International Business Machines Corporation

Shettigar Parkala Srinivas

Soma Shekar Naganna

Neeraj Ramkrishna Singh

Prabhakaran Ramalingam

Sameep Mehta

Scott Schumacher

Abhishek Seth

Balaji Ganesan

Chitra A Iyer

Kalapriya Kannan

Berthold Reinwald

Sumit Bhatia

Hima Patel

Subramanian Palaniappan

Geetha Sravanthi Pulipaty

Puneet Sharma

Lawrence Dubov

James B Cushman, Ii

Avirup Saha

Matheen Ahmed Pasha

Erik Hume Jones

Upwan Chachra

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

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

An embodiment establishes a designated attribute value as a semantic criterion for grouping records in a bucket, identifies a first set of records having attribute values that satisfy the semantic criterion, and adds the first set of records to the bucket. The embodiment detects that the first set of records represent a first series of events that occurred in succession at respective times. The embodiment derives a temporal attribute value representative of a time pattern formed by the times of the first series of events and designates the temporal attribute value as a temporal criterion for grouping records in the bucket. The embodiment identifies a second set of records that represent a second series of events and satisfy the temporal criterion and adds the second set of records to the bucket based at least in part on the second set of records satisfying the temporal criterion.

Bucketing records using temporal point processes

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.

Auto detection of matching fields in entity resolution systems

A first score associated with matching between entity records of a plurality of entities of master data of an MDM system is received. A set of entity records with a first score above a lower threshold score and below an upper threshold score is identified as unresolved; neither confirmed as matched or unmatched. A second score associated with relationships between entity records is generated. Overall scores for pairs of the set of entity records are determined by combining the first matching score with the second relationship score. The overall score of respective pairs of the set of entities is compared to the upper threshold, and if the upper threshold is exceeded, then the information of the pair of entity records of the set of entity records are combined into a single record, and redundant entity records are removed from the MDM system.

Entity resolution of master data using qualified relationship score

Indexing and matching records in a data management system by defining entity indexing attributes associated with system records, receiving an incoming data entity, selecting a set of entity candidates according to the entity indexing attributes, matching the incoming entity to an entity candidate, generating an analysis of the entity candidate selection according to entity attribute effectiveness, and revising the entity indexing attributes according to the analysis.

Defining indexing fields for matching data entities

Candidate data record prioritization for match processing includes identifying candidate records for use in match processing to determine records that match to an incoming record. The candidates are grouped into buckets according to bucket roles, with each bucket correlating to a bucket role, and with each bucket role being defined by a unique record attribute set. The method obtains an effectiveness score for each of the bucket roles. The scores are measures of effectiveness of the bucket roles in identifying candidates that match to incoming data records. The method establishes an order of priority in which to process the candidates by prioritizing the buckets into an order based on the effectiveness scores for the bucket roles. The process then commences match processing to process the candidates in the established order of priority where the match processing processes candidates of a higher priority bucket before processing candidates of lower priority buckets.

Candidate data record prioritization for match processing

A method, apparatus, and computer program product for processing data is disclosed. In one aspect of the invention, a computer implemented method comprises receiving, on one or more computers, a record having one or more attributes from a source system. The method also comprises measuring, on one or more computers, a data accuracy of the received record as compared to a golden record stored in a master data management system. One or more remediation actions are performed, on one or more computers, to enforce improvements to the measured data accuracy of the received record in the source system. In one or more embodiments, measurement of data accuracy comprises calculating, on one or more computers, an accuracy score defined by a closeness of the received record to the golden record.

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Bengaluru, India

Shettigar Parkala Srinivas