Abstract: Disclosed are a system, method and apparatus for classification of data in a machine learning system. In one aspect, a method for classification of data in a machine learning system through one or more computer processors is disclosed. Further, generating, through one or more computer processors, a data classifier using a first dataset and determining an accuracy value of the data classifier to achieve a predefined model accuracy threshold. Still further, iterating, through one or more computer processors, calibration of the first dataset based on a set of parameters until the accuracy value matches or exceeds the predefined model accuracy threshold value. Further, the calibration comprises a user input to indicate a correctness of a presented subset of data from a second dataset and using the above to generate an enhanced data classifier for the classification of data.

Abstract: The present disclosure discloses a method and an object determination system for determining one or more target objects in an image. The image is segmented by the object detection system into one or more segments based on visual attributes in a first set. Morphological operations are performed on the one or more segments to obtain one or more morphed segments. One or more candidates of target objects are identified based on visual attributes in a second set corresponding to each one or more morphed segments. The object determination system identifies at least one of true positive and false positive from the one or more candidates which indicates presence or absence of the one or more target objects respectively, based on neighborhood information associated with the one or more candidates. The present disclosure facilitates in determining target objects in document automatically, thereby eliminating manual intervention in identifying target objects in the document.

Abstract: Disclosed are a system, method and apparatus for classification of data in a machine learning system. In one aspect, a method for classification of data in a machine learning system through one or more computer processors is disclosed. Further, generating, through one or more computer processors, a data classifier using a first dataset and determining an accuracy value of the data classifier to achieve a predefined model accuracy threshold. Still further, iterating, through one or more computer processors, calibration of the first dataset based on a set of parameters until the accuracy value matches or exceeds the predefined model accuracy threshold value. Further, the calibration comprises a user input to indicate a correctness of a presented subset of data from a second dataset and using the above to generate an enhanced data classifier for the classification of data.

Abstract: The present disclosure discloses a method and an object determination system for determining one or more target objects in an image. The image is segmented by the object detection system into one or more segments based on visual attributes in a first set. Morphological operations are performed on the one or more segments to obtain one or more morphed segments. One or more candidates of target objects are identified based on visual attributes in a second set corresponding to each one or more morphed segments. The object determination system identifies at least one of true positive and false positive from the one or more candidates which indicates presence or absence of the one or more target objects respectively, based on neighborhood information associated with the one or more candidates. The present disclosure facilitates in determining target objects in document automatically, thereby eliminating manual intervention in identifying target objects in the document.

Abstract: A system and method for evaluating performance of a software application. The present invention includes analyzing by one or more computing devices a plurality of program code lines of the software application stored in one or more computer databases. Further, one or more equivalent program regions within the plurality of program code lines may be identified. One or more markers in the identified one or more equivalent program regions may be inserted and stored in the one or more computer databases. Further, the plurality of program code lines may be compiled and assembled respectively to generate an executable code. The executable code may include a plurality of instructions. Further, performance metrics of the software application may be measured by manipulating the plurality of instructions based on the one or more equivalent program regions identified by the inserted one or more markers and executing the executable code.

Abstract: A method and system for monitoring health of virtual environment includes receiving input data for one or more virtual machines in the virtual environment. A number of entitled CPU cycles for each of the virtual machines based on the input data are determined. A number of actual CPU cycles obtained by each of the virtual machines based on the input data are computed. The number of entitled CPU cycles is compared with the number of actual CPU cycles for each of the virtual machines. A health status of the virtual machines based on the comparison is displayed.

Abstract: This technique determines the possible Service Level Agreement (SLA) violation during migrating a workload from a physical environment to a virtualized environment and also migrating a virtual machine (VM) from one physical machine to another within a virtualized environment. This technique uses capacity factor, i.e. the ratio of host physical machine (host PM) capacity and target physical machine (target PM) capacity, to determine the service rate change in the target PM compared to host PM and eventually calculates the VM utilization and response time in the target PM. It then checks if the VM utilization and response time in the target PM lies within the SLA limit. It further checks if the migration in the target virtualized PM is violating a predefined target PM utilization threshold or not and finally causes the migration in the target physical machine if all the SLA parameters are satisfied.

Abstract: This technology relates to a device and method for determining co-locatability of a plurality of virtual machines on one or more physical infrastructures. The plurality of virtual machines hosts a plurality of workloads. This involves identifying workloads which have high variability from the time series data and determining the workload capacity threshold of the identified workloads. Thereafter, the candidate workloads are selected among the identified workloads to colocate on a virtual machine based on the workload variability. After that, the total capacity required by each candidate workload pair to meet the service requirement is determined based on the workload capacity threshold. Then, an optimal sharing point of each workload of the pair with respect to the other workload of the pair is identified. Further, percentage compatibility of each workload pair is determined and finally, the candidate workloads are colocated based on the optimal sharing point and percentage compatibility.

Abstract: A method system and computer program product are disclosed for identifying silent failures in an application, comprising of generating a finite state machine (FSM) model of the application based on an input data, extracting state specific invariants relevant to the generated FSM, performing a localized invariant violation check at each state of the generated FSM and upon detection of an invariant violation at any state of the FSM, logging the violation as a silent failure.

Abstract: A method and system for monitoring health of virtual environment includes receiving input data for one or more virtual machines in the virtual environment. A number of entitled CPU cycles for each of the virtual machines based on the input data are determined. A number of actual CPU cycles obtained by each of the virtual machines based on the input data are computed. The number of entitled CPU cycles is compared with the number of actual CPU cycles for each of the virtual machines. A health status of the virtual machines based on the comparison is displayed.

Abstract: This technique determines the possible Service Level Agreement (SLA) violation during migrating a workload from a physical environment to a virtualized environment and also migrating a virtual machine (VM) from one physical machine to another within a virtualized environment. This technique uses capacity factor, i.e. the ratio of host physical machine (host PM) capacity and target physical machine (target PM) capacity, to determine the service rate change in the target PM compared to host PM and eventually calculates the VM utilization and response time in the target PM. It then checks if the VM utilization and response time in the target PM lies within the SLA limit. It further checks if the migration in the target virtualized PM is violating a predefined target PM utilization threshold or not and finally causes the migration in the target physical machine if all the SLA parameters are satisfied.

Abstract: A system and method for evaluating performance of a software application. The present invention includes analyzing by one or more computing devices a plurality of program code lines of the software application stored in one or more computer databases. Further, one or more equivalent program regions within the plurality of program code lines may be identified. One or more markers in the identified one or more equivalent program regions may be inserted and stored in the one or more computer databases. Further, the plurality of program code lines may be compiled and assembled respectively to generate an executable code. The executable code may include a plurality of instructions. Further, performance metrics of the software application may be measured by manipulating the plurality of instructions based on the one or more equivalent program regions identified by the inserted one or more markers and executing the executable code.

Abstract: A method system and computer program product are disclosed for identifying silent failures in an application, comprising of generating a finite state machine (FSM) model of the application based on an input data, extracting state specific invariants relevant to the generated FSM, performing a localized invariant violation check at each state of the generated FSM and upon detection of an invariant violation at any state of the FSM, logging the violation as a silent failure.

Abstract: This technology relates to a device and method for determining co-locatability of a plurality of virtual machines on one or more physical infrastructures. The plurality of virtual machines hosts a plurality of workloads. This involves identifying workloads which have high variability from the time series data and determining the workload capacity threshold of the identified workloads. Thereafter, the candidate workloads are selected among the identified workloads to colocate on a virtual machine based on the workload variability. After that, the total capacity required by each candidate workload pair to meet the service requirement is determined based on the workload capacity threshold. Then, an optimal sharing point of each workload of the pair with respect to the other workload of the pair is identified. Further, percentage compatibility of each workload pair is determined and finally, the candidate workloads are colocated based on the optimal sharing point and percentage compatibility.

Abstract: The present technique is an apparatus and method evaluating software performance. The method identifies performance scenarios using a knowledge base and selects the performance scenarios from a context module using operational laws. The system analyzes performance criticality of an application workload. Furthermore, the system comprises a context module that ranks the performance criterions depending on the input and the knowledge base stores and retrieves the performance criterions using the operational laws. The knowledge base comprises the criterions and their relevant ranks based of the application context. The present technique automates classification of performance criterions into benefit and cost categories with the usage of the operational laws.

Abstract: A system adapted for analyzing performance and scalability of at least one computer system is disclosed. The system include a monitoring module configured for capturing at least one metric of an application for analyzing performance and scalability and a performance knowledge repository created and represented by using the at least one metric captured by the monitoring module. The system further includes a diagnosis module adapted for mapping the at least one metric to the performance knowledge repository using a diagnosis module and an analysis module configured for conducting performance and scalability analysis based on the mapping of the at least one metric for detecting at least one undesirable condition.

Abstract: A system adapted for analyzing performance and scalability of at least one computer system is disclosed. The system include a monitoring module configured for capturing at least one metric of an application for analyzing performance and scalability and a performance knowledge repository created and represented by using the at least one metric captured by the monitoring module. The system further includes a diagnosis module adapted for mapping the at least one metric to the performance knowledge repository using a diagnosis module and an analysis module configured for conducting performance and scalability analysis based on the mapping of the at least one metric for detecting at least one undesirable condition.

Abstract: The present technique is an apparatus and method evaluating software performance. The method identifies performance scenarios using a knowledge base and selects the performance scenarios from a context module using operational laws. The system analyzes performance criticality of an application workload. Furthermore, the system comprises a context module that ranks the performance criterions depending on the input and the knowledge base stores and retrieves the performance criterions using the operational laws. The knowledge base comprises the criterions and their relevant ranks based on the application context. The present technique automates classification of performance criterions into benefit and cost categories with the usage of the operational laws.

Abstract: A method of developing early design components of a software application includes identifying business events. An object model or entity relationship diagram is drawn to identify the entities. A matrix is created to evaluate how the business events interact with the entities. Clustering is applied to identify a selected set of entity components (ECs) from the matrix. Process components (PCs) are then created from business events that are not methods of ECs.