International Business Machines Corporation

Andrew T Penrose

Jonathan D Dunne

Stanley Kieran Jerrard-Dunne

Vijay Francis

Brendan P Arthurs

Dennis Jinlone Chen

Alan Byrne

Jason Lloyd

Liam S Harpur

Jonathan Frederick Brunn

Olgierd Stanislaw Pieczul

Mark Alexander McGloin

Alice-Maria Marascu

Bo Jiang

Rogelio Vazquez-Rivera

Ethan A Geyer

Naama Tepper

Rachael M H Dickens

Conor D McGrath

Lisa Seacat DeLuca

Sushain Pandit

Charles Daniel Wolfson

Gary Denner

Vijay Arya

Himanshu Gupta

Jitendra Singh

Jinhui Wang

Stephen Thomas Kelly

Jing Zhang

Massimiliano Gallo

Rachel Kutok

Rory O Donnell

Karen Forde

William Holmes

Ryan Goldblatt

Paddy Barrett

Michael Maclynn

Keith Quinn

In an embodiment of the present invention, imagery of a geographic area is received from an image capture system. The imagery includes an obscurity impeding visibility. A geographic region within the geographic area is determined corresponding to a location of the obscurity. The image capture system is requested to capture new imagery of the geographic region at a time determined based on information including weather patterns. The new imagery of the geographic region is combined with the imagery of the geographic area to produce resulting imagery resolving the obscurity.

Resolving obfuscated sections of an image with selectively captured new imagery

One embodiment provides a method for recommending model characteristics to be used in developing a target geo-spatial physical model for a target geographic location utilizing historical lineage data corresponding to historical geo-spatial physical models, including: receiving information related to the target geographic location, wherein the information describes geographical and domain features of the target geographic location; identifying, using at least one similarity algorithm, at least one other geographic location that is similar to the target geographic location, wherein the at least one geographic location has at least one corresponding historical geo-spatial physical model; and recommending, using at least one machine-learning model and based upon the at least one other geographic location, initial model characteristics for developing and deploying the target geo-spatial physical model.

Development of geo-spatial physical models using historical lineage data

In an approach for routing a user analytic request to a registry of analytics models and data sources and operationalizing the user analytic request, a processor receives a user analytic request. A processor applies natural language processing to the user analytic request. A processor associates a first set of vectors and a second set of vectors from the user analytic request to one or more analytics models and data sources by utilizing a plurality of domain-specific ontologies and a plurality of knowledge bases. A processor performs a semantic search for one or more concepts in the one or more analytics models and data sources. A processor receives the one or more concepts found in the one or more analytics models and data sources. A processor selects an analytics model to process the user analytic request. A processor executes the analytics model. A processor outputs a result to the user.

Context-driven analytics selection, routing, and management

Electronic message text classification framework selection is described. An incoming electronic message is classified using a current text classification framework. A classification of the electronic message by the current text classification framework is scored. A cost of re-training the current text classification is compared against a cost of switching to a different text classification framework. One of multiple text classification frameworks, which includes the current text classification framework and other text classification frameworks, is selected based on the score of the classification by the current text classification framework and a result of the comparison.

Electronic message text classification framework selection

An embodiment of the invention may include a method, computer program product and computer system for forecasting future states of a multi-active cloud. The method, computer program product and computer system may include a computing device that determines the operating state of the passive server. The operating state of the passive server is one or more of a deploying state and a smoke testing state. The computing device may determine the probability of the operating state successfully completing. The computing device may delay the second asynchronous workflow until the operating state successfully completes based on the probability of the operating state successfully completing exceeding a predetermined value.

Forecasting future states of a multi-active cloud system

A method, system and computer program product for providing enhanced action fulfillment using classification valency. At least a first message from a user in a channel is analyzed, the first message containing first data of a first data type, and a classification of the first message is inferred based on the first data's content. At least a second message from the user in the channel is analyzed, the second message containing second data of a second data type different from the first data type, and a classification model of the second message is derived based on the second data's content. The classification of the first message and the classification model of the second message are used to determine whether an additional inference is available based on the classification valency. The additional inference is incorporated to determine the enhanced action fulfillment, and the enhanced action is presented to the user for fulfillment.

Enhanced action fulfillment using classification valency

A set of data comprising heterogeneous data is processed in accordance with a data clustering algorithm so as to obtain an initial data cluster comprising homogeneous data. A supervised data cluster generated based on a human analysis of the set of data is obtained and compared with the initial data cluster to obtain a comparison result. The data clustering algorithm is modified based on the comparison result.

Data clustering

In an approach for extracting natural language text from a native device configuration of a network device, a processor converts a native device configuration of a network device to a standard element document. A processor extracts natural language text from the standard element document based on a set of predefined expressions. A processor extracts named entities from the extracted natural language text, according to given categories, via named-entity recognition. A processor maps each of the extracted and categorized named entities to a respective network device, based on respective extraction origin.

Natural language processing in modeling of network device configurations

A thread delta prediction and cognitive conversation opportunity generation. A prediction model may be generated based on chat space conversation data, and predicts a chat thread delta associated with a current chat conversation data. A data package may be created based on the chat thread delta. Based on the data package, a conversation opportunity may be determined for a given topic for given user in the chat space, to conduct during a time period of the chat thread delta in real-time.

Chat delta prediction and cognitive opportunity system

Aspects of the present disclosure relate to managing messages within a collaboration environment. A group collaboration channel in which messages are displayed from multiple users is accessed, where the channel includes an application that provides application messages to the channel. A transcript of the channel is analyzed to determine a set of topics a user has discussed. A subset of topics is selected and a user sentiment of each topic is determined. A machine learning classification model is trained based on the selected subset of topics and the user sentiment of each topic. A message is then received from the application. Prior to displaying the message at the channel, a management category for the message is determined based on the classification model. The management category is applied to the message and the display of the message is managed based on the management category.

Managing content in a collaboration environment

A method, computer system, and computer program product for calculating a group chat segment duration is provided. The embodiment may include capturing a plurality of group chat messages from a chat message repository. The embodiment may also include determining a probability distribution based on analyzing the captured group chat messages over a time vector. The embodiment may further include calculating a time parameter based on the determined probability distribution. The embodiment may also include calculating a content parameter based on one or more relevant chat topics. The embodiment may further include calculating an attendee parameter based on a plurality of attendees and one or more attendee associations. The embodiment may also include determining a chat duration prediction based on the calculated time parameter, the calculated content parameter, and the calculated attendee parameter.

Expected group chat segment duration

Embodiments for text segmentation for topic modelling by a processor. A machine learning mechanism is implemented to enhance one or more topic modelling operations of collected real-time conversation data, the real-time conversation data classified into burst segments or reflection segments; wherein the burst segments comprise successive messages received into the conversation data within a first time interval and the reflection segments comprise multiple messages each received into the conversation data having an inter-arrival time outside the first time interval. A summary of the one or more topic modelling operations is presented on a display to a user according to an output of a text mining analysis implementing the one or more topic modelling operations enhanced by the machine learning mechanism.

Topic kernelization for real-time conversation data

Embodiment for identifying data convergence are presented. First and second sets of data each comprising heterogeneous data are each processed in accordance with a data clustering algorithm so as to obtain a plurality of primary and secondary data clusters, respectively, where each data cluster comprising homogeneous data. The primary and secondary data clusters are analyzed with respect to time to identify convergence of data of the first and second sets of data to first and second topics, respectively. The first and second topics are compared to determine a pattern of data convergence for the first and second data sets.

Data convergence

Embodiments of the present invention provide systems and methods for self-certification by a developer that the software components used during development are used in a secure manner, through the use of annotations. Input and return conditions are defined in an annotation for the software components of a system. The input and return conditions are compared for a match and a warning is generated when the input and return conditions do not match.

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Dublin, Ireland

Andrew T Penrose