International Business Machines Corporation

Michelle Welcks

Aaron K Baughman

Nicholas Albert McCrory

Mohamed Nooman Ahmed

Thomas Benjamin Harrison

David Alexander Provan

Andeep S Toor

Brian M O'Connell

John S Cope

Social media inputs specific to a content of a broadcast program and received from a plurality of users are analyzed to determine a sentiment value expressed relative to a portion of the content. A group is formed of those users whose sentiment values expressed relative to the portion of the content are within a specified tolerance value of sentiment value and are expressed within a defined period during the broadcast of the program. A historical data from the inputs unrelated to the content is analyzed to extract an entity. A time zone of a geographical location of the entity is associated with the group. a sentiment of the group expressed relative to the portion of the content is shifted by an amount of time on a program continuum timeline, such that the sentiment of the group aligns with a time when the portion was broadcast in the time zone.

Event affinity detangling with broadcasts rolling over time zones

A content of a social media data is analyzed. The social media data relates to a workload that is to be located on a node. A location corresponding to the social media data is computed. The social media data is regarded as originating from the location. A set of nodes is selected by computing a social distance objective function, the set of nodes includes the node. Each node in the set of nodes is located within a range of distances specified by the social distance objective function. A first subset of nodes is removed from the set of nodes, where the first subset of nodes fails to satisfy another objective function. In response to a second subset of nodes satisfying the social distance objective function and the other objective function, the node is selected from the second subset and the workload is deployed on the node.

Social objectives-based workload resolution in a cloud environment

Social media inputs from users, and specific to a content of a broadcast program, are analyzed to determine a sentiment value expressed relative to a portion of the content by a user. A group is formed of those users whose sentiment values expressed relative to the portion within a defined period during the broadcast of the program are within a specified tolerance value of sentiment value expressed by the user. A time zone of a location extracted from a social media input of the user is assigned to the group. A fabricated event that evokes a predicted sentiment is inserted in the broadcast program. A confidence value for the time zone is computed by comparing a sentiment value of a response by the group to the fabricated event with the predicted sentiment value. The group's placement in the time zone is confirmed when the confidence value exceeds a threshold.

Location-based event affinity detangling for rolling broadcasts

A set and a second set of collections of forecasted feature vectors are selected from a repository for a future time window, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set and a collection in the second set indicating an event related to the cyber-attack in a first region and a second event in a second region, respectively, of the environment at a discrete time. The events corresponding to the collections are classified, using an LTSM network, into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase that corresponds to the class is predicted as likely to occur during the future time window in the region.

Detecting and predicting cyber-attack phases in adjacent data processing environment regions

An application is deployed on a first cloud from a set of clouds. A simulator is deployed on each cloud in the set. A vertical relationship function is computed between a time-series of the application and a time-series of the simulator. A first actual value in the time-series of the application is forecasted for a future time. A horizontal relationship function is computed between a first simulator value in the time-series of the simulator on the first cloud and a second simulator value in the time-series of the simulator on a second cloud. A second actual value in the time-series of a hypothetical deployment of the application on the second cloud is forecasted for the future time without deploying the application on the second cloud. The application is placed on the second cloud when the second actual value satisfies a condition.

Workload placement in a hybrid cloud environment

Methods, computer program products, and systems are presented. The methods include, for instance: providing social media feed cognizant lighthouse display service for communicating to spectators, information in regard to social response to an event, the lighthouse display service by use of a lighthouse structure further including a private display for subscribed data for an accessibility-layered communication service as encoded with a private key to make inaccessible to the spectators but to be decoded on a user device with the private key.

Accessibility-layered communication service using lighthouse

A set and a second set of collections of forecasted feature vectors are selected from a repository for a future time window, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set and a collection in the second set indicating an event related to the cyber-attack in a first region and a second event in a second region, respectively, of the environment at a discrete time. The set of collections is input at a first input and the second set of collections is input at a second input in the LSTM. The events corresponding to the collections are classified into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase that corresponds to the class is predicted as likely to occur during the future time window in the region.

A set of collections of forecasted feature vectors is selected from a repository for a future time window after a present time, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set having feature vectors that are indicative of an event related to the cyber-attack in a region of the environment at a discrete time. The events corresponding to the collections in the set are classified into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase is determined that corresponds to the class. The determined phase is predicted as likely to occur during the future time window in the region.

Detecting and predicting cyber-attack phases in data processing environment regions

Methods, computer program products, and systems are presented. The methods include, for instance: providing social media feed cognizant lighthouse display service for communicating to spectators, information in regard to social response to an event, the lighthouse display service further including a private display for subscribed event details as encoded with a key to make inaccessible to the spectators but to be decoded on a user device with the key.

Growing community of inventors

Tampa, FL, United States of America

Michelle Welcks