International Business Machines Corporation

Sameep Mehta

Manish Kesarwani

Manish Anand Bhide

Kuntal Dey

Akshar Kaul

Vinayaka D Pandit

Krishnasuri Narayanam

Kalapriya Kannan

Himanshu Gupta

Pranay Kumar Lohia

Seema Nagar

Gagandeep Singh

Sreyash Kenkre

Nishtha Madaan

Rajmohan Chandrahasan

Nitin Kumar Gupta

Diptikalyan Saha

Samiulla Zakir Hussain Shaikh

Balaji Ganesan

Soma Shekar Naganna

L Venkata Subramaniam

Gyana Ranjan Parija

Arvind Agarwal

Shashank Mujumdar

Mukesh Kumar Mohania

Srikanth Govindaraj Tamilselvam

Vijay Arya

Hima Patel

Sriram Lakshminarasimhan

Avirup Saha

Hong Min

Sachindra Joshi

Akshat Verma

Stefano Braghin

Kush Raj Varshney

Berthold Reinwald

Ritesh Kumar Gupta

Anindya Neogi

Sumit Negi

Rema Ananthanarayanan

Spyridon Antonatos

Sumit Bhatia

Laurent Sebastien Mignet

Atul S Kumar

Salil Joshi

Deepak Vijaykeerthy

Prasad G Naldurg

Shettigar Parkala Srinivas

Rakesh Rameshrao Pimplikar

Sandeep Hans

Srikanta Bedathur Jagannath

Soujanya Soni

Anton Zorin

Ramya Raghavendra

Malolan Chetlur

Amith Singhee

Ramasuri Narayanam

Abhishek Seth

Mohit Jain

Karthik Sankaranarayanan

James Willis Pickel

Paul Sherwood Taylor

John Thomas Richards

Raghuram Krishnapuram

Aleksandra Mojsilovic

Darrell Christopher Reimer

Rachel K E Bellamy

Senthil Kumar Kumarasamy Mani

Dinesh Garg

Tanveer Afzal Faruquie

Matthew R Arnold

Neeraj Ramkrishna Singh

Kaoutar El Maghraoui

Hima Prasad Karanam

Girish Bhimrao Chafle

Deepak S Padmanabhan

Andrzej Jan Wrobel

Michael James Hind

Om Dadaji Deshmukh

Venkata Nagaraju Pavuluri

Aniya Aggarwal

Anush Sankaran

Inkit Padhi

Stephanie Houde

Stuart A Siegel

Naveen Panwar

Narendran Sachindran

Abhishek Shah

David John Piorkowski

Animesh Nandi

Vaibhav S Dantale

Sumit Neelam

Suranjana Samanta

Jason Tsay

Ankush Gupta

Prashant Jain

Sai Koti Reddy Danda

Devbrat Sharma

Ananya Aniruddha Poddar

Tanveer A Faruquie

Methods, systems, and computer program products for prioritized data cleaning are provided herein. A computer-implemented method includes obtaining a dataset comprising a plurality of data issues; determining a priority of one or more features of the dataset; generating a respective model for each of a plurality of data resolution algorithms, wherein each model indicates computing costs of the corresponding data resolution algorithm for resolving at least portion of the plurality of data issues in an order of the priority of the features; and applying one or more of the plurality of data resolutions algorithm to resolve at least a portion of the data issues in the order of the priority of the features based at least in part on the generated models.

Prioritized data cleaning

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

Embodiments for identifying an optimal cloud computing environment for a computing task is disclosed. Embodiments comprises receiving a computing task to be executed in a cloud computing environment, wherein the computing task requires a set of cloud computing environment parameter values of the cloud computing environment, pre-selecting a set of candidate cloud computing environments, each of which meets the set of cloud computing environment parameter values, ranking the candidate cloud computing environments using reward-based ranking parameter values of the candidate cloud computing environments as an additional selection constraint, and selecting the highest ranking cloud computing environment as the optimal cloud computing environment for the computing task. Furthermore, embodiments comprise executing the computing task in the optimal cloud computing environment, monitoring execution when executing the computing task, and updating parameter values of the reward-based ranking for the selected optimal cloud computing environment.

Selecting best cloud computing environment in a hybrid cloud scenario

An apparatus is disclosed which includes at least one processing device comprising a processor coupled to a memory. The at least one processing device, when executing program code, is configured to: extract one or more entities identified in a plurality of data artifacts based at least in part on one or more datasets, extract one or more entities identified in a plurality of code artifacts based at least in part on the one or more datasets, extract one or more entities identified in a plurality of user interface artifacts based at least in part on the one or more datasets, generate a set of dependency graphs each based at least in part on one or more relationships among the respective extracted one or more entities, and perform one or more of a lexical analysis and a semantic analysis on the set of dependency graphs to identify a data domain of the one or more datasets.

Automatic data domain identification

Methods, systems, and computer program products for identifying anomalous transformations using lineage data are provided herein. A computer-implemented method includes generating a set of column profiles for a corresponding set of columns within one or more datasets based at least in part on lineage data and glossary data, wherein the lineage data comprises information related to transformations performed on each column in the set by a computing platform, and wherein the glossary data comprises information related to one or more terms assigned to one or more of the columns; obtaining information related to a new transformation involving at least one column in the set of columns; comparing the new transformation to the set of column profiles to determine whether the new transformation is anomalous; and in response to determining the new transformation is anomalous, outputting an alert to a user of the computing platform.

Identifying anomalous transformations using lineage data

A computer implemented method creates microservices for an application. A number of processor units clusters programs and data structures for the application using runtime metadata to form groups of the programs and data structures. The runtime metadata is obtained from running the application. The number of processor units creates a design for the microservices for the application using the groups of the programs and the data structures.

Microservice creation using runtime metadata

Methods, systems, and computer program products for factchecking artificial intelligence models using blockchain are provided herein. A computer-implemented method includes obtaining at least one artificial intelligence model and at least one set of data related to the at least one artificial intelligence model; determining a set of characteristics based at least in part on the at least one artificial intelligence model and the at least one set of data; selecting one of a plurality of networks based at least in part on a target deployment of the at least one artificial intelligence model to verify the set of characteristics; generating a report based at least in part on verifying the set of characteristics using the selected network, wherein the report establishes a threshold level of trust for the at least one artificial intelligence model; and storing the report on a blockchain.

Factchecking artificial intelligence models using blockchain

The method performs at the orchestration interface at which update information, including changes to tasks of a workflow, is received from a task manager system (TMS), where the workflow includes a set of tasks, inputs to the tasks, and outputs from the tasks. The inputs and outputs determine runtime dependencies between the tasks. Based on the update information received, the orchestration interface populates a topology of nodes and edges as a directed acyclic graph (DAG) that maps nodes to tasks and edges to runtime dependencies between tasks, based on node inputs and outputs. The orchestration interface instructs the execution of the tasks and handling dependencies by interacting with a task execution system (TES) and by traversing the DAG, the orchestration interface identifies tasks that depend on completed tasks as per the runtime dependencies and instructs the TES to execute the dependent tasks identified.

Automatically orchestrating a computerized workflow

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

Embodiments are disclosed for a method. The method includes receiving an annotation set for a machine learning model. The annotation set includes multiple data points relevant to a task for the machine learning model. The method also includes determining total weights corresponding to the data points. The total weights are determined based on multiple ordering constraints indicating multiple data classes and corresponding weights. The corresponding weights represent a relative priority of the data classes with respect to each other. The method further includes generating an ordered annotation set from the annotation set. The ordered annotation set includes the data points in a sequence based on the determined total weights.

Ordering annotation sets for machine learning

Methods, systems, and computer program products for providing the status of model extraction in the presence of colluding users are provided herein. A computer-implemented method includes generating, for each of multiple users, a summary of user input to a machine learning model; comparing the generated summaries to boundaries of multiple feature classes within an input space of the machine learning model; computing correspondence metrics based at least in part on the comparisons; identifying, based at least in part on the computed metrics, one or more of the multiple users as candidates for extracting portions of the machine learning model in an adversarial manner; and generating and outputting an alert, based on the identified users, to an entity related to the machine learning model.

Providing the status of model extraction in the presence of colluding users

Aspects of the present invention provide an approach for reducing bias in active learning. In an embodiment, a data point is selected from a training dataset for a current training iteration while monitoring for data bias at each addition of data to a virtual training dataset. In addition, a machine learning model is examined for bias after adding the selected data point to the virtual training dataset. When data bias and/or model bias is detected, the data point is considered for potential label modification. The selected data point is modified and, if the raw value of the modified data point is within a predefined tolerance and within a bin of a desired class, the modified data point having a label of the target class is retained. Otherwise, it can be discarded.

Training artificial intelligence models using active learning

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

Techniques for automatically determining different data types found in databases are disclosed. In one example, a computer implemented method comprises receiving a portion of identifying information for one or more components of a database, and generating one or more descriptions for the one or more components based at least in part on the portion of the identifying information for the one or more components. The one or more descriptions are inputted to one or more machine learning models, and, using the one or more machine learning models, one or more data types associated with the one or more components are predicted. The prediction is based at least in part on the one or more descriptions.

Automated classification of data types for databases

Growing community of inventors

New Delhi, India

Sameep Mehta