Abstract: An example operation may include one or more receiving an entry at a blockchain-as-a-service (Baas) provider, determining whether the entry satisfies a first set of policies, and controlling placement of the entry into a first queue when the first set of policies is satisfied and into a second queue when the first set of policies is not satisfied, wherein the first queue is to store confirmed entries to be submitted for consensus without validation and the second queue is to store pending entries that require validation before consensus.

Abstract: A method includes training a machine learning model using a current set of labeled data points. Each of the data points is multiple data records. A label of a data point indicates a classification of the data point. The training results in a trained machine learning model configured to classify a data point as representing a same entity or different entities. The method includes selecting a subset of unlabeled data points from a current set of unlabeled data points using classification results of the current set of unlabeled data points. The method includes providing the subset of unlabeled data points to a classifier and in response to providing receiving labels of the subset of unlabeled data points. The method may be repeated using the subset of labeled data points in addition to the current set of labeled data points as the current set of labeled data points.

Abstract: An example operation may include one or more of hosting a first virtual node in a blockchain-as-a-service (Baas) provider, hosting a second virtual node in the Baas provider, and controlling transmission of information between the first virtual node and the second virtual node along an internal signal path of the Baas provider, wherein the information corresponds to a block in a blockchain that includes an entry for the first and second virtual nodes.

Abstract: A method for generating data standardization rules includes receiving a training data set containing tokenized and tagged data values. A set of machine mining models is built using different learning algorithms for identifying tags and tag patterns using the training set. For each data value in a further data set: a tokenization is applied on the data value, resulting in a set of tokens. For each token of the set of tokens one or more tag candidates are determined using a lookup dictionary of tags and tokens and/or at least part of the set of machine mining models, resulting for each token of the set of tokens in a list of possible tags. Unique combinations of the sets of tags of the further data set having highest aggregated confidence values are provided for use as standardization rules.

Abstract: A method matches information. A first center node in a first subgraph and a second center node in a second subgraph are identified. Groups of neighboring nodes having the neighboring nodes from both of subgraphs are identified. A group of the neighboring nodes in the groups has the neighboring nodes with a same node type. A best matching node pair of the neighboring nodes in each cluster is identified. The neighboring nodes in each best matching node pair comprise a first node from the first subgraph and a second node from the second subgraph. Whether the center nodes match is determined based on an overall distance between the center nodes using the first and second center node and the best matching node pair pairs.

Abstract: The invention relates to a computer-implemented method for classifying a set of data values. For each of the data values of the set of data values, a set of one or more terms associated with the respective data value is determined using one or more first knowledge bases. A set of common terms is determined. The set of common terms comprises terms present in more than one of the sets of terms. For each of the common terms, a number of hits for a lookup query against one or more second knowledge data bases is determined. One or more common terms of the set of common terms with the smallest number of hits are determined and a result is returned. The result comprises the one or more common terms with the smallest number of hits as one or more candidate classes for classifying the set of data values.

Abstract: A computer-implemented method includes determining, by a computer device, composite view rules for combining first data from a first data record and second data from a second data record to create a composite view of an entity in a master data management system; receiving, by the computer device, the first data; receiving, by the computer device, the second data; creating, by the computer device, the composite view from the first data and the second data based on the composite view rules; physically materializing the composite view on a storage device; preserving, by the computer device, the first data record; and preserving, by the computer device, the second data record.

Abstract: The present disclosure relates to a method, computer program product and system. The method may comprise providing a first graph being an instance of a first ontology. Sample values of a plurality of concept attributes may be collected from the first graph. The sample values may be clustered into one or more clusters based on content and/or format of the sample values. A cluster of the clusters that contains sample values representing different concept attributes may be identified. An additional concept and associated set of relations representing the concept attribute values of the cluster may be determined and the first ontology may be updated using the additional concept and associated set of relations.

Abstract: A computer implemented method for classifying at least one source dataset of a computer system. The method may include providing a plurality of associated reference tables organized and associated in accordance with a reference storage model in the computer system. The method may also include calculating, by a data classifier application of the computer system, a first similarity score between the source dataset and a first reference table of the reference tables based on common attributes in the source dataset and a join of the first reference table with at least one further reference table of the reference tables having a relationship with the first reference table. The method may further include classifying, by the data classifier application, the source dataset by determining using at least the calculated first similarity score whether the source dataset is organized as the first reference table in accordance to the reference storage model.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for processing suspect duplicate records in a master data management system. A master data management module identifies two or more suspect duplicate records in the master data management system based on scores. A matching engine classifies the two or more suspect duplicate records, by comparing the scores against threshold values, into one of: a match, a non-match, and a possible match. The master data management module re-classifies the suspect duplicate records and adjusting the threshold values of the matching engine for classification of future records, in response to receiving, by a data stewardship client, a user input indicating an incorrect classification of the suspect duplicate records.

Abstract: Methods, computer program products and/or systems are provided that perform the following operations: obtaining a first graph comprising first nodes representing first entities and first edges representing relationships between first entities, the first nodes being associated with first entity attributes descriptive of the first entities represented by the first nodes, the first edges being associated with first edge attributes descriptive of the relationships represented by the first edges; determining a first subgraph for a certain node of the first nodes of the first graph, the first subgraph including the certain node and at least one neighboring node of the certain node; and determining a data quality issue regarding the certain node based, at least in part, on applying one or more applicable rules of a set of data quality rules to first entity attribute values and first edge attribute values of the first subgraph.

Abstract: In an approach to improving machine learning model training for data matching from manual decisions, one or more computer processors detect a correction made to two data records. One or more computer processors determine a common attribute between the two data records. One or more computer processors identify a first machine learning model associated with the common attribute. One or more computer processors add comparison data of the two data records to training data for the machine learning model, where the comparison data includes the correction.

Abstract: Selecting entity matching systems sample record pairs by selecting at least one first record pair from entity matching system data records, scoring attribute compare methods of the at least one first record pair according to the entity matching system, adding the at least one first record pair to a no-match set according to the attribute matching score, selecting at least one second record pair from an entity matching system data record bucket, scoring attribute compare methods of the at least one second record pair according to the entity matching system, adding the at least one second record pair to record pair set, according to the second record pair attribute compare method score, and providing the record pair set to a user.

Abstract: A computer-implemented method includes determining, by a computer device, composite view rules for combining first data from a first data record and second data from a second data record to create a composite view of an entity in a master data management system; receiving, by the computer device, the first data; receiving, by the computer device, the second data; creating, by the computer device, the composite view from the first data and the second data based on the composite view rules; physically materializing the composite view on a storage device; preserving, by the computer device, the first data record; and preserving, by the computer device, the second data record.

Abstract: A computer implemented method for classifying at least one source dataset of a computer system. The method may include providing a plurality of associated reference tables organized and associated in accordance with a reference storage model in the computer system. The method may also include calculating, by a data classifier application of the computer system, a first similarity score between the source dataset and a first reference table of the reference tables based on common attributes in the source dataset and a join of the first reference table with at least one further reference table of the reference tables having a relationship with the first reference table. The method may further include classifying, by the data classifier application, the source dataset by determining using at least the calculated first similarity score whether the source dataset is organized as the first reference table in accordance to the reference storage model.

Abstract: An example operation may include one or more of determining data at a first node satisfies a condition, obtaining a pointer to a shared storage area for the data, controlling generation of a block including the pointer, and appending the block to a blockchain without the data, wherein the first node corresponds to first virtual node hosted by blockchain-as-a-service (Baas) provider and wherein the first virtual node receives the data from a network coupled to the Baas provider.

Abstract: A method for presenting search query results is provided. The method may include detecting an occurrence of the trigger event. The method may include determining a category of information based on data associated with the trigger event. The method may include identifying at least one constraint based on the determined category of information. The method may include appending to the identified at least one constraint to the determined category of information. The method may include generating at least one search query. The method may include selecting at least one candidate website based on the category of information. The method may include performing the at least one search query on the at least one candidate website. The method may include filtering each search query result within the search query results. The method may include sending each filtered search query result within the search query results to a user.

Abstract: The present disclosure relates to a method for a secure storage, matching and linking of data records. The method comprises: receiving a current data record having one or more attributes, each attribute having an attribute value. For each attribute of at least part of the attributes a predefined set of variations of the attribute value of the attribute may be generated. The received attribute values may be encrypted resulting in an encrypted record and the generated sets of variations may be encrypted. The encrypted record may be stores in a storage system in association with the respective encrypted sets of variations.

Abstract: The present disclosure relates to a method comprising providing a set of one or more records, each record of the set of records having a set of one or more attributes. Values of the set of attributes of the set of records may be input to a trained data representation learning model for receiving, as an output of the trained data representation model, a set of feature vectors representing the set of records respectively. The set of feature vectors may be stored.

Abstract: Cross-matching of match indexes assigned to respective data sources can be performed. Each match index can store at least a portion of data from a subset of data records in the data source to which the match index is assigned. Such data can be stored in the match index as match index records. Responsive to identifying matches between match records of different match indexes, the data records in the data sources to which the match records correspond can be updated to add links between the respective data records.