International Business Machines Corporation

Kyndryl, Inc.

Amitava Kundu

Manoj Kumar Agarwal

Curt Lee Cotner

Rajesh Sambandhan

Prasan Roy

Raghuveer Prasad Nagar

Sarbajit Kumar Rakshit

Shikhar Kwatra

Mangesh V Shanbhag

Petr Novotny

Sujatha B Perepa

Pritesh Patel

Abhijit Singh

Sujan Sarathi Ghosh

Kantharaju P G

Rajesh Samabandhan

Rajasekhar Thallam

Aspects of the present disclosure relate generally to reminder systems and, more particularly, to a contextual item discovery and pattern inculcated reminder mechanism and methods of use. A computer-implemented method includes: determining, by a computing device, an event in which a user will participate; associating, by the computing device, one or more items with the event; determining a location of the one or more items associated with the event including that the user does not possess any combination of the one or more items; and providing an alert to the user that the any of the one or more items associated with the event is not in the possession of the user.

Contextual item discovery and pattern inculcated reminder mechanism

Provided are techniques for detecting contamination propagation and performing decontamination. In response to receiving a request to detect contaminants and perform decontamination, an entity is identified. Whether there are contaminants on the entity is detected using any combination of sensor data, one or more images, and one or more contamination propagation patterns. In response to determining that there are no contaminants, the entity is allowed to pass through an entry point. In response to determining that there are contaminants, one or more decontamination techniques are identified, decontamination of the entity is performed, using the one or more decontamination techniques, and, in response to determining that the decontamination has been successful, the entity is allowed to pass through the entry point.

Detecting contamination propagation

A method, system, and computer program product for virtual reality simulation-based device configuration are provided. The method generates a virtual reality environment for a simulated activity. Interactions of a user are monitored within the virtual reality environment. Based on the interactions of the user, one or more simulated devices used are identified during the simulated activity. The method determines a set of device characteristics for the one or more simulated devices. A set of simulated configurations are determined for the one or more simulated devices based on the set of device characteristics. The method matches a set of configurations for one or more physical devices to the set of simulated configurations for the one or more simulated devices.

Virtual reality-based device configuration

A tool for providing context-based copy and paste commands. The tool monitors a plurality of user activities across one or more user devices for a plurality of copied content items and associated contextual data. The tool generates a list of the plurality of copied content items and the associated contextual data. Responsive to a determination that a user issued a paste command, the tool calculates a relevancy score for each of the plurality of copied content items in the list. The tool determines at least one of the plurality of copied content items to fulfill the paste command based, at least in part, on the relevancy score for each of the plurality of copied content items. The tool presents the at least one of the plurality of copied content items to a user to select for the paste command.

Contextual copy and paste across multiple devices

Method and apparatus for using machine learning to monitor biometric data to provide intelligent alerts are provided. At a first moment in time, first biometric data for a plurality of users are received from a plurality of sensor devices. A group metric is generated by processing the first biometric data using at least one trained machine learning model, and it is determined that the group metric does not satisfy one or more predefined criteria. At a second moment in time, second biometric data for the plurality of users is received from the plurality of sensor devices, and an updated group metric is generated by processing the second biometric data using the at least one trained machine learning model. Upon determining that the updated group metric satisfies the one or more predefined criteria, an indication is provided that the one or more predefined criteria have been satisfied.

Machine learning system for predicting optimal interruptions based on biometric data collected using wearable devices

Sequences of hierarchical records are aggregated and summarized. A capture log that includes a plurality of operations of a workload is received. A first data structure that models transaction types as sequences of nodes is created. The nodes identify operations in the workload. A present operation and a transaction identifier are read from the capture log. The transaction identifier is dissociated from a first node that identifies a prior operation. The transaction identifier is associated with a second node that identifies the present operation. In a second data structure that associates nodes with transaction identifiers, the first node is dissociated from the transaction identifier and the second node is associated with the transaction identifier. A summary of the workload is generated based, at least in part, on the first and second data structures. The summary includes signatures of transaction types and counts of instances of the transaction types.

Aggregating and summarizing sequences of hierarchical records

Database operation records are sequentially read from two or more log files. If the transaction identifier is new and the record is not an end-of-transaction record, an open transactions list entry is created. If the transaction identifier is new and the record is an end-of-transaction record, a transaction type list entry is created or updated. If the transaction identifier is not new and is not an end-of-transaction record, an open transactions list entry is updated. If the transaction identifier is not new and the record is an end-of-transaction record, a transaction type list entry is created or updated. When all log file records are read, analytical comparison between the information associated with two or more of the log files in data fields in the transaction type list entries is performed.

Comparing database performance without benchmark workloads

Identifying matching transactions. First and second log files contain operation records of transactions in a transaction workload, each file recording a respective execution of the transaction workload, the method comprising. A first record location in the first file and an associated window of a defined number of sequential second record locations in the second file are advanced one record location at a time. Whether each operation record of a complete transaction at a first record location has a matching operation record at one of the record locations in the associated window of second record locations is determined. If so, the complete transaction in the first file and the transaction that includes the matching operation records in the second file are identified as matching transactions.

Matching transactions in multi-level records

A predefined number of matches is identified between records in a first file and records in a second file. For the matches, determine the span of the actual range of record positions in the second file relative to the positions of the operation records in the first file within which all matches were found. If the actual span is smaller than the span of a current defined range of record positions by at least a first threshold value, decrease the span of the current defined range. If the actual span is within a second threshold value of the span of the current defined range, increase the span of the current defined range. If an amount above a third threshold value of operation records in the first file are not matched to operation records in the second file, increasing the span of the current defined range.

Match window size for matching multi-level transactions between log files

A log file contains operation records, each operation record is of a certain type, and each operation record is associated with a transaction. A plurality of operation records is read from the log file into a record store. Records of the plurality of operation records of each operation record type are sampled at a predefined sampling rate. Operation records in the plurality of operations records are identified that are associated with completed transactions of which the sampled operation records are associated. The identified operation records are then extracted from the record store into a data store.

Sampling transactions from multi-level log file records

A method for identifying matching transactions between two log files where each transaction includes one or more statements. Each log file record records the execution of a statement and includes a transaction identifier. Each record in turn in one log file is compared to an advancing window of records in the other log file. A first table contains associations of statements to transactions and transactions to statements for records in the window. If a match is found between a record in the one file and a record in the window, information associating partial transactions in the one file to potential transactions of the records in the window is added to a second table. If an end-of-transaction record is read from the one file, a best match is found between the ended transaction and the potential transactions based on information in the first and second tables.

Growing community of inventors

Bengaluru, India

Amitava Kundu