International Business Machines Corporation

Globalfoundries Inc.

Qi Zhang

Petr Novotny

Lei Yu

Nitin Gaur

Salman Abdul Baset

Ashish Kundu

Andy C Wei

Arun Kwangil Iyengar

Hong Min

Zhenyu Hu

Gong Su

Palani Sakthi

Richard J Carter

Sruthi Muralidharan

Kevin K Chan

Min Yang

Yun-Yu Wang

Dae-Gyu Park

Donna N Dillenberger

Xinhui Wang

Yuan Yuan

Venkatraman Ramakrishna

Amy L Child

Daniel W Bingham

Ruilong Xie

Ajey Poovannummoottil Jacob

Xiuyu Cai

Ravi Nair

Xusheng Kevin Wu

Michael John Hargrove

Ke Wei Wei

Shyam Ramji

Zehra Noman Sura

Motohiro Kawahito

Takuya Nakaike

Mathew S Thoennes

Peter Bendel

Taesung Lee

Heng Liu

Hongliang Shen

Tatsuhiro Chiba

Daniel T Pham

Shiqiang Wang

Tatsushi Inagaki

Yohei Ueda

Donna N Eng Dillenberger

Sean K Dunne

Shuang Ys Yu

Sunyanan Choochotkaew

Shengjiao Cao

Meichi M Lin

Byoung-Gi Min

Wisam Mohammed

Jeasung Park

Hongxiang Mo

Wenqi Cao

Marcelo Carneiro Do Amaral

In accordance with an embodiment of the invention, a method is provided for performing data pre-processing operations during data preparation of a machine learning lifecycle. The method includes defining one or more data pre-processing functions for applying to data stored in a dataset, executing one or more learn functions for learning the data, and executing one or more transform functions for transforming the data. Each of the one or more learn functions generates a first Structured Query Language (SQL) statement representing a definition of corresponding learn function for corresponding defined data pre-processing function. Each of the one or more transform functions generates a second SQL statement representing a definition of corresponding transform function for corresponding defined data pre-processing function. The dataset is stored in a database.

Performing data pre-processing operations during data preparation of a machine learning lifecycle

An example operation may include one or more of generating a blockchain request which comprises data for a blockchain, creating a tag value which identifies whether the blockchain request is a noisy request that comprises fake data or a non-noisy request that comprises non-noisy data, storing the tag value within the blockchain request, and transmitting the blockchain request to one or more blockchain peers.

Noisy transaction for protection of data

A system may enact a process including initiating an operation to transfer a secret, establishing n groups of nodes in a blockchain network, dividing a secret into n secret parts, creating n group keys, encrypting each secret part of the secret with a corresponding group key, and recording encrypted secret parts on the blockchain networks.

Secret smart operations in blockchain

An example operation may include one or more of receiving, via a first blockchain peer, a request to verify an identity of a user, performing, via the first blockchain peer, an identity validation of the user via a know your customer (KYC) service provider of a blockchain network, determining whether a digital identity of the user has been previously stored by another blockchain peer on a blockchain of the blockchain network, in response to a determination that the digital identity of the user has not been stored on the blockchain, creating a non-fungible token (NFT) that identifies the user, and appending information about the identity validation of the user to a structure of the NFT and storing the NFT in a block of the blockchain.

Token-based identity validation via blockchain

An approach is provided in which a method, system, and program product analyze, while training a machine learning model, a set of first data transformation operators in a first data preparation pipeline that generates a plurality of constructed features from a set of training data. The method, system, and program product create a plurality of second data preparation pipelines from the first data preparation pipeline, wherein the set of first data transformation operators are converted to a set of second data transformation operators and each assigned to one of the plurality of second data preparation pipelines. The method, system, and program product deploy the plurality of second data preparation pipelines to a runtime system.

Auto feature preparation for high performance online inferencing

Mechanisms for performing intelligent federated machine learning (ML) model updates are provided. A plurality of ML model updates, and a plurality of dataset sketch commitment data structures (sketches), are received from a plurality of participant computing systems. Each sketch provides statistical characteristics of a corresponding local dataset used by a corresponding participant to train a local ML model. A potentially malicious participant identification operation is performed based on an analysis of the plurality of sketches to identify one or more potentially malicious participants based on differences in sketches. ML model updates received from participant computing systems identified as potentially malicious participants are discarded to thereby generate a modified set of updates. The federated ML computer model is updated based on the modified set of updates.

Federated learning with dataset sketch commitment based malicious participant identification

An approach for blockchain transaction processing using generative cryptograms for bi-lateral and Multi-lateral transactions may be provided. The approach may include, the use of decentralization ledger storage, negating the need of repetitive storage while preserving the need for data redundancy. A generative cryptogram may provide verification of processed blocks and file handles or storage address schemes of the ledger storage. The approach creates a cryptogram which is added to transaction processing, post processing and commits to the ledger. In the approach, the cryptogram is the only version of storage pointer and is utilized in subsequent transaction processing without the need for extensive compute and latest cryptogram is all that would be needed for transaction processing.

Generative cryptogram for blockchain data management

A method and apparatus for loading balancing among blockchain peers by using a probing transaction from a client node to identify a latency of each blockchain peer. The client node can then select which blockchain peers to use based on the identified latencies.

Load balancing based blockchain transaction submission

An example operation may include one or more of receiving, by a risk score module, a blockchain transaction proposal, obtaining transaction proposal data, obtaining external data, computing a risk score from the transaction proposal data and the external data, comparing the risk score to a risk score threshold, providing a commitment decision, based on the comparison, and one of committing or rejecting an endorsed transaction corresponding to the transaction proposal.

Anomalous transaction commitment prevention for database

A client computer may split a process into sub-processes, send each sub-processes to a different group of peers in a blockchain network, wherein each group has at least one peer from each essential organization in the blockchain network, receive processed sub-transactions from the peers in the blockchain network, validate each sub-transaction, and validate the transaction based on the validation of all sub-transactions, wherein all sub-transaction must be valid for the transaction to be valid.

Parallel processing of blockchain procedures

A computer-implemented method for detecting bottlenecks in microservice cloud systems is provided including identifying a plurality of nodes within one or more clusters associated with a plurality of containers, collecting thread profiles and network connectivity data by periodically dumping stacks of threads and identifying network connectivity status of one or more containers of the plurality of containers, classifying the stacks of threads based on a plurality of thread states, constructing a microservice dependency graph from the network connectivity data, aligning the plurality of nodes to bar graphs to depict an average number of working threads in a corresponding microservice, and generating, on a display, an illustration outlining the plurality of thread states, each of the plurality of thread states having a different representation.

Detecting layered bottlenecks in microservices

A processor may record one or more transaction proposals regardless of validity of the one or more transaction proposals. The processor may separate a transaction validation into a series of steps. The processor may validate, asynchronously, one or more transactions respectively associated with each of the one or more transaction proposals based on the transaction validation.

Topological ordering of blockchain associated proposals

An example operation may include one or more of collecting, by a lead peer, state and quality of service (QoS) data from a plurality of peers of a blockchain network, building, by the lead peer, a network graph (NG) based on the state and the QoS data from the plurality of the peers, and mapping, by the lead peer, the NG to a block deliver graph (BDG), wherein edges of the BDG represent a sequence of blocks to be sent from a source peer to a destination peer.

Optimization of delivery of blocks

An example operation may include one or more of receiving, by a blockchain node or peer comprising hot asset storage and cold asset storage, an asset request to provide an asset, the asset comprising a key-value pair, determining if the asset request may be satisfied without accessing a blockchain of a shared ledger, providing the asset to a requesting node or peer from hot asset storage in response to the blockchain node or peer determining the asset request may be satisfied without accessing the blockchain, and in response to the blockchain node or peer determining the asset request cannot be satisfied without accessing the blockchain, the method further comprising utilizing a pointer in cold asset storage to obtain the requested asset from the blockchain.

Database world state performance improvement

An example operation may include one or more of receiving, by a risk score module, a blockchain transaction proposal, obtaining transaction proposal data, obtaining external data, computing a risk score from the transaction proposal data and the external data, comparing the risk score to a risk score threshold, providing an endorsement decision, based on the comparison, and one of endorsing or rejecting the transaction proposal.

Anomalous transaction detection for database

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Elmsford, NY, United States of America

Qi Zhang