International Business Machines Corporation

King Abdullah University of Science and Technology

Anshul Sheopuri

Zon-Yin Shae

Hani Talal Jamjoom

Mark Edward Podlaseck

Huiming Qu

Yaoping Ruan

Qi Yu

Hongfei Li

Jinfeng Yi

Milind R Naphade

Markus Ettl

Wesley M Gifford

Karthik Sourirajan

Young Min Lee

Pawan Raghunath Chowdhary

Thomas Robert Ervolina

Daniel John Peters

Richard John Nash

Duncan A Ashby

So Yeon Chun

Todd M Appel

Anders Thornqvist

Martin Vuyk

Ashish Jagmohan

Ying Li

Maheswaran Surendra

Yi-Min Chee

Chitra Dorai

Rong Liu

Priyanka Agrawal

Nandakishore Kambhatla

Pavankumar Murali

Wei Sun

Shota Aki

Saikat Mukhopadhyay

Priyanka Dasgupta

Aditya Telang

Binny Rieder

Pooja Arya

Nickle Lamoreaux

Joanna Daly

Benjamin Letham

John J Ambrose

Suzanne M Rodeman

Denis R Saure

Mahmoud A Abdelhamid Mohamed

Diane J Gherson

Nickle J LaMoreaux

Richard Patterson

Carla Grant-Pickens

Thomas A Stachura

Brenda Swatek

Maria Tarsia

Monther Abdullah Al-Dawsari

Denis Roland Saure

En Cheng

Various embodiments are provided for implementing intelligent decision support system in a computing environment by a processor. Data of historical decisions may be collected and examples of decisions by domain experts may be generated. One or more machine learning models may be generated using different splits of the historical data and the annotated data. The one or more machine learning models may be combined and used to generate ensemble machine learning models that generate recommendations for the decisions. Users interact with a user interface displaying the data, recommendations, reasons for recommendations and a conversational dialog system for querying about the data, recommendations and guidance for decision making.

Intelligent decision support system

A method and system are provided. The method includes receiving by a computer having a processor and a memory, sequence data that includes labeled data and unlabeled data. The method further includes generating, by the computer having the processor and the memory, a recurrent neural network model of the sequence data, the recurrent neural network model having a recurrent layer and an aggregate layer. The recurrent neural network model feeds sequences generated from the recurrent layer into the aggregate layer for aggregation, stores temporal dependencies in the sequence data, and generates labels for at least some of the unlabeled data.

Customer profile learning based on semi-supervised recurrent neural network using partially labeled sequence data

A user trajectory graph may be constructed based on spatio-temporal data. A mobility pattern may be extracted from the user trajectory graph. Users may be clustered into groups, wherein the users in a same group possess similar feature values in the mobility pattern, and the users in different groups have different feature values, to identify personas and location sets. A distribution model may be constructed that models user timing and location preference, wherein an outcome indicates a preference for a particular time bin on a particular day for a particular location.

Automatic extraction of user mobility behaviors and interaction preferences using spatio-temporal data

A method and system are provided. The method includes converting, by a computer having a processor and a memory, categorical sequence data for a customer journey into a numerical similarity matrix. The method further includes learning, by the computer, features of the customer journey by applying a distance metric learning based matrix factorization approach to the numerical similarity matrix.

Feature learning on customer journey using categorical sequence data

A system, method and program product for bundling resources for a resource provisioning platform. A system is disclosed that includes a plurality of resources, wherein each resource belongs to one of a plurality of categories; a bundling system having: a data collection system that gathers historical transaction data associated with the resources; an analysis system that analyzes the historical transaction data to assign estimated valuations to different bundles of resources and includes (a) a substitution effect analyzer to analyze a substitution effect of resources in each category using discrete choice modeling and marginal value estimation, and (b) a joint dependence analyzer that determines intra-category and inter-category joint dependent inferences across all resources; and a bundle selection system that selects a set of bundles for provisioning based on the estimated valuations.

Resource provisioning platform with optimized bundling

Identifying one or more patterns of content consumption across multiple entities and determining an engagement action for a user of an entity based on the patterns may include receiving information associated with content, cross-industry user data associated with consumption of the content, and a given organization user data associated with consumption of the content. A first set of consumption profile vectors associated with the given organization and one or more second set of consumption profile vectors associated respectively with one or more cross-industry organizations may be generated. Information associated with a target user in the given organization may be received. A customized learning plan for the target user in the given organization may be generated based on the first set of consumption profile vectors, the one or more second set of consumption profile vectors, and the information associated with the target user.

Identifying patterns of learning content consumption across multiple entities and automatically determining a customized learning plan based on the patterns

A scheduling system and method for high-performance computing (HPC) applications includes a network management component stored in physical memory and executed by a processor. The management component is configured to transform HPC resources into a schedulable resource catalog by transforming multi-dimensional HPC resources into a one dimension versus time resource catalog with a dependent graph structure between resources such that HPC resources are enabled to be provisioned into a service environment with predictable provisioning using the resource catalog. A graphical user interface component is coupled to the network management component and configured to provide scheduling visibility to entities and to enable a plurality of different communication modes for scheduling and communication between entities.

High performance computing as a service

A system and method for scheduling resources includes a memory storage device having a resource data structure stored therein which is configured to store a collection of available resources, time slots for employing the resources, dependencies between the available resources and social map information. A processing system is configured to set up a communication channel between users, between a resource owner and a user or between resource owners to schedule users in the time slots for the available resources. The processing system employs social mapping information of the users or owners to assist in filtering the users and owners and initiating negotiations for the available resources.

System and method for dynamic rescheduling of multiple varying resources with user social mapping

Methods and a system are provided that is performed by a computer server for inferring location context categories for a set of mobile users having at least two members. A method includes, for each mobile user in the set, obtaining at least one location context category therefor from publically available information responsive to uncertain mobile device location data. The method further includes applying multi-user collaborative machine learning to the at least one location context category for each mobile user in the set to infer a single refined location context category for each mobile user in the set.

Location context inference based on user mobile data with uncertainty

Selecting candidates from a set of candidates by receiving a set of input parameters about a single aspect of the candidates and applying multiple scoring methods to those input parameters to compute a candidate score via each scoring method. A subset of zero or more candidates is then selected from the set of candidates by applying one or more selection criteria to the set of scores. There is not necessarily a value for every input parameter of every candidate.

Robust selection of candidates

A computer readable medium embodies a program of machine-readable instructions executable by a processing apparatus to perform operations including determining information corresponding to a number of differences in distances between ending points of journeys taken by a vehicle and starting points of consecutive journeys taken by the vehicle, and transmitting one or more representations of the information. Another computer readable medium tangibly includes instructions for, for each of a number of vehicles, receiving one or more scores corresponding to a vehicle, and determining a ranked list containing at least a portion of the scores, and outputting the ranked list. Another computer readable medium includes instructions for, for each of a number of vehicles, receiving information corresponding to a vehicle, using one or more metrics, calculating one or more values from the received information, and communicating one or more representations of the one or more value to one or more entities.

Anomaly detection for road user charging systems

Techniques for providing a reservation system are provided. The techniques include displaying a scalable visualization object, wherein the scalable visualization object comprises an expanded view element of the reservation system depicting information in connection with a selected interval of time and a compressed view element of the reservation system depicting information in connection with one or more additional intervals of time, maintaining a visual context between the expanded view and the compressed view within the visualization object, and enabling a user to switch between the expanded view and the compressed view to facilitate use of the reservation system.

Reservation system with graphical user interface

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Hartsdale, NY, United States of America

Anshul Sheopuri