# US 12,602,263 B2 · Transparent data transformation and access for workloads in cloud environments > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,602,263 B2 - Document ID: US12602263B2 - Status: Granted - Kind: B2 - Application number: 17/138,858 - Filed: 2020-12-30 - Published: 2022-06-30 - Granted: 2026-04-14 - Earliest claimed: 2020-12-30 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Ioannis Gkoufas; Christian Pinto; Srikumar Venugopal; Stefano Braghin - Web page: https://patentgrep.com/patent/US12602263B2 ## CPC - G06F 9/5083: {Techniques for rebalancing the load in a distributed system} - G06F 9/5027: {the resource being a machine, e.g. CPUs, Servers, Terminals} - G06F 9/5072: {Grid computing} - G06F 16/258: {Data format conversion from or to a database} - G06F 21/6254: {by anonymising data, e.g. decorrelating personal data from the owner's identification} - G06N 20/00: Machine learning - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A computer-implemented method of providing data transformation includes installing one or more data transformation plugins in a dataset made accessible for processing an end user's workload. A dataset-specific policy for the accessible dataset is ingested. A data transformation of the accessible dataset is executed by invoking one or more of the data transformation plugins to the accessible dataset based on the dataset-specific policy to generate a transformed dataset. The user's workload is deployed to provide data access for processing using the transformed dataset in accordance with a data governance policy. ## Claims ### Claim 1 A computer-implemented method of providing data transformation, the computer-implemented method comprising: installing, prior to processing a computational workload of an end-user on a dataset, data transformation plugins comprising data-caching, data anonymization, and node selection, in the dataset made accessible for processing the computational workload; ingesting a dataset-specific policy for the accessible dataset and a data governance policy for the accessible dataset, wherein the data governance policy defines node selection rules for the accessible dataset; executing the data transformation of the accessible dataset by invoking the data transformation plugins to the accessible dataset based on the dataset-specific policy to generate a transformed dataset using the data transformation plugins, wherein the transformed dataset is anonymized and stored in a local cache of a computing device, for deployment and processing; determining, based on the data governance policy, one or more authorized computational nodes permitted for deployment of the computational workload; and deploying, by the computing device, the computational workload to the determined one or more authorized computational nodes to provide data access for processing with the transformed dataset. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the executing of the data transformation of the accessible dataset and the deploying of the computational workload to provide the data access for the processing using the transformed dataset are performed on a cloud. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, further comprising creating a new endpoint for the data access of the transformed dataset. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the computational workload comprises a computing or a machine learning computational workload, and the provided data access using the transformed dataset is data transparent to the end-user. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the execution of the data transformation by invoking the data transformation plugins includes activating a data-caching plugin installed in the accessible dataset. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the invoking of the data transformation plugins comprises activating an anonymization plugin installed in the accessible dataset for the data anonymization. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, further comprising applying materializing policies for applying the data transformation according to the data governance policy. ### Claim 8 (depends on 7) The computer-implemented method of claim 7, wherein the applying of the materializing policies related to the data governance policy is based on a location of the end-user. ### Claim 9 A computing device, comprising: a processor; a storage device coupled to the processor; and an engine stored in the storage device, wherein an execution of the engine by the processor configures the computing device to: install, prior to processing a computational workload of an end-user on a dataset, data transformation plugins comprising data-caching, data anonymization, and node selection, in the dataset made accessible for processing the computational workload; ingest a dataset-specific policy for the accessible dataset and a data governance policy for the accessible dataset, by a policy ingestion component, wherein the data governance policy defines node selection rules for the accessible dataset; apply the data transformation plugins to the accessible dataset based on the dataset-specific policy to generate a transformed data source that is anonymized and stored in a local cache of the storage device, for deployment and processing, by a dataset mutator; apply, to the dataset, policies related to the data governance policy and a location of the end-user, by an orchestrator; determine, based on the data governance policy, one or more authorized computational nodes permitted to process the transformed data source, by the orchestrator; update user-defined computational workload definitions transparently for use with the transformed data source; and deploy the computational workload with access to the transformed data source for processing to the determined one or more authorized computational nodes, by a computational workload mutator. ### Claim 10 (depends on 9) The computing device of claim 9, wherein the dataset mutator is configured to apply a data anonymization plugin to the accessible dataset. ### Claim 11 (depends on 9) The computing device of claim 9, wherein the dataset mutator is configured to a data-caching plugin to the accessible dataset. ### Claim 12 A computing device providing data transformation, the computing device comprising: a processor; and a memory coupled to the processor, the memory storing instructions to cause the processor to perform operations comprising: installing, prior to processing a computational workload of an end-user on a dataset, data transformation plugins comprising data-caching, data anonymization, and node selection, in the dataset made accessible for processing the computational workload; ingesting a dataset-specific policy for the accessible dataset and a data governance policy for the accessible dataset, wherein the data governance policy defines node selection rules for the accessible dataset; executing the data transformation of the accessible dataset by invoking the data transformation plugins to the accessible dataset based on the dataset-specific policy to generate a transformed dataset using the data transformation plugins, wherein the transformed dataset is anonymized and stored in a local cache of the computing device, for deployment and processing; determining, based on the data governance policy, one or more authorized computational nodes permitted for deployment of the computational workload; updating user-defined computational workload definitions transparently for use with the transformed dataset; and deploying the computational workload to the determined one or more authorized computational nodes to provide data access for processing with the transformed dataset. ### Claim 13 (depends on 12) The computing device of claim 12, wherein the the operations further comprise activating a data-caching plugin installed in the accessible dataset. ### Claim 14 (depends on 12) The computing device of claim 12, wherein the operations further comprise activating a data anonymization plugin installed in the accessible dataset. ### Claim 15 (depends on 12) The computing device of claim 12, wherein the the operations further comprise creating a new endpoint for the data access in the generating of the transformed dataset. ### Claim 16 A non-transitory computer-readable storage medium tangibly embodying a computer-readable program code having computer-readable instructions that, when executed, causes a computer device to perform operations of providing data transformation, the operations comprising: installing, prior to processing a computational workload of an end-user on a dataset, data transformation plugins comprising data-caching, data anonymization, and node selection, in the dataset made accessible for processing the computational workload; ingesting a dataset-specific policy for the accessible dataset and a data governance policy for the accessible dataset, wherein the data governance policy defines node selection rules for the accessible dataset; executing the data transformation of the accessible dataset by invoking the data transformation plugins to the accessible dataset based on the dataset-specific policy to generate a transformed dataset using the data transformation plugins, wherein the transformed dataset is anonymized and stored in a local cache of a computing device, for deployment and processing; determining, based on the data governance policy, one or more authorized computational nodes permitted for deployment of the computational workload; and deploying the computational workload to the determined one or more authorized computational nodes to provide data access for processing with the transformed dataset. ### Claim 17 A computer-implemented method for performing data transformation in a Kubernetes framework for a cloud-based application, the computer-implemented method comprising: installing, by a computing device, prior to processing a computational workload on a dataset, data transformation plugins comprising data-caching, data anonymization, and node selection, in the dataset made accessible for processing the computational workload; receiving, by the computing device, the accessible dataset including a masked ID field and a restriction to one or more authorized computational nodes for processing; ingesting, by the computing device, a dataset-specific policy for the accessible dataset and a data governance policy for the accessible dataset, wherein the data governance policy defines node selection rules for the accessible dataset; matching, by the computing device, the ingested dataset-specific policy and the data governance policy with available data transformations for the dataset; executing, by the computing device, the data transformation of the accessible dataset by invoking the data transformation plugins to the accessible dataset based on the dataset-specific policy to generate a transformed dataset that is anonymized and stored in a local cache of the computing device, for deployment and processing, wherein the invoked data transformation plugins correspond to the available data transformations; determining, by the computing device, based on the data governance policy, the one or more authorized computational nodes permitted for deployment of the computational workload; and deploying, by the computing device, the computational workload to the determined one or more authorized computational nodes to provide data access for processing with the transformed dataset. ### Claim 18 (depends on 17) The computer-implemented method of claim 17, further comprising creating a new endpoint for the data access in the generating of the transformed dataset. ### Claim 19 (depends on 17) The computer-implemented method of claim 17, further comprising caching the transformed dataset, transparently, in the determined one or more authorized computational nodes according to the data governance policy.