International Business Machines Corporation

Soma Shekar Naganna

Abhishek Seth

Neeraj Ramkrishna Singh

Bhavani Kumar Eshwar

Scott Schumacher

James Albert O'Neill, Jr

Sarbajit Kumar Rakshit

Subramanian Palaniappan

Shettigar Parkala Srinivas

Somnath Asati

Vishal Tomar

Martin Oberhofer

Geetha Sravanthi Pulipaty

Shashidhar R Yellareddy

Sameep Mehta

Umasuthan Ramakrishnan

Shweta Shandilya

Balaji Ganesan

Alexander T Bentley

Pushpalatha M Hiremath

Devbrat Sharma

Matheen Ahmed Pasha

Avirup Saha

Muhammed Abdul Majeed Ameen

Mohammad Khatibi

Jason Allen Woods

Prabhakaran Ramalingam

Rin Davis

Pankuj Chachra

Joseph Xaviour

Mahendra Singh Kanyal

Subramanian B Manjunath

Kalapriya Kannan

Berthold Reinwald

Trent Gray-Donald

Arvind Agarwal

Lars Bremer

Sumit Bhatia

Hima Patel

Willie L Scott, Ii

Thomas Andrew Brunet

Chitra A Iyer

Mariya Chkalova

Arvind S Shetty

Mark Wang

Balakumaran Vaithyalingam

Amit Malla

Rishabh Saraf

Mohit Singh Chouhan

Sergio Luis Olvera Gutierrez

Shivakumar Lakshminarayana

Randy Hu

Venkata JayaPrakash Jinka

One embodiment provides a computer implemented method, including: obtaining an information document corresponding to an entity, wherein the information document includes redacted information spans; identifying an entity type for each of the redacted information spans, wherein the entity type identifies a relationship between a redacted information span and at least one other entity within the information document; replacing the redacted information spans with replacement entities corresponding to the entity type of a given redacted information span, wherein the replacing is performed in view of a frequency distribution of actual information and wherein the replacing includes maintaining relationships of the redacted information spans; and controlling bias within the replacement entities, wherein the controlling includes detecting bias within the replacement entities.

Generation of training data from redacted information

Mechanisms are provided for dynamic re-resolution of entities in a knowledge graph (KG) based on streaming updates. The KG and corresponding initial clusters associated with first entities are received along with a dynamic data stream having second documents referencing second entities. Clustering on the second documents based on the set of initial clusters, and document features of the second documents, is performed to provide a set of second document clusters. For second document clusters that should be modified based on entities associated with the second document cluster, a cluster modification operation is performed. Updated clusters are generated based on the clustering and modification of clusters. Entity re-resolution is dynamically performed on the entities in the KG based on the second entities associated with the updated clusters to generate an updated knowledge graph data structure.

Distributed entity re-resolution based on streaming updates

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

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

Techniques are described relating to coordinating asynchronous communication among a plurality of client microservices in a managed services domain of a cloud computing environment. An associated computer-implemented method includes receiving at a single request topic queue of a message broker application programming interface (API) at least one message associated with a topic from at least one publisher microservice among the plurality of client microservices. The method further includes identifying an authorization identification parameter included in each of the at least one message. The method further includes publishing each of the at least one message to a respective bucket within a single response topic queue of the message broker API, the respective bucket corresponding to one of at least one subscriber microservice among the plurality of client microservices associated with the authorization identification parameter included in the message.

Coordinating asynchronous communication among microservices

A method, system, and computer program product for workflow-based dynamic screen sharing in a multi-screen presentation environment are provided. The method identifies a presentation schema for a multi-screen presentation. A set of presenters are identified for the multi-screen presentation. The method determines a presentation rate of the multi-screen presentation during the multi-screen presentation. A workflow visualization is generated for the multi-screen presentation based on the presentation schema and the presentation rate. The method dynamically transitions between presenters of the set of presenters during the multi-screen presentation based on the presentation schema, the presentation rate, and the workflow visualization.

Workflow-based screen sharing during live presentation

A method, a computer system, and a computer program product for data validation in a microservice environment is provided. Embodiments of the present invention may include receiving a request based on an application configuration validation. Embodiments of the present invention may include determining a schema is not defined based on the request. Embodiments of the present invention may include generating the schema using machine learning. Embodiments of the present invention may include using the generated schema for a plurality of validations.

Governance based validation framework for JSON data using machine learning

Growing community of inventors

Bengaluru, India

Soma Shekar Naganna