Abstract: Embodiments of the present disclosure disclose methods and systems for generating operational alarms for a plurality of clients, the operational alarms being generated by a business performance system, each client being associated with a plurality of Key Performance Indicators (KPIs). An aggregate rank for each client based on ranks of each KPI is generated. For each client: the aggregate rank of the client over one or more time periods is monitored. If the aggregate rank deteriorates over the one or more time periods, a severity indicator is determined based on change of value of each KPI and change of rank of each KPI for the client over the one or more time periods. An operational alarm based on value of the severity indicator is determined. The operational alarm is displayed on a user interface, wherein the operational alarm displays one or more KPIs with highest severity values.

Abstract: A system and method for determining a configuration of a plurality of tasks to meet the specified deadline of a linear workflow of a real-time heterogeneous network. Often times, while meeting expected application performance in the heterogeneous network, it may possible to have graceful degradation of quality for ensuring timing constraints at the same time. In a multi-layered architecture, where each layer is equipped with multiple computational resources, the time optimization for each of the plurality of tasks can be achieved through approximate computing and analyzing all possible configurations of each task in a workflow within a particular layer.

Abstract: A method for data partitioning in an internet-of-things (IoT) network is described. The method includes determining number of computing nodes in the IoT network capable of contributing in processing of a data set. At least one capacity parameter associated with each computing node in the IoT network and each communication link between a computing node and a data analytics system can be ascertained. The capacity parameter can indicate a computational capacity for each computing node and communication capacity for each communication link. An availability status, indicating temporal availability, of each of computing nodes and each communication link is determined. The data set is partitioned into subsets, based on the number of computing nodes, the capacity parameter and the availability status, for parallel processing of the subsets.

Abstract: Embodiments of a system and a method for evaluating fairness of an incentive scheme are disclosed. The method includes generating desired ranks for a set of employees based on multiple Key Performance Indicator (KPI) vectors associated with the set, where the generated desired ranks are refined based on a most promising vector in the plurality of KPI vectors; computing a distance between a pair of ranks including a pre-set rank based on a predefined incentive scheme and a desired rank from the generated desired ranks for each employee; comparing the computed distance for each employee in the set with a predefined value; evaluating the pre-set rank to be fair and indicative of the predefined incentive scheme being fair to a corresponding employee if the computed distance is relatively less than the predefined value based on the comparison; and displaying a visualization of the computed distance.

Abstract: Embodiments of a system and a method for design and implementation of an employee incentive scheme for one or more tasks are disclosed. The system includes a data input module and a unified incentive module. The data input module on a computer with a processor and a memory being configured to: receive a task for a plurality of employees from the user device; and identify whether or not a task is associated with a predetermined baseline. The task is identified as a routine task if associated with the predetermined baseline, else the task is identified as a special task. The unified incentive module on the computer is configured to compute a task incentive for at least one of the routine task and the special task associated with an employee among multiple employees.

Abstract: A system and method for determining a configuration of a plurality of tasks to meet the specified deadline of a linear workflow of a real-time heterogeneous network. Often times, while meeting expected application performance in the heterogeneous network, it may possible to have graceful degradation of quality for ensuring timing constraints at the same time. In a multi-layered architecture, where each layer is equipped with multiple computational resources, the time optimization for each of the plurality of tasks can be achieved through approximate computing and analyzing all possible configurations of each task in a workflow within a particular layer.

Abstract: Embodiments of the present disclosure disclose methods and systems for generating operational alarms for a plurality of clients, the operational alarms being generated by a business performance system, each client being associated with a plurality of Key Performance Indicators (KPIs). An aggregate rank for each client based on ranks of each KPI is generated. For each client: the aggregate rank of the client over one or more time periods is monitored. If the aggregate rank deteriorates over the one or more time periods, a severity indicator is determined based on change of value of each KPI and change of rank of each KPI for the client over the one or more time periods. An operational alarm based on value of the severity indicator is determined. The operational alarm is displayed on a user interface, wherein the operational alarm displays one or more KPIs with highest severity values.

Abstract: A method comprises, receiving, at each of a plurality of computing devices, a task execution estimation request message from a central server, the task execution estimation request message comprising a worst-case execution time (WCET) corresponding to the computing device. The method further comprises, computing, by each of the plurality of computing devices, an estimate task execution time for the task based on the WCET and a state transition model corresponding to the computing device, wherein the state transition model indicates available processing resources corresponding to the computing device. Further, the method comprises transmitting, by each of the plurality of computing devices, the estimate task execution time to the central server for allocation of the task to a computing device from amongst the plurality of computing devices based on the estimate task execution time corresponding to the computing device.

Abstract: A method comprises, receiving, at each of a plurality of computing devices, a task execution estimation request message from a central server, the task execution estimation request message comprising a worst-case execution time (WCET) corresponding to the computing device. The method further comprises, computing, by each of the plurality of computing devices, an estimate task execution time for the task based on the WCET and a state transition model corresponding to the computing device, wherein the state transition model indicates available processing resources corresponding to the computing device. Further, the method comprises transmitting, by each of the plurality of computing devices, the estimate task execution time to the central server for allocation of the task to a computing device from amongst the plurality of computing devices based on the estimate task execution time corresponding to the computing device.

Abstract: A method for data partitioning in an internet-of-things (IoT) network is described. The method includes determining number of computing nodes in the IoT network capable of contributing in processing of a data set. At least one capacity parameter associated with each computing node in the IoT network and each communication link between a computing node and a data analytics system can be ascertained. The capacity parameter can indicate a computational capacity for each computing node and communication capacity for each communication link. An availability status, indicating temporal availability, of each of computing nodes and each communication link is determined. The data set is partitioned into subsets, based on the number of computing nodes, the capacity parameter and the availability status, for parallel processing of the subsets.

Abstract: In an OLED device having a substrate, a first electrode layer disposed over the substrate, an inorganic short reduction layer disposed over the first electrode layer, an organic electroluminescent medium disposed over the short reduction layer, and a second electrode layer over the electroluminescent medium, a feature is the inclusion of a mixture of ZnS, SiO2, and ITO in the short reduction layer wherein the ratio of In atoms to Zn atoms is in the range of from 0.90 to 2.37.

Abstract: In an OLED device having a substrate, a first electrode layer disposed over the substrate, an inorganic short reduction layer disposed over the first electrode layer, an organic electroluminescent medium disposed over the short reduction layer, and a second electrode layer over the electroluminescent medium, a feature is the inclusion of a mixture of ZnS, SiO2, and ITO in the short reduction layer wherein the ratio of In atoms to Zn atoms is in the range of from 0.90 to 2.37.