Abstract: An example operation may include one or more of receiving a request to certify a digital record, retrieving a first hashed data value of the digital record from a data block included among a first hash-linked chain of blocks on a first blockchain, retrieving a second hashed data value of the digital record from a second data block included among a second hash-linked chain of blocks on a second blockchain which is different from the first blockchain, determining whether the digital record is valid based on a cross-validation of the first hashed data value and the second hashed data value, and storing the determination of the validity of the digital record in a data block among a third hash-linked chain of blocks.

Abstract: A mechanism is provided in a data processing system for predictive and prescriptive analytics for managing high-cost claimants. The mechanism trains a machine learning model using the set of de-identified claims data to predict high-cost claimants using the training data. The mechanism applies transfer learning by retraining the machine learning model using a first set of customized client data to generate a client-specific machine learning model. The mechanism then applies the client-specific machine learning model to a second set of customized client data to identify a set of predicted high-cost claimants within the second set of customized client data. The mechanism generates association rules for determining recommendations for preventing the set of predicted high-cost claimants from becoming high-cost claimants and applies the association rules to the second set of customized client data to generate a set of recommendations.

Abstract: Techniques are provided detecting diluted drugs using machine learning. Measurements and images corresponding to a product are obtained, wherein the product is formulated as a liquid, and wherein the measurements and images capture physical, spectral, optical, and/or chemical properties of the product. The measurements and images are provided to a machine learning model, wherein the machine learning model is trained using data generated from interactive learning modules (e.g., a generative adversarial network). The machine learning model detects whether the product or chemical is a real or counterfeit product. In addition, these techniques may be used by practitioners (e.g., medical personnel dispensing a prescribed dosage of a drug with a specific dilution level) to detect prescription errors at the point of administration.

Abstract: Techniques are provided for detecting counterfeit products. Measurements and images corresponding to a product are obtained, wherein at least a portion of the measurements or images are obtained from a mobile/IoT/IoB device. The measurements and images are provided to a trained machine learning model to progressively analyze the measurements and images. Based on the progressive analysis, a determination/prediction is made with an associated confidence score as to whether the product is real or counterfeit.

Abstract: An approach for providing a cap with an embedded high-resolution lens and a sampling insert that is used during an authentication of a composition of a liquid in a container sealed by the cap. The cap has a top portion of a cap with an opening, a sampling insert inside the opening in the top portion of the cap, and a high-resolution lens inside an opening in the sampling insert.

Abstract: A method, computer system, and computer program product to detect anomalies in a multivariate or multidimensional time series data set. The time series data set is retrieved from a monitored device. A pair of neural networks are trained simultaneously using the retrieved time series data set by implementing an adversarial training process, to generate a generative neural network and a discriminative neural network. The anomalies in the time series data set of the monitored device are detected by implementing one or both of the generative neural network and the discriminative neural network to monitor the time series data set.

Abstract: Techniques are provided detecting diluted drugs using machine learning. Measurements and images corresponding to a product are obtained, wherein the product is formulated as a liquid, and wherein the measurements and images capture physical, spectral, optical, and/or chemical properties of the product. The measurements and images are provided to a machine learning model, wherein the machine learning model is trained using data generated from interactive learning modules (e.g., a generative adversarial network). The machine learning model detects whether the product or chemical is a real or counterfeit product. In addition, these techniques may be used by practitioners (e.g., medical personnel dispensing a prescribed dosage of a drug with a specific dilution level) to detect prescription errors at the point of administration.

Abstract: Techniques are provided for detecting counterfeit products. Measurements and images corresponding to a product are obtained, wherein at least a portion of the measurements or images are obtained from a mobile/IoT/IoB device. The measurements and images are provided to a trained machine learning model to progressively analyze the measurements and images. Based on the progressive analysis, a determination/prediction is made with an associated confidence score as to whether the product is real or counterfeit.

Abstract: A computer-implemented method is provided for predicting cloud enablement from storage and data metrics harnessed from across stack. The computer-implemented method includes identifying a corpus of data to be classified, and configuring at least one access threshold and at least one sensitivity threshold. The computer-implemented method also includes classifying at least a portion the data within the corpus based on the at least one access threshold and the at least one sensitivity threshold. Finally, the computer-implemented method includes outputting a model, based on the classification, that identifies at least a portion of the data for migration for enabling a hybrid cloud environment.

Abstract: One embodiment provides a quality of service (QoS) monitoring framework for dynamically binding one or more customer applications to one or more microservices in a dynamic service environment, collecting compliance data and contextual data from the dynamic service environment and one or more hosting environments, and modifying a monitoring infrastructure for the one or more customer applications based on the compliance data and the contextual data.

Abstract: One embodiment provides a method comprising receiving metadata comprising univariate time series data for each variable of a multivariate time series. The method comprises, for each variable of the multivariate time series, applying a hybrid and hierarchical model selection process to select an anomaly detection model suitable for the variable based on corresponding univariate time series data for the variable and covariations and interactions between the variable and at least one other variable of the multivariate time series, and detecting an anomaly on the variable utilizing the anomaly detection model selected for the variable. Based on each anomaly detection model selected for each variable of the multivariate time series, the method further comprises performing ensemble learning to determine whether the multivariate time series is anomalous at a particular time point.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification and information retrieval. One example method of operation may include one or more of monitoring a computing service via various nodes operating on a blockchain, identifying a proposed change to the computing service, storing details of the proposed change in a smart contract, storing the smart contract as a transaction in the blockchain, and validating the proposed change of the smart contract.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification and information retrieval. One example method of operation may include one or more of storing original data in a blockchain, storing transformed data based on the original data in the blockchain, storing chaincode on the blockchain used to transform the original data to the transformed data, and retrieving a transaction from the blockchain with the transformed data and the chaincode.

Abstract: Provided are a method, system and program for requesting storage performance models for a configuration pattern of storage resources to deploy at a client computing environment. A determination is made of a new configuration pattern of storage resources to deploy. A request is sent to a service provider with information on the new configuration pattern. A result set is received from the service provider having at least one provided configuration pattern having a degree of similarity to the new configuration pattern and a storage performance model for each of the provided configuration patterns. Each of the storage performance models indicate workload and performance characteristics for one of the provided configuration patterns. One of the provided configuration patterns is selected from the result set and the storage performance model for the selected configuration pattern is used to model performance at the client.

Abstract: A computer-implemented method according to one embodiment includes receiving a data object from a first application running on a computing device, a unique identifier (ID) of the data object assigned by the first application, and an access permission for the data object from the first application. The computer-implemented method also includes storing the data object, the unique ID, and the access permission in a data repository in a data distributor layer on the computing device. The computer-implemented method also includes receiving, at an access controller layer of the computing device, a request for the data object from a second application, the request including the unique ID, and retrieving, by the access controller layer, the data object from the data distributor layer using the unique ID in response to the request. The computer-implemented method includes providing, by the access controller layer, the data object to the second application.

Abstract: Provided are a method, system and program for publishing configuration patterns for storage resources and storage performance models from client systems to share with client systems in a network computing environment. A determination is made of a configuration pattern of storage resources within the client computing environment and storage performance metrics for the configuration pattern of storage resources within the client. At least one storage performance model is created based on the determined storage performance metrics. Information on the at least one storage performance model and the configuration pattern of the storage resources for which the storage performance metrics were determined are published to the service provider.

Abstract: A method for distributing data among storage devices. The method comprising one or more processors receiving a first graph workload that executes within a networked computing environment. The method further includes identifying data from the first graph workload that is utilized during the execution of the first graph workload that includes a plurality of data packets. The method further includes creating a first graph workload model representative of the graph structure of the first graph workload and determining two or more partitions that are representative of a distribution of the identified data utilized by the first graph workload based, at least in part, on the first graph workload model. The method further includes allocating a plurality of network accessible storage devices among the two or more partitions and copying a first set of data packets of the plurality of data packets to a network accessible storage device.

Abstract: A method and associated systems for optimized orchestration of a data-migration project. A data-migration orchestration system represents a hierarchical organization of each dataset to be migrated as a tree, where each leaf node of the tree represents data to be migrated and where a path between the leaf node and the root node represents a hierarchical directory pathname of sensitive data represented by the leaf node. Each tree is assigned a sensitivity signature that is proportional to the relative sensitivity and access frequency of the dataset represented by that tree. The signatures are organized into clusters as a function of the distances between each signature, and each signature is associated with a soft migration cost specific to that signature's cluster. A soft cost for migrating an application that requires multiple datasets may be determined by adding the migration costs associated with each of the multiple datasets.

Abstract: Systems and methods for resource provisioning are disclosed. A method includes: generating, by a computer device, a resource provisioning policy for a resource; receiving, by the computer device, a request for an allocation of the resource from an account; applying, by the computer device, the resource provisioning policy to the request based on receiving the request; automatically approving or denying, by the computer device, the request based on the applying the resource provisioning policy to the request; updating, by the computer device, the resource provisioning policy for the account based on the automatic approving or denying the request.

Abstract: A virtual blockchain configuration may provide a distributed structure that uses a distributed hash configuration to reduce the complexity of blockchain transactions. One example method of operation may comprise one or more of storing a subset of blockchain data in a network device, accessing via the network device a virtual copy of a blockchain, accessing a blockchain block via the virtual copy of the blockchain, and writing blockchain transactions to the blockchain block via the network device.