. The processor may also be configured to generate a prioritization of the new report among other reports based upon the predicted completion time and the combined predicted importance and generate the new report based upon the prioritization.

Abstract: A meeting scheduling system may include a processor and a memory cooperating therewith. The processor may be configured to obtain meeting attributes for a meeting to be scheduled including attributes of desired meeting participants, attributes about meetings already scheduled for the desired meeting participants, attributes of meetings already conducted, and attributes of communications by the desired meeting participants. The processor may also be configured to classify the meeting to be scheduled by determining respective probabilities of whether the meeting will be held, cancelled, or rescheduled based upon the meeting attributes and present, to a meeting scheduler, potential time slots for the meeting to be scheduled based upon the classifying. The processor may also determine, based upon selection by the meeting scheduler, a selected time slot from among the potential time slots and present, to each desired meeting participant, the selected time slot.

Abstract: One embodiment provides for predicting and planning of staffing needs for services including obtaining data from an opportunity pipeline. The data including current and historical project information, offerings information included in each opportunity and current and historical staffing information. An optimization model is generated to provide a threshold for deals predicted to be won. A threshold of win score for deals to be considered as predicted to be won is optimized. Opportunities to be won are predicted including: executing a win prediction model for current opportunities in the opportunity pipeline, filtering deals with scores less than the win score threshold, processing a deal progress monitoring model for each remaining deal to predict a future event and related timeline, and simulating progress of each deal by updating each deal with a predicted event until an end of a simulation time window.

Abstract: One embodiment provides a method for estimating unit price reduction of services in a new in-flight deal using data of historical deals and market reference deals cost structures. The method includes receiving a detailed cost structure for historical information, market deals information, services quantity information and deals metadata for a first year. For each service: peer deals to the in-flight deal are selected based on the detailed cost structure; missing cost data values in the peer deals are augmented; unit cost reduction values for the peer deals estimated; the unit cost reduction for the in-flight deal from each year in total contract years to a next year without a last contract year are estimated; and a total cost for the in-flight deal for all years in the total contract years beyond the first year are estimated.

Abstract: A method for selecting a set of information technology (IT) services peer deals to an in-flight deal for each first level service in the in-flight deal includes receiving a detailed cost structure for historical information, in-flight deals information, market deals information, services baselines and deals metadata, and multiple peer deals for selection. For historical information and market deals information, all missing baselines for all services in the in-flight deal and all missing unit cost for services are augmented at the first level service. The multiple peer deals are classified into different clusters at the first level service. A closest cluster to the in-flight deal at the first level service is selected. For each second level service of the in-flight deal the method: classifies the selected peer deals into different clusters. A predetermined number of peer deals that appear in a largest number of the selected clusters is selected.

Abstract: Adaptive monitoring dynamically optimizes the monitoring frequency of metrics with respect to system constraints. One or more metrics are monitored. The monitoring includes receiving a value for the metric and evaluating the received metric value. If the evaluation is determined to affect one or monitoring parameters, or if an environment-based event occurs the metrics are adapted. Adapting metrics includes removing or adding a metric based on each metric's correlation to the affected monitoring parameter or environment based trigger. The frequencies of the metrics are optimized based on the available resources.

Abstract: A computing device retrieves historical data regarding one or more historical time periods during which cloud application instances were provisioned. A probability distribution of a number of queries received by the historical cloud application instances during each of the one or more historical time periods is determined by the computing device. A probability distribution of a number of received queries completed by each of the one or more historical cloud application instances during each of the one or more historical time periods is determined by the computing device. A new provisioning plan for further time periods is generated via application of a stochastic optimization model based upon the probability distribution of the number of received queries during each of the one or more historical time periods and the probability distribution of the number of received queries completed by each of the one or more cloud application instances.

Abstract: One embodiment provides a method for assessing probability of winning an in-flight deal. The method comprises receiving information for the in-flight deal. The information for the in-flight deal comprises a set of price points for the in-flight deal and metadata relating to the in-flight deal. The method further comprises, for each price point of the set of price points, predicting a probability of winning the in-flight deal at the price point based on a predictive analytics model.

Abstract: One embodiment provides a method for assessing probability of winning an in-flight deal. The method comprises receiving information for the in-flight deal. The information for the in-flight deal comprises a set of price points for the in-flight deal and metadata relating to the in-flight deal. The method further comprises, for each price point of the set of price points, predicting a probability of winning the in-flight deal at the price point based on a predictive analytics model.

Abstract: Adaptive monitoring dynamically optimizes the monitoring frequency of metrics with respect to system constraints. One or more metrics are monitored. The monitoring includes receiving a value for the metric and evaluating the received metric value. If the evaluation is determined to affect one or monitoring parameters, or if an environment-based event occurs the metrics are adapted. Adapting metrics includes removing or adding a metric based on each metric's correlation to the affected monitoring parameter or environment based trigger. The frequencies of the metrics are optimized based on the available resources.

Abstract: An example method for multi-tenant adaptive monitoring comprises detecting occurrence of a trigger event and modifying a selection of metrics included in a plurality of monitored metrics that are monitored using available resources of a plurality of tenants. The method further comprises assigning a respective monitoring frequency for each metric; computing respective weights for each metric in the modified selection of metrics; performing a feasibility check to find a solution to a mathematical model for monitoring the modified selection of metrics at the respective assigned monitoring frequency for each metric; and, in response to determining that a solution to the mathematical model cannot be found, adjusting the respective monitoring frequency for one or more metrics. The method further comprises, in response to finding a first solution to the mathematical model, allocating processing associated with monitoring each metric among the available resources of the plurality of tenants.

Abstract: One embodiment provides optimizing predictive precision for actionable forecasts of revenue change including receiving revenue data with timestamps for a number of historical periods at a particular level, with attributes of the particular level and a percentage of the required revenue change. The data is filtered. The filtered data is aggregated at the particular level for a selected prediction. A sliding window of the number of historical periods is moved over business periods, creating a data point for each historical period temporal window by extracting features. A required target output is created for each data point for at least one future time period. A model is trained to optimize predictive precision for actionable forecasts of revenue change. A set of recent histories is converted into a quantitative health value.

Abstract: One embodiment provides optimizing potential revenue savings when predicting client revenue change including receiving revenue data with timestamps for a number of historical periods at a particular level, with attributes of the particular level and a percentage of the required revenue change. The data is filtered. The filtered data is aggregated at the particular level for a selected prediction. A sliding window of the number of historical periods is moved over business periods, creating a data point for each historical period temporal window by extracting features. A required target output is created for each data point for at least one future time period. A weight is assigned to each data point proportional to value of revenue. A model is trained to optimize a weighted linear combination of losses over each data point. A set of recent histories is converted into a quantitative health value.

Abstract: One embodiment provides a method for predicting revenue change in a ledger including receiving, by a processor device, revenue data with timestamps for a number of historical periods at a particular level, with attributes of the particular level and a percentage of the required revenue change. The data is filtered. The filtered data is aggregated at the particular level for a selected prediction. A sliding window of the number of historical periods is moved over business periods, creating a data point for each historical period temporal window by extracting features. A required target output is created for each data point for at least one future time period. A statistical classification model is trained to predict the revenue change. A set of recent histories is converted into a quantitative health value.

Abstract: A computer-implemented method according to one embodiment includes identifying a set of virtual machines to be placed within a system, receiving characteristics associated with the set of virtual machines, determining characteristics associated with a current state of the system, determining a placement of the set of virtual machines within the system, based on the characteristics associated with the set of virtual machines and the characteristics associated with a current state of the system, determining an updated placement of all virtual machines currently placed within the system, based on the characteristics associated with the set of virtual machines and the characteristics associated with a current state of the system, and determining a migration sequence within the system in order to implement the updated placement of all virtual machines currently placed within the system.

Abstract: Adaptive monitoring dynamically optimizes the monitoring frequency of metrics with respect to system constraints. One or more metrics are monitored. The monitoring includes receiving a value for the metric and evaluating the received metric value. If the evaluation is determined to affect one or monitoring parameters, or if an environment-based event occurs the metrics are adapted. Adapting metrics includes removing or adding a metric based on each metric's correlation to the affected monitoring parameter or environment based trigger. The frequencies of the metrics are optimized based on the available resources.