International Business Machines Corporation

Martin Oberhofer

Ivan Matthew Milman

Sushain Pandit

Yannick Saillet

Jens Peter Seifert

Sebastian Nelke

Brian Patrick Byrne

Geetika Tewari Lakshmanan

Dan Jeffrey Mandelstein

Ram S Narayanan

Carl Michael Marrelli

Albert Maier

Sol Rashidi

Charles Daniel Wolfson

Lars Bremer

Donald Andrew Padilla

Khirallah Birkler

Boris Feist

Torsten Wilms

Robert Kern

Dmitriy Fot

Hemanth Kumar Babu

Soma Shekar Naganna

Scott Schumacher

Mike W Grasselt

Thuany Karoline Stuart

Su Liu

Fang Wang

Thomas Schwarz

Andreas Uhl

Andreas Trinks

Adapala S Reddy

Dinesh C Verma

Namik Hrle

Donna N Dillenberger

Abhishek Seth

Patrick Dantressangle

Michael Wurst

Jonathan Limburn

Calvin Stacy Powers

Jason Allen Woods

Thomas Cherel

Basem Elasioty

Florian Mentzel

Hien Pham The

Thishanth Thevarajah

Karin Steckler

Eberhard Hechler

Michail Tausch

Lisa Seacat DeLuca

Daniel Pittner

Wayne Francis Tackabury

Neeraj Ramkrishna Singh

Philipp Hoffmann

Stephen Rees

David Lebutsch

Geetha Sravanthi Pulipaty

Olena Woolf

Mariya Chkalova

Kostas Rakopoulos

Jonathan Roesner

Michael B Litherland

Kristen E Cochrane

Claudio Andrea Fanconi

Joerg Rehr

Mehmet Uenluetepe

Rin Davis

Pankuj Chachra

Jeffrey T Matteo

Trudy L Hewitt

Mark Patrick Delaney

Robert Huntington Grant

Knut Stolze

Namit Kabra

Michael Baessler

Eric B Libow

Torsten Steinbach

Oliver Suhre

Mohammad Khatibi

Dennis Butterstein

Michael Cobbett

Markus Lorch

Alexander Neef

James Albert O'Neill, Jr

Daniel C Wolfson

Sergej Schuetz

Tobias Kevin Fränzel

Alexandre Luz Xavier Da Costa

Holger Koenig

Mark Wang

Manfred Vodegel

Ingo Hotz

Mathias Rueck

Utkarsh Bajpai

Steven Lockwood

Ruolin Wu

Benjamin Fabian Hogl

Sergio Luis Olvera Gutierrez

Nicholas Kanellos

A method, computer system, and a computer program product for storing a graph is provided. The present invention may include providing a key-value store. The present invention may include storing a graph in the key-value store, wherein storing a specific node of the graph in the key-value store comprises: storing, as a key, identifiers of the specific node and of k-hop neighborhood of the specific node, and as value of the key, attribute values of the specific node, wherein the k-hop neighborhood of the specific node comprises a set of neighboring nodes that are reachable from the specific node within k edges.

Storing graph data in a key-value store

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.

Method and system for processing data records

A computer-implemented method for managing data records subject to placement condition rules in a distributed data management system comprising a plurality of nodes. The method may include receiving, by an endpoint management component of the distributed data management system, a request for a data record. The method may also include routing, by the endpoint management component of the distributed data management system, the request to a node of the distributed data management system. The method may also include upon determining that the request is a create request, verifying, by a service component of the distributed data management system, a placement condition rule for the data record, where the placement condition rule is retrieved from a configuration component of the distributed data management system.

Multi-partitioned global data system

Duplicating identification in a graph having a second version. The graph includes nodes representing entities and edges representing relationships between the entities. The method includes: providing a previous first version of the graph. At least two target nodes of the graph may be identified. The first version and the second version of the graph may be compared for determining a comparison score indicative of a similarity between the two target nodes. The comparison score may be used for determining whether the two target nodes are duplicates with respect to each other.

Duplicate determination in a graph using different versions of the graph

A method, system, and computer program product for identifying reference data values in a source data set. The method may include inputting a block of attribute values to a predefined machine learning model. The method may also include receiving an indication of a presentation layout of the block of the attribute values and an associated reference data extraction method. The method may also include determining a reading direction of the block of values. The method may also include identifying one or more inspection areas in the reading direction of the block of values. The method may also include determining sets of the one or more inspection areas that share a common presentation feature. The method may also include identifying tokens in an inspection area. The method may also include determining if the inspection area includes reference data values. The method may also include outputting the reference data values.

Identifying reference data in a source data set

The present disclosure relates to a method for a weighting graph comprising nodes representing entities and edges representing relationships between entities in accordance with one or more domains. The method comprises: pre-processing the graph comprising assigning weights to the nodes and/or the edges of the graph in accordance with a specific domain of the domains, wherein the weight indicates a domain specific data quality problem of attribute values representing an edge of the edges and/or an entity involved in that edge. The weighted graph may be provided for enabling a processing of the graph in accordance with the specific domain.

Method for weighting a graph

According to at least one embodiment, a method, computer system, and computer program product for model-driven extraction of event data by a computational device is provided. The present invention may include transforming event data of an event occurring on a blockchain network using an event schema to comply with a data model of an external data structure; mapping, based on the event schema and the data model, the event data to a logical structure of the external data structure; and loading the transformed event data to the external data structure to modify a data content of the external data structure based on the mapping.

Extracting data from a blockchain network

A computer-implemented method for generating a speeding ticket. The method includes generating a first image of at least a part of a vehicle by a first camera, detecting a first set of the characters of the number plate of the vehicle dependent on the first image, and generating a secured file by a first camera using the first set of the characters and a first character code. The method further includes generating a second image of the part of the vehicle by a second camera and detecting a second set of the characters of the number plate dependent on the second image. The method further includes generating the speeding ticket, in response to determining that the vehicle violates a speed limit and in response to verifying that the first set of the characters is equal to the second set of the characters.

Generating a speeding ticket using a persistently stored character code in a camera for masking information about characters of a number plate of a vehicle

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.

Composite views in a master data management system

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.

Method for classifying an unmanaged dataset

A method for relating different types of records. The method may include providing comparison functions, wherein each comparison function corresponds to a semantical class, and wherein a computational cost is associated with each comparison function. The method may include determining one or more attribute pairs between the different types of records. The method may include sorting the comparison functions according to a determined accuracy. The method may include selecting a set of comparison functions associated with semantical classes according to a predefined rule. The method may include determining a total computational cost based on the computational cost of the selected set of comparison functions. The method may include determining whether two or more records are related using the selected set of comparison functions. The method may include relating the two or more records. The method may include determining a rate of false negative records.

Auto-tuning of comparison functions

A computer implemented method comprising processing the unstructured objects of each record of records of a database for identifying a set of one or more values of attributes in the unstructured objects of the each record. The sets of unstructured attribute values of two records of the database may be compared for determining a similarity level between the two sets. It may be determined whether the two records are representing a same entity based on the comparison result.

Record matching in a database system

The present disclosure relates to a method for enabling data integration. The method comprises collecting matching results of matching of records by a matching component over a time window. The number of false tasks of user defined tasks and system defined tasks in the collected matching results may be determined. The matching criterion used by the matching component may be adjusted to minimize the number of user defined tasks while the fraction of false tasks stays within a certain limit. The matching criterion may be replaced by the adjusted matching criterion for further usage of the matching component.

Method for configuring a matching component

Cognitive pre-loading of referenced content in electronic mail (e-mail) messages includes determining, using computer hardware, metadata for an electronic message directed to a user, detecting, using the computer hardware, a universal resource locator (URL) within a body portion of the electronic message, determining, using the computer hardware, a content type for the URL, and performing, using the computer hardware, natural language processing on the electronic message to determine an action importance corresponding to the URL. At least a portion of content specified by the URL can be pre-fetched from a data processing system based on the metadata, the content type of the URL, and the action importance. The electronic message and at least a portion of the content can be provided to a client device of the user.

Cognitive pre-loading of referenced content in electronic messages

Embodiments of the present invention describe creating a mesh network of autonomous vehicles. Embodiments describe, for a set of autonomous vehicles configured to perform joint tasks, creating a plurality of subset networks of autonomous vehicles based on geographical proximity, wherein the plurality of subset networks of autonomous vehicles overlap to form a mesh network of autonomous vehicles, and wherein the mesh network of autonomous vehicles and the subset networks of autonomous vehicles support a non-centralized task assignment feature to assign tasks to autonomous vehicles in the mesh network. Additionally, embodiments describe sending a status signal to a first autonomous vehicle in the mesh network via a second autonomous vehicle in the mesh network, wherein the first autonomous vehicle is in a same subset network of autonomous vehicles as the second autonomous vehicle.

Dynamic autonomous vehicle mutuality support for autonomous vehicle networks

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Bondorf, Germany

Martin Oberhofer