International Business Machines Corporation

Ritwik Chaudhuri

Ramasuri Narayanam

Gyana Ranjan Parija

Rishi Saket

Rohith Dwarakanath Vallam

Sougata Mukherjea

Ety Khaitzin

Arvind Agarwal

Kushal Mukherjee

Rakesh Rameshrao Pimplikar

Rajmohan Chandrahasan

Shrihari Vasudevan

Kirushikesh Db

Embodiments of the present invention provide an approach for exploring interesting data patterns in structured tables through recommending aggregate questions in a conversational data exploration. Specially, interesting features and operators are selected that are used to frame aggregate questions based on user intent and the data. The aggregate questions are ranked based on user persona and interestingness of the questions. The approach dynamically adapts and improves the recommendation of interesting and relevant aggregate questions for the user based on user feedback iteratively.

Recommending aggregate questions in a conversational data exploration

One embodiment provides a method, including: receiving, at a central system, a query requesting access to a dataset, wherein the central system communicates with a plurality of data storage locations, each having a governance policy for data stored at the data storage location, wherein different portions of the dataset are stored within different of the plurality of data storage locations; sending a sub-query formulated based upon the query; receiving a governance enforcement actions listing corresponding to the portion of the dataset stored within the corresponding data storage location; generating a meta-policy of enforcement actions for all of the plurality of data storage locations storing portions of the dataset, wherein the meta-policy identifies enforcement actions and an order of the enforcement actions to be applied to the dataset; and providing the meta-policy to each of the plurality of data storage locations.

Consistent governance with asset constraints across data storage locations

A processor may identify one or more transaction verification requests from one or more entities. The processor may convert each of the one or more transaction verification requests into respective hashed transaction verification requests. The processor may send, on one or more private, anonymous channels, the hashed transaction verifications to an orchestrator. The processor my decrypt the hashed transaction verifications with the orchestrator. The processor may determine whether information in each of the one or more transaction verification requests matches.

Private shared resource confirmations on blockchain

A computer-implemented method is disclosed which includes receiving an initial decision from each of a plurality of agents based on the same criteria, wherein at least a subset of the agents are computing nodes, and wherein the criteria pertain to a resource allocation request; assigning an equal weight to resources specific to each of the agents; generating, for each of the initial decisions, a revised decision based on (i) comparing the initial decisions to each other, (ii) modeling aggregate group behavior represented by the initial decisions of the plurality of agents, and (iii) modeling a behavior of each of the agents in view of the initial decisions; updating the weights assigned to the resources specific to each of the agents based at least in part on the revised decisions; and dynamically allocating resources in response to the resource allocation request based at least in part on the updated weights.

Multi-agent system for efficient decentralized information aggregation by modeling other agents' behavior

Methods, systems, and computer program products for generating capacity planning schedules while protecting the privacy of stakeholder preferences of a set of metrics are provided herein. A computer-implemented method includes identifying stakeholders associated with capacity planning for a project; determining metrics to be used in the capacity planning; obtaining, from each of the stakeholders, an initial preferred order of emphasis of the metrics; calculating similarity scores between the initial preferred orders of emphasis; outputting, to each of the stakeholders, the similarity scores, wherein the identity of the stakeholders has been masked; obtaining, from each of the stakeholders, at least a second iteration of a preferred order of emphasis of the metrics; generating a final order of emphasis of the multiple metrics upon a determination that the stakeholders provided at least a predetermined number of identical preferred orders of emphasis; and outputting the final order of emphasis of the metrics.

Determining an order of emphasis for capacity planning metrics based on similarity scores of stakeholder preferred orders of emphasis while protecting stakeholder privacy

Growing community of inventors

New Delhi, India

Ritwik Chaudhuri