Abstract: The present embodiments relate to data processing model recommendation and review of a portion of data using a recommended model. A model catalog executing on a cloud infrastructure (CI) system can parse data from an obtained dataset identifying aspects of the dataset. The parsed data from the dataset can be compared with a plurality of potential models stored in a domain ontology store of the model catalog to identify one or more recommended models. Review output data can be generated using the dataset and any of the recommended models. The review output data resulting from the recommended model can be provided to the client for the client to either accept or reject the model.

Abstract: The present embodiments relate to data processing model recommendation and simulation. A model catalog executing on a cloud infrastructure (CI) system can parse data from an obtained dataset identifying aspects of the dataset. The parsed data from the dataset can be compared with a plurality of potential models stored in a domain ontology store of the model catalog to identify one or more recommended models. A simulation of any of the recommended models can be executed using a portion of the dataset to provide insights into output data resulting from executing the recommended model. The output data resulting from the simulation of the recommended model can be provided to the client for the client to either accept or reject the model.

Abstract: Systems and methods for implementing federated learning engine for integration of vertical and horizontal AI are disclosed herein. A method can include receiving a global model from a central aggregator communicatingly connected with a plurality of user environments, which global model including a plurality of layers. The method can include training a mini model on top of the global model with data gathered within the user environment, uploading the at least a portion of the mini model to the central aggregator, receiving a plurality of mini models, and creating a fusion model based on the received plurality of mini models.

Abstract: The present embodiments relate to implementing change data on no-master NoSQL data stores. An optimized node can be identified from a plurality of NoSQL data storage nodes and a specialized node can be connected (e.g., collocated) to the optimized node. The specialized node can maintain change data capture (CDC) data provided by client nodes in a hash map that can be used as a point of truth for coordinating CDC data across the plurality of NoSQL data storage nodes. The plurality of NoSQL data storage nodes can identify and coordinate all read/write data obtained from multiple client devices in a geographically separated large-scale (e.g., planet scale) system to identify change data in a distributed data store. The specialized data can provide read data to devices in the large-scale system to reconcile inconsistencies in change data across nodes in the large-scale system.

Abstract: The present embodiments relate to data processing model recommendation and simulation. A model catalog executing on a cloud infrastructure (CI) system can parse data from an obtained dataset identifying aspects of the dataset. The parsed data from the dataset can be compared with a plurality of potential models stored in a domain ontology store of the model catalog to identify one or more recommended models. A simulation of any of the recommended models can be executed using a portion of the dataset to provide insights into output data resulting from executing the recommended model. The output data resulting from the simulation of the recommended model can be provided to the client for the client to either accept or reject the model.

Abstract: The present embodiments relate to implementing change data on no-master NoSQL data stores. An optimized node can be identified from a plurality of NoSQL data storage nodes and a specialized node can be connected (e.g., collocated) to the optimized node. The specialized node can maintain change data capture (CDC) data provided by client nodes in a hash map that can be used as a point of truth for coordinating CDC data across the plurality of NoSQL data storage nodes. The plurality of NoSQL data storage nodes can identify and coordinate all read/write data obtained from multiple client devices in a geographically separated large-scale (e.g., planet scale) system to identify change data in a distributed data store. The specialized data can provide read data to devices in the large-scale system to reconcile inconsistencies in change data across nodes in the large-scale system.

Abstract: The present embodiments relate to implementing change data on no-master NoSQL data stores. An optimized node can be identified from a plurality of NoSQL data storage nodes and a specialized node can be connected (e.g., collocated) to the optimized node. The specialized node can maintain change data capture (CDC) data provided by client nodes in a hash map that can be used as a point of truth for coordinating CDC data across the plurality of NoSQL data storage nodes. The plurality of NoSQL data storage nodes can identify and coordinate all read/write data obtained from multiple client devices in a geographically separated large-scale (e.g., planet scale) system to identify change data in a distributed data store. The specialized data can provide read data to devices in the large-scale system to reconcile inconsistencies in change data across nodes in the large-scale system.

Abstract: A method and system for automatically metering a distributed file system node is provided. The method includes receiving data associated with jobs for execution via a distributed file system. Characteristics of the jobs are uploaded and policy metrics data associated with hardware usage metering is retrieved. Resource requests associated with hardware resource usage are retrieved and attributes associated with the resource requests are uploaded. The policy metrics data is analyzed and a recommendation circuit is queried with respect to the resource requests. A set of metrics of the policy metrics data associated with the resource requests is determined and a machine learning circuit is updated. Utilized hardware resources are determined with respect to the hardware usage metering and said resource requests.

Abstract: A method and system for automatically metering a distributed file system node is provided. The method includes receiving data associated with jobs for execution via a distributed file system. Characteristics of the jobs are uploaded and policy metrics data associated with hardware usage metering is retrieved. Resource requests associated with hardware resource usage are retrieved and attributes associated with the resource requests are uploaded. The policy metrics data is analyzed and a recommendation circuit is queried with respect to the resource requests. A set of metrics of the policy metrics data associated with the resource requests is determined and a machine learning circuit is updated. Utilized hardware resources are determined with respect to the hardware usage metering and said resource requests.

Abstract: A method and system for automatically metering a distributed file system node is provided. The method includes receiving data associated with jobs for execution via a distributed file system. Characteristics of the jobs are uploaded and policy metrics data associated with hardware usage metering is retrieved. Resource requests associated with hardware resource usage are retrieved and attributes associated with the resource requests are uploaded. The policy metrics data is analyzed and a recommendation circuit is queried with respect to the resource requests. A set of metrics of the policy metrics data associated with the resource requests is determined and a machine learning circuit is updated. Utilized hardware resources are determined with respect to the hardware usage metering and said resource requests.

Abstract: A method and system for automatically metering a distributed file system node is provided. The method includes receiving data associated with jobs for execution via a distributed file system. Characteristics of the jobs are uploaded and policy metrics data associated with hardware usage metering is retrieved. Resource requests associated with hardware resource usage are retrieved and attributes associated with the resource requests are uploaded. The policy metrics data is analyzed and a recommendation circuit is queried with respect to the resource requests. A set of metrics of the policy metrics data associated with the resource requests is determined and a machine learning circuit is updated. Utilized hardware resources are determined with respect to the hardware usage metering and said resource requests.

Abstract: A system and computer-implemented method for generating an optimized allocation of a plurality of tasks across a plurality of processors or slots for processing or execution in a distributed computing environment. In a cloud computing environment implementing a MapReduce framework, the system and computer-implemented method may be used to schedule map or reduce tasks to processors or slots on the network such that the tasks are matched to processors or slots in a data locality aware fashion wherein the suitability of node and the characteristics of the task are accounted for using a minimum cost flow function.

Abstract: A method and a system for determining a skill matrix of an employee is provided. The method comprises analyzing the employee's folksonomic behavior and consequently creating the skill matrix of the employee. Folksonomy comprises creating one or more tags to annotate and categorize web content. The system includes a Tag Cloud Management Module, a Tag Cluster Generator Module and a Skill Matrix Generator Module. The Tag Cloud Management Module is configured to generate a tag cloud, wherein the tag cloud includes one or more tags created by the employee while browsing content of interest. The Tag Cluster Generator Module is configured to create one or more groups/clusters of one or more tags included in the tag cloud, based on one or more technologies related to the one or more tags. The Skill Matrix Generator Module is configured to analyze the created one or more clusters of tags to determine the skill matrix of the employee.

Abstract: A multi-layer architecture is provided for elastic provisioning of virtualized computing resources. The multi-layer architecture comprises a physical hardware layer comprising a plurality of physical computing machines, a distributed operating system layer that aggregates and virtualizes the computing resources, and a virtual machine layer that comprises virtual machines provisioned, by the distributed operating system layer, with virtualized computing resources. Elastic provisioning of virtualized computing resources comprising receiving computing resource information of a plurality of physical computing machines, producing virtualized computing resources by aggregating the received computing resource information of the plurality of physical computing machines, and provisioning the virtualized computing resources among a plurality of virtual machines.

Abstract: Composite applications can be created that utilize a plurality of different services across a plurality of different cloud stack layers. The composite applications are defined using the Service Component Architecture (SCA) model. Composite applications can be translated from the SCA model into a format compatible for a virtualization platform, such as the Open Virtualization Format (OVF). Composite applications, as defined in the format compatible for the virtualization platform, can be deployed on the virtualization platform.

Abstract: A system and computer-implemented method for generating an optimized allocation of a plurality of tasks across a plurality of processors or slots for processing or execution in a distributed computing environment. In a cloud computing environment implementing a MapReduce framework, the system and computer-implemented method may be used to schedule map or reduce tasks to processors or slots on the network such that the tasks are matched to processors or slots in a data locality aware fashion wherein the suitability of node and the characteristics of the task are accounted for using a minimum cost flow function.

Abstract: A method for creating a search index on cloud database is provided. The method enables providing inputs for creating multiple indexes on documents stored in the cloud database. One of the inputs may include a first value representing number of documents to be assigned a single index. The method further enables determining total number of documents stored in the cloud database which is represented by a second value. Further, the method enables estimating total number of indexes to be created based on first value and second value. The method further comprises executing a loop to create multiple indexes for a predetermined number of iterations which corresponds to the estimated value. Furthermore, the method comprises indexing documents for creating the multiple indexes. Finally, the method comprises merging the multiple indexes to create a single index which facilitates a user to search documents stored in the cloud database.

Abstract: A method and a system for determining a skill matrix of an employee is provided. The method comprises analyzing the employee's folksonomic behavior and consequently creating the skill matrix of the employee. Folksonomy comprises creating one or more tags to annotate and categorize web content. The system includes a Tag Cloud Management Module, a Tag Cluster Generator Module and a Skill Matrix Generator Module. The Tag Cloud Management Module is configured to generate a tag cloud, wherein the tag cloud includes one or more tags created by the employee while browsing content of interest. The Tag Cluster Generator Module is configured to create one or more groups/clusters of one or more tags included in the tag cloud, based on one or more technologies related to the one or more tags. The Skill Matrix Generator Module is configured to analyze the created one or more clusters of tags to determine the skill matrix of the employee.

Abstract: A multi-layer architecture is provided for elastic provisioning of virtualized computing resources. The multi-layer architecture comprises a physical hardware layer comprising a plurality of physical computing machines, a distributed operating system layer that aggregates and virtualizes the computing resources, and a virtual machine layer that comprises virtual machines provisioned, by the distributed operating system layer, with virtualized computing resources. Elastic provisioning of virtualized computing resources comprising receiving computing resource information of a plurality of physical computing machines, producing virtualized computing resources by aggregating the received computing resource information of the plurality of physical computing machines, and provisioning the virtualized computing resources among a plurality of virtual machines.

Abstract: Composite applications can be created that utilize a plurality of different services across a plurality of different cloud stack layers. The composite applications are defined using the Service Component Architecture (SCA) model. Composite applications can be translated from the SCA model into a format compatible for a virtualization platform, such as the Open Virtualization Format (OVF). Composite applications, as defined in the format compatible for the virtualization platform, can be deployed on the virtualization platform.