International Business Machines Corporation

Yahoo! Inc.

Abhishek Seth

Soma Shekar Naganna

Neeraj Ramkrishna Singh

James Albert O'Neill, Jr

Geetha Sravanthi Pulipaty

Somnath Asati

Vishal Tomar

Sarbajit Kumar Rakshit

Shashidhar R Yellareddy

Devbrat Sharma

Martin Oberhofer

Matheen Ahmed Pasha

Muhammed Abdul Majeed Ameen

Scott Schumacher

Bhavani Kumar Eshwar

Mohammad Khatibi

Subramanian Palaniappan

Shettigar Parkala Srinivas

Pushpalatha M Hiremath

Mahendra Singh Kanyal

Sameep Mehta

Sudheesh S Kairali

Arvind Agarwal

Trent Gray-Donald

Hariharan Krishna

Lars Bremer

Shajeer K Mohammed

Balaji Ganesan

Avirup Saha

Mariya Chkalova

Prabhakaran Ramalingam

Arvind S Shetty

Balakumaran Vaithyalingam

Alexandre Luz Xavier Da Costa

Rishabh Saraf

Mohit Singh Chouhan

Sergio Luis Olvera Gutierrez

Randy Hu

Venkata JayaPrakash Jinka

Mahendra Kumar

Abhinav Gupta

A system for searching and/or linking corresponding electronic data records includes: choosing, for a target electronic data record, a group of candidate electronic data record(s) for comparison; selecting a first set of one or more attributes for comparison between the target record and the group of candidate record(s), and choosing a threshold for the comparisons. The system further includes comparing attribute(s) of the target electronic data record to attribute(s) of the group of candidate records; and scoring the comparison of the attribute(s) of the target electronic data record to the attribute(s) of the candidate records. The system further can include configuring a sieve to apply to electronic records, wherein the sieve has a second set of attribute(s) and a corresponding threshold for each of the second set of attribute(s), and applying the sieve to one or more electronic data records to identify records.

Dynamic electronic data record resolution in electronic data environments

One or more trained embedding generation artificial intelligence models are executed to generate a plurality of record attribute embeddings. The plurality of record attribute embeddings represents a plurality of attributes of data of a plurality of records. Grouping of the plurality of record attribute embeddings is performed. The grouping of a record attribute embedding includes grouping attribute values of the record attribute embedding into one or more groups of attribute values. The performing grouping provides a plurality of groups of attribute values for the plurality of record attribute embeddings. Selected records are compared to provide a set of matched records. The comparing, based on a group of attribute values, includes comparing records that include one or more attribute values grouped in the group of attribute values providing a subset of matched records of the set of matched records. The set of matched records is stored in an accessible computer location.

Records processing based on record attribute embeddings

Disclosed are techniques for recovering power from remote vehicles near a landing surface. Air turbulence from a remote vehicle near a landing surface is detected and measured. When the air turbulence exceeds a threshold, a subset of landing surface components corresponding to sections of the landing surface with attached wind power generators are determined for wind power generation from the detected air turbulence. The wind power generators of the subset of landing surface components are exposed from their protective coverings, and the subset of landing surface components are reoriented towards the source of the air turbulence, tracking the remote vehicle generating the air turbulence. Power generated by the wind power generators can be stored and used to recharge remote vehicles that land at the landing surface.

Recovering wind power from remote vehicles

A computer-implemented method may include a generating a semantic label based on a frequency of similar values in a data field; and profiling a data source; identifying, based on the profiling, critical data elements (CDEs) and non-CDEs within the data source; auto-persisting the CDEs; matching the CDEs together under the semantic label; establishing virtual objects for the non-CDEs; and joining the CDEs and the virtual objects.

Semantic matching model for data de-duplication or master data management

Described are techniques for disintegrating an entity into smaller entities. A graph ('first graph') for the entity of records to be disintegrated is constructed, where each vertex of the first graph represents a record in the entity of records to be disintegrated. The edges in the first graph connecting records in the entity of records represent matching links between the records, where each edge is associated with a weight corresponding to a similarity score. Furthermore, two or more additional graphs representing two or more sub-entities of the entity of records to be disintegrated are constructed. Such graphs are constructed based on selecting edges with a maximum weight out of the edges connected between each pair of records in the first graph or based on the number of connections each record has with other records in the first graph exceeding a threshold value.

Disintegrating an entity of records into smaller entities

An approach for accessing multi-attribute data records of a master data management system. The method comprises: enhancing the master data management system with one or more search engines for enabling data record access. A request of data may be received at the master data management system. A set of one or more of the multiple attributes, referenced in the received request, may be identified. A combination of one or more of the search engines of the master data management system, whose performances for searching values of at least part of the set of attributes fulfil a current selection rule may be selected. And, the request may be processed using the combination of search engines. At least part of the results of the processing may be provided, and the selection rule may be updated based on user operations on the provided results, the updated selection rule becoming the current selection rule.

Method for accessing data records of a master data management system

A method, a structure, and a computer system for mapping data fields. The exemplary embodiments may include, based on determining that a first data set and a second data set contain homogenous data, mapping at least one column of the first data set to at least one column of the second data set based on comparing at least one of relative column position and unique value sets. Based on determining that the first data set and the second data set contain heterogeneous data, the exemplary embodiments may include mapping the at least one column of the first data set to the at least one column of the second data set based on a difference between distribution signatures of unique value sets within each of the first data set and the second data set being less than a threshold.

Mapping of heterogeneous data as matching fields

Records can be matched by a graph neural network model performing entity resolution on the records, and representing each record as a respective node in a graph. Record matching explanations can be generated, each record matching explanation indicating a first set of attributes, and a first set of corresponding values, used for the matching at least two of the records. Nodes can be clustered into a plurality of clusters by aggregating the record matching explanations and, based on the record matching explanations, determining which of the records have high importance values, in the first set of values, that match. At least one cluster explanation can be generated, the cluster explanation indicating a second set of attributes, and a second set of values corresponding to the second set of attributes, used for the clustering the nodes. The record matching explanation and the cluster explanation can be output.

Entity explanation in data management

A computer-implemented method increases the efficiency of matching records from two sources. The method includes identifying a first source and a second source wherein each of the sources include one or more records and each record includes one or more attributes. The method further includes determining, based on a corpus, the one or more attributes and generating, based on the attributes, a set of feature vectors which vectors represent the one or more attributes. The method includes comparing each record in the first source against each record in the second source. The method further includes generating, in response to the comparing, a link confidence. The method also includes linking, in response to the link confidence being above a linking threshold, the associated records. The method includes determining a first feature vector of the set of feature vectors used in the linking, and outputting a set of results.

Feature vector generation for probabalistic matching

A method, apparatus, computer system, and computer program product for managing information. A set of bucket hashes and comparison information for a data record are identified by a computer system. The set of bucket hashes is generated from the comparison information, wherein the set of bucket hashes and the comparison information form a metadata record. A number of candidate metadata records in a metadata database is identified by the computer system using the set of bucket hashes, wherein the number of candidate metadata records comprises a set of candidate bucket hashes and candidate comparison information. An entity membership is identified by the computer system for the data record from a comparison of the comparison information in the metadata record with the candidate comparison information in the number of candidate metadata records.

Metadata indexing for information management using both data records and associated metadata records

Computer system for performing biometric matching in a way that balances accuracy level required in the biometric matching against computing resources (for example, processor cycles) that will be needed to match authentication requesters with profiles of authorized users. In some embodiments, this is achieved by controlling the number of clusters and/or the number of clusters to be searched pursuant to an authentication request.

Automatic configuration switching in biometric matching

A voice command can be received from a user. One or more voice command devices (VCDs) that the voice command is targeting can be determined. A visual indicator of each of the one or more targeted VCDs can be displayed on an XR device worn by the user, wherein each visual indicator visually indicates a respective targeted VCD the voice command is directed to on the XR device.

Extended reality based voice command device management

An apparatus for a wearable device with expandable display strips, the apparatus includes a first layer with a first display panel coupled to a second layer with a second display panel, where the first display panel can extend and retract over a top surface of the second display panel. The apparatus also includes a first gear and rack mechanism coupled to the top surface of the second display panel and a bottom surface of the first display panel, where the first gear and rack mechanism is configured to extend and retrack the second layer with the second display relative to the first layer with the first display. Furthermore, the first layer and the second layer are arranged in a concentric manner.

Wearable device with expandable display strips

A method for recording a plurality of augmented reality (AR) sessions between a set of user(s) and an AR computer system, receiving first user input, through the AR computer system and from a first user, identifying a first AR session of the plurality of AR sessions, and presenting at least a portion of the recording of the first AR session on the AR computer system for the first user.

Chatbot with augmented reality based voice command navigation

Methods, computer program products and/or systems are provided that perform the following operations: obtaining payload attribute fields; determining potential matching fields from the payload attribute fields; determining a matching function for each of the potential matching fields; determining an attribute score for each of the potential matching fields based on the matching function; obtaining a score list for a reference data set; determining a correlation of the attribute score for each of the potential matching fields with the reference data set score list; selecting new matching fields from the potential matching fields based at least in part on the correlation; determining an optimal weight for each of the selected new matching fields; selecting attribute fields for matching from the selected new matching fields based on a threshold rate for false positives and false negatives; and providing the attribute fields for matching and the associated optimal weight for the attribute fields.

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Uttar Pradesh, India

Abhishek Seth