International Business Machines Corporation

Bartlomiej Tomasz Malecki

Pawel K Gocek

Marek Peszt

Piotr Padkowski

Michal Klak

Pawel J Borkowski

Konrad K Skibski

Michal S Paluch

Maria Hanna Oleszkiewicz

Joanna Wawrzyczek

Grzegorz K Lech

Jan Marszalek

Piotr Kania

Blazej Rafal Rutkowski

Daniel Jakub Ryszka

Maksymilian J Kowalik

Lukasz Grzegorz Cmielowski

Rafal Bigaj

Tomasz Andrzej Stopa

Artur M Gruszecki

Tomasz Kazalski

Lukasz Gaza

Tomasz Stradomski

Piotr Jozef Walczak

Piotr P Godowski

Pawel Slowikowski

Artur Zezula

Krzysztof Pienkowski

Piotr Józef Walczak

Arkadiusz W Biegun

Tomasz D Chmielecki

Wojciech Sobala

Wlodzimierz Martowicz

Marcin D Gierlak

Slawomir T Mezyk

Jagoda Kuzma

Jakub Kania

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

Aspects of the present invention provide an approach for exposing payloads from non-integrated machine learning systems. A generic binding identifier is established to represent a machine learning (ML) system among a set of non-integrated learning systems. A generic subscription identifier is established to represent a deployed model in the ML system. Payload data including a user request, a response, the generic binding identifier, and the generic subscription identifier are received from the ML system and stored in a database for later analysis to identify any issues related to the deployed model.

Exposing payload data from non-integrated machine learning systems

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 computer-implemented method for determining an evaluation metric value for a multi-class classifier with unknown labels may be provided. The method comprises providing a trained classifier system for classifying input data vectors resulting in a plurality of result label values and respective probability vectors, predicting, based on received input data vectors, result label values and respective probability vectors, and receiving feedback data comprising respective feedback label values. The method comprises also mapping each of the feedback label values to a respective highest probability dimension value of the related probability vector resulting in an index map, and determining, based on the index map, an evaluation metric value for the quality of the trained classifier system.

Multi-class classification evaluation metric determination

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 method, system and computer program product for providing early diagnosis of hardware, software or configuration problems in a data warehouse system. A received query is parsed to determine the properties of the query. The query may then be joined to existing groups of queries if those groups have shared properties of the query. After executing the query according to an execution plan, results from the execution of the query is received, which may include problem(s) that occurred during execution of the query. For those problems that reach a pre-defined threshold of becoming a 'group problem' in those groups joined by the query, the problem is reported to the end user concerning those groups where the problem exceeds the pre-defined threshold. In this manner, an early diagnosis of the problems in the data warehouse system that can cause delay and failure of the processing of queries is able to occur.

Early diagnosis of hardware, software or configuration problems in data warehouse system utilizing grouping of queries based on query parameters

A method, a computer system, and a computer program product for clustering operational parameter values in a micro-service architecture used in a computing infrastructure. The computer system measures a plurality of operational parameter values of elements of the computing infrastructure and logs identifiers for elements having caused a problem situation and related problem resolution times. The computer system clusters the operational parameter values of the elements having caused the problem situation, according to a correlation function. The computer system orders the operational parameter values within a cluster and the elements having caused the problem situation. The computer system periodically performs the clustering and the ordering such that a sequence of the operational parameter values and the elements having caused the problem situation is indicative of a resolution time required for a new problem situation.

Optimized incident management using hierarchical clusters of metrics

A mechanism is provided for adding software signatures to a software catalog may be provided. The software catalog comprises a vendor-maintained software catalog section, a user-maintained software catalog section, and a community-maintained software catalog section. The mechanism scans a system using a software asset management tool. The mechanism adds a new software signature to the community-maintained software catalog section via a Blockchain transaction. An acceptance of the signature into the community-maintained software catalog section is based on a received Blockchain proof-of-work message.

Distribution of software signatures using a community catalog based on blockchain

A method for connecting graph processing cut endpoints is disclosed. The method comprises determining a first capability matrix comprising capability values, applying weights to the capability values of the first capability matrix resulting in a first weighted capability matrix, and determining a second capability matrix comprising, for each pairing of another side of the cut endpoint, capability values. For both, the first capability matrix and second capability matrix, the capability values relate at least to a platform, a runtime framework, a connection technology, a protocol for a connection of a node of the stream processing system. The method comprises further determining a joint weighted capability matrix for two corresponding cut endpoints of a cut by multiplying corresponding capability matrix values of the first weighted capability matrix and the second capability matrix, and selecting a preferred connection for the cut endpoints.

Automatic selection of cut-point connections for dynamically-cut stream processing systems

A method for detecting a bottleneck in a system includes receiving a graph, wherein a node represents a software module and an edge represents a communication channel between software modules, monitoring selected resources for each software module in comparison to available resources, monitoring a ratio of a bandwidth consumed on a communication channel versus available bandwidth, traversing the graph for identifying a source software module whose produced amount of output is below the amount of output needed by the software module that is in idle mode, and analyzing a resource consumption of the identified source software module to identify a lacking amount of resource for the identified source software module. A computer system and computer program product corresponding to the above method are also disclosed herein.

Detecting and predicting bottlenecks in complex systems

Nesting information indicative of a nested arrangement of a set of scrollable areas within an environment is obtained. Focus information indicative of which of the set of scrollable areas is in focus within the environment is obtained. A visualization scheme is applied to each of the set of scrollable areas other than the scrollable area in focus. The visualization scheme adjusts at least one display property of each other scrollable area by each of a function of a relative position of each other scrollable area relative to the scrollable area in focus within the nested arrangement and a function of a relative distance of each other scrollable area from the scrollable area in focus.

Scroll focus

The invention relates to an NFC device configured for receiving a request to perform a transaction. In response to receiving the request, sensor data from one or more sensors of the NFC device is fed into a first classifier. The sensor data comprises environment parameters of the NFC device. The first classifier determines if sensor data provided as input to the first classifier were sensed in a transaction-typical environment or in a transaction-atypical environment. In case the first classifier determines that the sensor data is indicative of a transaction-atypical environment, the first classifier blocks the transaction or requests a user to confirm the transaction and blocks the transaction until the confirmation is received.

Blocking fraudulent transactions in an NFC device

A processor may identify, using historical data, an amount of computing resources consumed to remedy the failure with an automatic remedy step. The processor may determine that the amount of consumed computing resources to remedy the failure is less than an amount of computing resources consumed by restarting the process. The processor may perform the automatic remedy step. The processor may identify that the automatic remedy step has failed. The processor may determine a waiting period based on an estimated time to receive a user response to the failure and an estimated load on the computing cluster. The processor may display a generated alert to a user during the waiting period. The processor may identify that no user input has been received during the waiting period. The processor may release computing resources corresponding to the process.

Automatic correcting of computing cluster execution failure

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.

Growing community of inventors

Slomniki, Poland

Bartlomiej Tomasz Malecki