Abstract: A computer-implemented method, apparatus and computer program product of question generation in a cognitive system, comprising: receiving a training question; applying rules of grammar and common usage to generate variants of the training question; identifying any generalized versions of specific words, such as verbs or nouns in the training question; identifying any questions related to the training question; filtering out, utilizing a classifier, generated variants which do not make sense; and providing one or more output questions, synonymous to the training question.

Abstract: A computer-implemented method, apparatus and computer program product of question generation in a cognitive system, comprising: receiving a training question; applying rules of grammar and common usage to generate variants of the training question; identifying any generalized versions of specific words, such as verbs or nouns in the training question; identifying any questions related to the training question; filtering out, utilizing a classifier, generated variants which do not make sense; and providing one or more output questions, synonymous to the training question.

Abstract: A method and associated systems for automatic redistribution of virtual machines. A cloud-optimization module selects parameters, such as bandwidth requirements, that characterize an efficiency of a virtual network. It assigns weightings to these parameters based on relative importance of each parameter to the proper operation of the network, where the weightings may be determined as functions of captured network-performance statistics. The module translates the network's topology into a graph in which each node represents a network entity, such as a virtual machine or an application, and each edge represents a connection between two such entities. The module then uses a growing neural gas algorithm to revise the graph and the weightings, and translates the revised graph to a more optimal topology that has redistributed the network entities to operate more efficiently, as measured by the weighted parameters.

Abstract: A method and associated systems for automatic redistribution of virtual machines. A cloud-optimization module selects parameters, such as bandwidth requirements, that characterize an efficiency of a virtual network. It assigns weightings to these parameters based on relative importance of each parameter to the proper operation of the network, where the weightings may be determined as functions of captured network-performance statistics. The module translates the network's topology into a graph in which each node represents a network entity, such as a virtual machine or an application, and each edge represents a connection between two such entities. The module then uses a growing neural gas algorithm to revise the graph and the weightings, and translates the revised graph to a more optimal topology that has redistributed the network entities to operate more efficiently, as measured by the weighted parameters.

Abstract: A method and apparatus for data exploration in a data processing system. An apparatus according to an embodiment includes: an analyzing component for analyzing a data model to identify a first and a second data entity and a relationship between the first and the second data entity; a generator component for creating a first and second atom data entity associated with the first and the second data entity; a generator component for creating a link data entity associated with the relationship between the first and second data entity; and an assignor component for assigning a behavior to one of the atom data entities or the link data entity, wherein the atom data entity exhibits a behavior associated with the link data entity and wherein the link data entity exhibits a behavior associated with one of the atom data entities.

Abstract: A method and apparatus for data exploration in a data processing system. An apparatus according to an embodiment includes: an analyzing component for analyzing a data model to identify a first and a second data entity and a relationship between the first and the second data entity; a generator component for creating a first and second atom data entity associated with the first and the second data entity; a generator component for creating a link data entity associated with the relationship between the first and second data entity; and an assignor component for assigning a behavior to one of the atom data entities or the link data entity, wherein the atom data entity exhibits a behavior associated with the link data entity and wherein the link data entity exhibits a behavior associated with one of the atom data entities.