International Business Machines Corporation

Vaibhav Murlidhar Kulkarni

Sweta Singh

Gopikrishnan Varadarajulu

Padmanabhan Krishnan

Rakhi S Arora

Eitan D Farchi

Mario D Briggs

Aiswarya L Mohanasundaram

Manish Anand

Samir Katti

Srikkanth R Kulkarni

Mario Dominic Savio Briggs

Deepak Anil Mahajan

Harshita Pandey

Devyanshi Singh

Missula Meghana

Dynamically determining timing for a machine learning model reward signal by receiving, by a first machine learning model, data associated with a first system state, and an action determined by a second machine learning model according to the first system state, determining, by the first machine learning model, a sleep time duration for the second machine learning model according to the first system state and the action, receiving, by the first machine learning model, after the sleep time duration, data associated with a second system state, determining, by the first machine learning model, a first reward signal according to the difference between the first system state and the second system state and the sleep time duration and updating the first machine learning model according to the first reward signal, the difference between the first system state and the second system state, and the sleep time duration.

Determining reinforcement learning model reward signal timing

Provided is a method for method for encrypting log file data in a multitenant database. The method comprises receiving a request to secure data of a tenant in a multitenant database. The method further comprises obtaining a symmetric encryption key that is specific to the tenant. The method further comprises encrypting data of the tenant using the symmetric encryption key. The data that is encrypted is stored in the multitenant database. The method further comprises encrypting a set of log file entries using the symmetric encryption key. The set of log file entries are associated with data of the tenant.

Encryption of database log files

One or more computer processors identify one or more similar, historical regression tests and historical builds utilizing a computed similarity measure between a regressed build and one or more historical builds conducted on a same release cycle, wherein the identified one or more similar historical regression tests and historical builds are K closest neighbors to the regressed build; predict an elapsed time of the one or more profiled regression tests utilizing a KNN algorithm comprising the K closest neighbors each weighted by a corresponding average distance from a test point and the elapsed time as a target variable; responsive to the predicted elapsed time exceeding an actual elapsed time associated with the regressed build, determine that the regressed build is an actual regression; responsive to determining that the regressed build is not due to variability, apply one or more mitigation actions to the regressed build.

Mitigating regression test variability

Embodiments are for generating a digital signature of a query execution plan using similarity hashing. A technique includes generating a node digital signature for nodes in a query and generating an edge digital signature for edges in the query, the edges connecting the nodes. The technique includes selecting at least one previously executed query based on the node digital signature and the edge digital signature for the query and causing the query to be processed according to an assignment associated with the at least one previously executed query.

Generate digital signature of a query execution plan using similarity hashing

Some embodiments of the present invention are directed towards techniques for validating performance degradation of cloud deployed application from neighbor based variability. Historical runs of an application deployed in a cloud environment are received. In these embodiments, a subset of these historical runs, using associated performance metrics recorded during the historical runs, are compared against performance metrics of a current version of the application which is deployed in a cloud environment to determine a subset of historical runs similar to the current version. The determined subset is then used to draw comparisons with performance metrics of a baseline run of the application to validate if a performance degradation has occurred by updating the application to the current version, reducing the impact of neighbor-based variability on evaluating performance degradation. Detailed performance analysis resources are less likely to be expended on performance degradation caused by neighbor activities in the cloud environment.

Detecting performance degradation in remotely deployed applications

In a secure multi-party computation (sMPC) system, a super mask is constructed using a set of masks corresponding to a set of data contributors. Each data contributor uses a corresponding different mask to obfuscate the data of the data contributor. a first scaled masked data is formed by applying a first scale factor to first masked data of the first data contributor, the scale factor being computed specifically for the first data contributor from the super mask. A union is constructed of all scaled masked data from all data contributors, including the first scaled masked data. A machine learning (ML) model is trained using the union as training data, where the union continues to keep obfuscated the differently masked data from the different data contributors. The training produces a trained ML model usable in the sMPC with the set of data contributors.

Machine learning with differently masked data in secure multi-party computing

Textual masking for multiparty computation is provided. The method comprises receiving masked input data from a number of contributors, wherein the input data from each contributor has a unique contributor mask value. A unique analyst mask factor is received for each contributor, computed by an analyst as a difference between a uniform analyst mask value and the contributor mask value. An API call is received from the analyst to aggregate the input data from the contributors. The respective analyst mask factors are added to the input data from the contributors, and the data is aggregated and shuffled. Computational results received from the analyst based on the aggregated input data are published. In response to API calls from the contributors, the analyst mask factors are removed from the computational results, wherein computational results received by each contributor are masked only by the respective contributor mask value.

Masking text data for secure multiparty computation

Embodiments of the present invention relate to a method, computer program product and system for optimizing database transactions configured for receiving a query. The query specifies a set of predicates supplied to the query and a minimal number of predicates to be satisfied for the query to be true. An operation using the query is performed on a repository that is stored in a computer readable storage medium. A set of results satisfying the minimal number of predicated is rendered.

Optimizing database query

A first query is received including a logical expression as a set of predicates in Disjunctive Normal Form. Each predicate from is represented as a bitwise predicate pattern to generate a set of bitwise predicate patterns. A set of valid bitwise patterns from the set of bitwise predicate patterns is identified using the first query. The set of valid bitwise patterns is parsed using logical operators to generate a logical bitwise expression. The logical bitwise expression is factored and the factored logical bitwise expression is mapped to corresponding predicates to generate a logical predicate expression. A second query is generated, which includes modifying the first query using the logical predicate expression and at least one logical operator. A subset of data is retrieved from the data repository using the second query.

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Bengaluru, India

Vaibhav Murlidhar Kulkarni