International Business Machines Corporation

Prateek Goyal

Manish Anand Bhide

Kuntal Dey

Seema Nagar

Sougata Mukherjea

Pramod Vadayadiyil Raveendran

Ravi Chandra Chamarthy

Madhavi Katari

A method includes receiving, by a computing device, concepts of a domain; determining, by the computing device, objects relevant to the concepts; generating, by the computing device, a new image by stitching the relevant objects together; determining, by the computing device, whether the new image is accurate or inaccurate; and in response to determining the new image is inaccurate, propagating, by the computing device, the inaccurate new image back to a convolutional neural network (CNN).

Object stitching image generation

An example operation may include one or more of storing a batch scoring engine and an application programming interface (API) for the batch scoring engine, receiving a trigger to perform a batch prediction process, reading input data from a source data store and executing, via the batch scoring engine, one or more predictive models on the input data to generate a predictive output and metadata associated with the predictive output, storing the predictive output and the metadata in a target data store, and updating the API with a location of the predictive output within the target data store and a location of the metadata within the target data store.

API management for batch processing

Early indications of application programming interface (API) usage are identified by correlation to particular issues with the API including singular and mutual consistency, completeness, accuracy, and staleness. Analysis of API input and output along with data type and formatting information facilitates identification of the API issues. Establishing a correlation between API usage and issues supports early detection of potential usage reduction on a case-by-case level. Corrective action to resolve identified issues may be performed in a timely manner to maintain usage levels.

Correlating usage to issues in application programming interfaces

One or more computer processors determine a storage strategy for each chunked data block in a training dataset based on a respective computed usefulness score and a series of usefulness thresholds, wherein the storage strategy comprises RAID strategies that include striping, mirroring, parity, and double parity. The one or more computer processors distribute each data block in the training dataset according to the respective determined storage strategy.

Defining redundant array of independent disks level for machine learning training data

A system and method is provided to selectively process and store tables of a relational database by calculating an overall data sensitivity score for each table based on predefined attribute rules; performing column-wise splitting of at least one of the tables into a first table and a second table based on the overall data sensitivity score of each table, thereby generating a total number of relational database tables; storing a first subset of the total number of relational database tables in a private cloud storage database in a distributed storage environment based on the overall data sensitivity scores of each of the total number of relational database tables; and storing a second subset of the total number of relational database tables in a public cloud storage database of the distributed storage environment based on the overall data sensitivity scores of each of the total number of relational database tables.

Sensitivity-based database processing and distributed storage

A software defined data security level method, computer program product, and data processing system. One embodiment may comprise intercepting, by a processor at a data security layer, an input/output (IO) request from a local software application, wherein the IO request includes a header and a data payload, analyzing, by the processor at the data security layer, the data payload of the IO request relative to a service level agreement (SLA), assigning, by the processor at the data security layer, a security level to the IO request based on the analysis.

Software defined data security layer

Application-specific prioritization of streaming data replication. Data streamed from connected devices is selectively replicated to data storage clusters based on needs of the applications being served by the data. Data characterization supports prioritized replication processing. Statistical metrics compare streaming data with estimated values to characterize the data for prioritization.

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Indore, India

Prateek Goyal