Abstract: The disclosed embodiments illustrate methods and systems for generating recommendations for client process execution of one or more client processes corresponding to a plurality of clients of an organization. The method comprises retrieving an event log including event data captured during execution of one or more processes in the organization to service a plurality of clients of a predefined type. The event log is analyzed across the plurality of clients to determine cross-clientele information including a process compliance deviation between an observed and an expected client process execution of the one or more processes. Thereafter, a set of root-causes of the process compliance deviation is determined based on process models of the one or more processes and/or decision rules of the organization. Further, one or more recommendations for the client process execution of the one or more processes of the organization are generated, based on the set of root-causes.

Abstract: A method and a system are provided to manage analytics assets associated with at least one computer accessible to an organization using operating system (OS) containers. The method comprising receiving a request to process of a task on the user computing device. The method further select a set of analytics assets from a first category, based on at least one of a role of the user in the organization and a historical usage of the one or more analytics assets or a second category that corresponds to the user and are included in the container image associated with the user. The method further update the container image of the user, based on at least the selection of the set of analytics assets from the first category. The method further render the selected set of analytics assets through the updated container image on the user computing device of the user.

Abstract: The disclosed embodiments illustrate methods and systems for generating recommendations for client process execution of one or more client processes corresponding to a plurality of clients of an organization. The method comprises retrieving an event log including event data captured during execution of one or more processes in the organization to service a plurality of clients of a predefined type. The event log is analyzed across the plurality of clients to determine cross-clientele information including a process compliance deviation between an observed and an expected client process execution of the one or more processes. Thereafter, a set of root-causes of the process compliance deviation is determined based on process models of the one or more processes and/or decision rules of the organization. Further, one or more recommendations for the client process execution of the one or more processes of the organization are generated, based on the set of root-causes.

Abstract: A method and a system are provided to manage analytics assets associated with at least one computer accessible to an organization using operating system (OS) containers. The method comprising receiving a request to process of a task on the user computing device. The method further select a set of analytics assets from a first category, based on at least one of a role of the user in the organization and a historical usage of the one or more analytics assets or a second category that corresponds to the user and are included in the container image associated with the user. The method further update the container image of the user, based on at least the selection of the set of analytics assets from the first category. The method further render the selected set of analytics assets through the updated container image on the user computing device of the user.

Abstract: The use of heap memory is optimized by extending a cache implementation with a CacheInterface base class. An instance of a ReferenceToCache is attached to the CacheInterface base class. The cache implementation is registered to a garbage collector application. The registration is stored as a reference list in a memory. In response to an unsatisfied cache allocation request, a garbage collection cycle is triggered to check heap occupancy. In response to exceeding a threshold value, the reference list is traversed for caches to be cleaned based upon a defined space constraint value. The caches are cleaned in accordance with the defined space constraint value.

Abstract: Class loader changes from at least one user-defined class loader to a bootstrap class loader are determined by a stack processing module during de-serialization of a serialized data object. A reference to each user-defined class loader is stored separately from a thread method frame stack in response to each determined change from the at least one user-defined class loader to the bootstrap class loader. For each user-defined class load operation of a user-defined class associated with the de-serialization of the serialized data object, a latest user-defined class loader (LUDCL) is identified using the stored reference and the user-defined class is loaded using the identified LUDCL.

Abstract: The use of heap memory is optimized by extending a cache implementation with a CacheInterface base class. An instance of a ReferenceToCache is attached to the CacheInterface base class. The cache implementation is registered to a garbage collector application. The registration is stored as a reference list in a memory. In response to an unsatisfied cache allocation request, a garbage collection cycle is triggered to check heap occupancy. In response to exceeding a threshold value, the reference list is traversed for caches to be cleaned based upon a defined space constraint value. The caches are cleaned in accordance with the defined space constraint value.

Abstract: There is provided a computer implemented method for determining the efficiency of a runtime compiler. A set of execution times representing the time taken for program code to perform a set task after two or more runtime compilations is recorded. A first metric as the difference between the first execution time and the last execution time of the set of execution times, a second metric as the average throughput improvement from the set of execution times, and a third metric as the time taken for the compiler to achieve the maximum throughput from the set of execution times is calculated. Finally, an efficiency metric is calculated using the first, second and third metrics to determine the efficiency of the compiler.

Abstract: Class loader changes from at least one user-defined class loader to a bootstrap class loader are determined by a stack processing module during de-serialization of a serialized data object. A reference to each user-defined class loader is stored separately from a thread method frame stack in response to each determined change from the at least one user-defined class loader to the bootstrap class loader. For each user-defined class load operation of a user-defined class associated with the de-serialization of the serialized data object, a latest user-defined class loader (LUDCL) is identified using the stored reference and the user-defined class is loaded using the identified LUDCL.

Abstract: There is provided a computer implemented method for determining the efficiency of a runtime compiler. A set of execution times representing the time taken for program code to perform a set task after two or more runtime compilations is recorded. A first metric as the difference between the first execution time and the last execution time of the set of execution times, a second metric as the average throughput improvement from the set of execution times, and a third metric as the time taken for the compiler to achieve the maximum throughput from the set of execution times is calculated. Finally, an efficiency metric is calculated using the first, second and third metrics to determine the efficiency of the compiler.

Abstract: One aspect of the present invention involves storing the operating time offset of a thread within an area of memory of the thread, and storing a load time and a relative load time for each frame of the thread stored within an area of memory of the frame. The operating time offset tracks the amount of time the thread has actively operated, the frame load time tracks the actual time the frame was loaded, and the relative frame load time tracks the time the frame was loaded after being adjusted for the inactivity of the thread. When a thread dump is performed at a specified time, a frame age and a relative frame age can be calculated for each frame based on the time of the thread dump, the operating time offset of the thread, the load time of the frames within the thread, and the relative load time of the frames within the thread.