Abstract: A computer processor generates a topic-based dataset based on parsing content received from a plurality of information sources, which includes historical data and scientific data, associated with a location of a natural resource. The processor generates a plurality of clusters, respectively corresponding to like-topic data of the topic-based dataset. The processor determines a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on features associated with each of the plurality of clusters of the like-topic data. The processor combines pairs of clusters, based on a similarity heuristic applied to the one or more pairs of clusters, and the processor determines a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.

Abstract: 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.

Abstract: Approaches are provided for answering an inquiry of a cognitive distributed network. An approach includes receiving the inquiry at the cognitive distributed network. The approach further includes determining a classification for the inquiry based on natural language of the inquiry. The approach further includes classifying the inquiry as a single question class. The approach further includes determining, by at least one computing device, a type of introspection to be used by the cognitive distributed network on the inquiry. The approach further includes generating an answer to the inquiry based on the determined type of introspection.

Abstract: 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.

Abstract: A computer processor generates a topic-based dataset based on parsing content received from a plurality of information sources, which includes historical data and scientific data, associated with a location of a natural resource. The processor generates a plurality of clusters, respectively corresponding to like-topic data of the topic-based dataset. The processor determines a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on features associated with each of the plurality of clusters of the like-topic data. The processor combines pairs of clusters, based on a similarity heuristic applied to the one or more pairs of clusters, and the processor determines a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.

Abstract: Embodiments of the present invention provide an approach for allocating computing resources based on social networking/media trends in a networked computing environment (e.g., a cloud computing environment). In a typical embodiment, a baseline computing resource allocation will be determined for the networked computing environment based upon historical computing resource data (e.g., stored in at least one computer storage device). Social networking trend data corresponding to usage of a set of social networking websites may be received and analyzed to determine a forecasted computing resource allocation (e.g., based on social networking trends). The baseline computing resource allocation may be compared to the forecasted computing resource allocation to identify any difference therebetween. A computing resource allocation protocol/plan may then be determined based on the comparison (e.g., to address the difference).

Abstract: A computer processor generates a topic-based dataset based on parsing content received from a plurality of information sources, which includes historical data and scientific data, associated with a location of a natural resource. The processor generates a plurality of clusters, respectively corresponding to like-topic data of the topic-based dataset. The processor determines a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on features associated with each of the plurality of clusters of the like-topic data. The processor combines pairs of clusters, based on a similarity heuristic applied to the one or more pairs of clusters, and the processor determines a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method and associated systems for optimizing a computing platform. A processor joins sets of configurable parameters into groups that each identifies a configuration of the computing environment or of a component or subsystem of the computing environment. The processor generates a set of variations of each group, where each variation identifies a candidate configuration of the component, subsystem, or platform, and where each candidate configuration identifies a distinct set of values of the group of parameters associated with that component, subsystem, or platform. Each configuration of this first generation of configurations undergoes a massively parallel iterative procedure that generates a next generation of configurations by performing operations upon the first generation that are similar to those of a natural-selection process.

Abstract: 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.

Abstract: A method and associated systems for optimizing a computing platform. A processor joins sets of configurable parameters into groups that each identifies a configuration of the computing environment or of a component or subsystem of the computing environment. The processor generates a set of variations of each group, where each variation identifies a candidate configuration of the component, subsystem, or platform, and where each candidate configuration identifies a distinct set of values of the group of parameters associated with that component, subsystem, or platform. Each configuration of this first generation of configurations undergoes a massively parallel iterative procedure that generates a next generation of configurations by performing operations upon the first generation that are similar to those of a natural-selection process.

Abstract: An approach is provided for allowing a network computing (e.g., cloud computing) infrastructure to modify its resource allocation plan (e.g., an instance count) by using a Kth derivative vector plot, which may be generated using historical logs. Among other things, this approach enables an infrastructure to project an allocation forecast for a specified duration and adapt to changes in network traffic.

Abstract: A method and associated systems for optimizing a computing platform. A processor joins sets of configurable parameters into groups that each identifies a configuration of the computing environment or of a component or subsystem of the computing environment. The processor generates a set of variations of each group, where each variation identifies a candidate configuration of the component, subsystem, or platform, and where each candidate configuration identifies a distinct set of values of the group of parameters associated with that component, subsystem, or platform. Each configuration of this first generation of configurations undergoes a massively parallel iterative procedure that generates a next generation of configurations by performing operations upon the first generation that are similar to those of a natural-selection process.

Abstract: A method and associated systems for optimizing a computing platform. A processor joins sets of configurable parameters into groups that each identifies a configuration of the computing environment or of a component or subsystem of the computing environment. The processor generates a set of variations of each group, where each variation identifies a candidate configuration of the component, subsystem, or platform, and where each candidate configuration identifies a distinct set of values of the group of parameters associated with that component, subsystem, or platform. Each configuration of this first generation of configurations undergoes a massively parallel iterative procedure that generates a next generation of configurations by performing operations upon the first generation that are similar to those of a natural-selection process.

Abstract: A method and associated systems for optimizing a computing platform. A processor joins sets of configurable parameters into groups that each identifies a configuration of the computing environment or of a component or subsystem of the computing environment. The processor generates a set of variations of each group, where each variation identifies a candidate configuration of the component, subsystem, or platform, and where each candidate configuration identifies a distinct set of values of the group of parameters associated with that component, subsystem, or platform. Each configuration of this first generation of configurations undergoes a massively parallel iterative procedure that generates a next generation of configurations by performing operations upon the first generation that are similar to those of a natural-selection process.

Abstract: A method and associated systems for optimizing a computing platform. A processor joins sets of configurable parameters into groups that each identifies a configuration of the computing environment or of a component or subsystem of the computing environment. The processor generates a set of variations of each group, where each variation identifies a candidate configuration of the component, subsystem, or platform, and where each candidate configuration identifies a distinct set of values of the group of parameters associated with that component, subsystem, or platform. Each configuration of this first generation of configurations undergoes a massively parallel iterative procedure that generates a next generation of configurations by performing operations upon the first generation that are similar to those of a natural-selection process.

Abstract: An approach is provided for allowing a network computing (e.g., cloud computing) infrastructure to modify its resource allocation plan (e.g., an instance count) by using a Kth derivative vector plot, which may be generated using historical logs. Among other things, this approach enables an infrastructure to project an allocation forecast for a specified duration and adapt to changes in network traffic.

Abstract: A computer processor generates a topic-based dataset based on parsing content received from a plurality of information sources, which includes historical data and scientific data, associated with a location of a natural resource. The processor generates a plurality of clusters, respectively corresponding to like-topic data of the topic-based dataset. The processor determines a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on features associated with each of the plurality of clusters of the like-topic data. The processor combines pairs of clusters, based on a similarity heuristic applied to the one or more pairs of clusters, and the processor determines a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.