Abstract: A computer-implemented method, according to one embodiment, includes: setting a target power demand corresponding to a consumer, and performing a process. The process includes: determining an actual power demand presented to a utility by the consumer, and determining a current error. The current error is the difference between the actual power demand and the target power demand. A determination is also made as to whether the actual power demand is adjustable in a direction that reduces the current error. In response to determining that the actual power demand is adjustable in the direction that reduces the current error, the current error is reduced by adjusting the actual power demand. Moreover, in response to determining that the actual power demand is not adjustable in the direction that reduces the current error, the target power demand is modified.

Abstract: A computer-implemented method, according to one embodiment, includes: receiving an offer request including one or more desired services, and selecting available offerings, each of which include at least one of the desired services. A determination is made whether available benchmarks exist for each of the at least one desired service included in each of the selected available offerings. For each desired service determined as not having available benchmarks, a draft benchmark is computed for each of a plurality of criteria. A confidence weight is also computed for each of the draft benchmarks. The available benchmarks, the draft benchmarks, and the confidence weights are further used to construct an offer which is submitted in response to the received offer request. Moreover, the draft benchmarks and the corresponding confidence weights are re-computed for each of the respective desired services in response to determining that the submitted offer was not accepted.

Abstract: One embodiment provides a method comprising receiving a flight plan request for a drone. The flight plan request comprises a drone identity, departure information, and arrival information. The method further comprises constructing a modified flight plan for the drone based on the flight plan request, wherein the modified flight plan represents an approved, congestion reducing, and executable flight plan for the drone, and the modified flight plan comprises a sequence of four-dimensional (4D) cells representing a planned flight path for the drone. For each 4D cell of the modified flight plan, the method further comprises attempting to place an exclusive lock on behalf of the drone on the 4D cell, and in response to a failure to place the exclusive lock on behalf of the drone on the 4D cell, rerouting the modified flight plan around the 4D cell to a random neighboring 4D cell.

Abstract: One embodiment provides a method comprising receiving a flight plan request for a drone. The flight plan request comprises a drone identity, departure information, and arrival information. The method further comprises constructing a modified flight plan for the drone based on the flight plan request, wherein the modified flight plan represents an approved, congestion reducing, and executable flight plan for the drone, and the modified flight plan comprises a sequence of four-dimensional (4D) cells representing a planned flight path for the drone. For each 4D cell of the modified flight plan, the method further comprises attempting to place an exclusive lock on behalf of the drone on the 4D cell, and in response to a failure to place the exclusive lock on behalf of the drone on the 4D cell, rerouting the modified flight plan around the 4D cell to a random neighboring 4D cell.

Abstract: A computer-implemented method, according to one embodiment, includes: receiving an energy consumption profile which spans multiple intervals in a period of time, and predicting a net energy demand of a consumer system over the period of time. Moreover, a first multiple is determined which, when applied to the received energy consumption profile, produces an updated energy consumption profile which corresponds to an amount of energy that is capable of satisfying the predicted net energy demand of the consumer system. A greatest amount of underprediction is estimated. A greatest amount of overprediction is also estimated. Furthermore, an initial state of an energy storage device electrically coupled to the consumer system is computed according to the updated energy consumption profile. The initial state of the energy storage device is also based on a second multiple applied to each of the greatest amount of underprediction, and the greatest amount of overprediction.

Abstract: One embodiment provides a method that includes generating, by a processor, a topic graph by converting information to topic nodes and edges in the topic graph. Bridges from a given topic node are provided to a neighbor node based on a weight assignment. The bridges are converted to an electronic form of communication. The processor orders suggested paths based on a determined distance from at least one other topic node to a topic goal node via an earliest topic node on each suggested path. The determined distance is found by a first search of the topic graph.

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: One embodiment provides a method that includes obtaining information including profile information and current event information. A processor generates a topic graph by converting the information to topic nodes in the topic graph. The processor determines a weight assignment for each topic node based on ratios of sums of weights of edges from topic nodes. Bridges are provided from a given topic node to a neighbor based on the weight assignment.

Abstract: One embodiment provides a method comprising maintaining a multi-dimensional data structure partitioned into cells utilizing a tree data structure (“tree”) comprising intervals for each dimension of a multi-dimensional space. To partition an interval for a node of the tree into multiple subintervals, multiple leaf nodes (“leaves”) are generated, each leaf descending from the node. To merge multiple intervals for multiple nodes of the tree, a parent node (“parent”) and multiple leaves descending from the parent are generated, the parent and the leaves are time constrained, and the leaves are scheduled for a merger. When transient data in cells included in a list that corresponds to a leaf scheduled for merger expires, each cell in the list is converted into a cell for inclusion in a different list corresponding to a parent of the leaf, each leaf of the parent removed, and the parent turned into a leaf.

Abstract: One embodiment provides a method comprising maintaining a multi-dimensional data structure partitioned into cells utilizing a tree data structure (“tree”) comprising intervals for each dimension of a multi-dimensional space. To partition an interval for a node of the tree into multiple subintervals, multiple leaf nodes (“leaves”) are generated, each leaf descending from the node. To merge multiple intervals for multiple nodes of the tree, a parent node (“parent”) and multiple leaves descending from the parent are generated, the parent and the leaves are time constrained, and the leaves are scheduled for a merger. When transient data in cells included in a list that corresponds to a leaf scheduled for merger expires, each cell in the list is converted into a cell for inclusion in a different list corresponding to a parent of the leaf, each leaf of the parent removed, and the parent turned into a leaf.

Abstract: Embodiments of the present invention provide a system, method and computer program product for forecasting a time series based on actuals and a plan. A method comprises generating a first and a second sequence of segments that rise and fall alternately based on a first and a second sequence of data values over time, respectively. The first and second sequence of data values represent actual data for an ongoing project and estimated data for a planned project, respectively. The method further comprises aligning at least one segment of the second sequence of segments with at least one segment of the first sequence of segments, and adjusting the estimated data by replacing at least one segment of the second sequence of segments with a scaled version of at least one corresponding aligned segment of the first sequence of segments. The scaled version maintains an estimated total for the planned project.

Abstract: Embodiments of the present invention provide a system, method and computer program product for forecasting a time series based on actuals and a plan. A method comprises generating a first and a second sequence of segments that rise and fall alternately based on a first and a second sequence of data values over time, respectively. The first and second sequence of data values represent actual data for an ongoing project and estimated data for a planned project, respectively. The method further comprises aligning at least one segment of the second sequence of segments with at least one segment of the first sequence of segments, and adjusting the estimated data by replacing at least one segment of the second sequence of segments with a scaled version of at least one corresponding aligned segment of the first sequence of segments. The scaled version maintains an estimated total for the planned project.

Abstract: Embodiments of the present invention provide a method for detecting a temporal change of name associated with performance data. The method comprises receiving at least one candidate name replacement pair comprising a pair of names. The method further comprises, in a training stage, for each known name replacement pair included in the performance data, determining a window of time covering a most recent appearance of a first name of the known name replacement pair. The window of time is determined based on quantitative features of a time series model comprising performance data for the first name and a second name of the known name replacement pair. The method further comprises, in the training stage, training a machine learning classifier based on quantitative features computed using a portion of the performance data for the first name and the second name, where the portion is within the window of time determined.

Abstract: Embodiments of the present invention provide a method for detecting a temporal change of name associated with performance data. The method comprises receiving at least one candidate name replacement pair comprising a pair of names. The method further comprises, in a training stage, for each known name replacement pair included in the performance data, determining a window of time covering a most recent appearance of a first name of the known name replacement pair. The window of time is determined based on quantitative features of a time series model comprising performance data for the first name and a second name of the known name replacement pair. The method further comprises, in the training stage, training a machine learning classifier based on quantitative features computed using a portion of the performance data for the first name and the second name, where the portion is within the window of time determined.

Abstract: A computer-implemented method according to one embodiment includes identifying an amount of force applied to a pressure-sensitive touchscreen of a device, and providing the amount of force as an input to an application of the device.

Abstract: Embodiments of the present invention provide a system, method and computer program product for generating a succinct approximate representation of a time series. A method comprises determining at least one peak data value and at least one trough data value of an input series comprising a sequence of data values over time. The input series is partitioned into multiple segments comprising at least one rising segment that rises to a peak data value and at least one falling segment that falls to a trough data value. A sequence of segments that rise and fall alternately is generated based on the segments. A sequence of totals representing a succinct approximate representation of the input series is generated. Each total comprises a sum of data values for a corresponding segment of the sequence of segments.

Abstract: Embodiments of the present invention provide an apparatus comprising a body including a cavity for storing one or more packages, and a conveyor belt disposed above a top surface of the body. The belt is shaped to receive one or more packages, and the belt is controllable to rotate a package placed on the belt either from the top surface to the cavity for storage or from the cavity to the top surface for dispatch. A package comprises at least one of a drone and a payload transported by the drone. The apparatus further comprises a landing mechanism for stabilizing a drone landing on the apparatus.

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 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: 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.