International Business Machines Corporation

Blazej Rafal Rutkowski

Maria Hanna Oleszkiewicz

Bartlomiej Tomasz Malecki

Daniel Jakub Ryszka

Wojciech Sobala

Piotr Padkowski

Lukasz Grzegorz Cmielowski

Rafal Bigaj

Adam Babol

Marcin Smetek

Mariusz Kolodziej

Systems and methods for composite storage of data across tiers of a multi-tenant storage device are provided. In embodiments, a method includes determining attributes associated with each query in a set of queries; determining whether each of the attributes meet respective predetermined threshold values for a first storage tier, a second storage tier and a third storage tier based on statistical data generated for the attributes; updating storage rules for at least one of the first storage tier, the second storage tier and the third storage tier based on the determining; receiving data from the tenant for storage; dividing the data into at least a first portion and a second portion based on the storage rules; and storing the at least the first portion and the second portion on separate ones of the first storage tier, the second storage tier and the third storage tier based on the storage rules.

Composite storage of objects in multi-tenant devices

An ML root model format having a root model definition, is converted into an ML target model format not having the root model definition. A learning system is assigned to the root model definition that is convertible to the machine learning target model format. A new version of the root model definition is ported from the ML root model to the format of the ML target model to generate a new version of the machine learning target model after a learning iteration of the learning system. Quality limits are determined using an X control chart method based on a cross-validation of fold results generated during a validation of the new version of the machine learning root model. A quality metric value of the new version of the ML target model is evaluated against the quality threshold values of the new version of the ML root model.

Continuous learning system for models without pipelines

A computer-implemented method for transferring data is provided. In an illustrative embodiment, the method includes retrieving, by a computer, an original dataset to be sent from a sender to a receiver. The method also includes generating, by the computer, a model based on at least a subset of the original dataset. The model generates a predicted dataset. The model is selected from a plurality of model types based on data complexity of the original dataset and a desired level of approximation of the predicted dataset to the original dataset. The method also includes transferring, by the computer, the model to the receiver. The receiver uses the model to generate the predicted dataset, wherein the predicted dataset matches the original dataset to a selected degree of approximation. Transfer of the model is quicker than transfer of the original dataset.

Transferring large datasets by using data generalization

A method for enhancing a set of object scan images may be provided. The set of object scan images comprises at least two scan images. The method comprises determining first distance and determining an interpolated scan image, by applying an interpolation algorithm comprising determining any pixel of the interpolated scan image as a white pixel if the corresponding pixels on the scan images are both white. The same applies to black pixels. The method may further comprise determining any pixel which corresponding pixels are black on one of the two image scans and white on the other image scans as white (black) if a predefined percentage of the directly surrounding pixels is white (black) in the interpolated scan image. Furthermore, the method comprises inserting the interpolated scan image between the first scan image and the second scan image and repeating the above steps until a stop condition is met.

Reconstructing missing slices in slice-based scans of 3D objects

A method for controlling exposure of sensitive data though a logging system is provided. The method comprises: upon receiving a request by the request handler, determining sensitive data as part of the request by applying a rule, converting the data into a transformed format, and registering the data together with a related data field label with a log handler. Then, upon receiving by the log handler a log entry, converting each expression of the log entry into the transformed format, and comparing each transformed expression with each of the sensitive data in the transformed format. Upon determining a match of one of the transformed expressions with one of the sensitive data in the transformed format, the method comprises issuing an alert indicating that the log entry comprises sensitive data.

Detecting sensitive data exposure via logging

A system for enhancing a set of object scan images may be provided. The set of object scan images may also here comprise at least a first scan image, located at a distance therefrom a second scan image.

A computer-implemented method of creating a log file is disclosed. The method comprises: storing a request of a service to be executed by a computer system, wherein the service is performed using multiple software methods; executing the service by providing the request to the computer system; detecting a failure of the service; performing a stack trace of the service to identify a subset of the multiple software methods; providing a source code for the subset of the multiple software methods; modifying the source code for the subset of the multiple software methods to add logging statements, wherein the logging statements are configured for generating log data; re-executing the service using the modified source code to generate the log data; and generating the log file from the log data.

Automatic log generation

A software installation method comprising receiving an instruction to install a first software entity in a first computing environment, establishing a virtual computing environment in the first computing environment, and installing the software entity in the virtual computing environment, wherein the establishing and the installing are subject to a determination that a user is not privileged to install the first software entity in the first computing environment.

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Cracow, Poland

Blazej Rafal Rutkowski