Abstract: Solid-state storage management for a system, the management including establishing, externally to a solid-state storage board, a correspondence between a first logical address and a first physical address on solid-state storage devices located on the solid-state storage board. The solid-state storage devices include a plurality of physical memory locations identified by physical addresses. The correspondence between the first logical address and the first physical address is accepted by the solid-state storage board. The correspondence between the first logical address and the first physical address is stored in a location on a solid-state memory device that is accessible by an address translator module, the address translator module and the solid-state memory device located on the solid-state storage board.

Abstract: Techniques are presented that include determining, for data to be written to a nonvolatile memory, a location in the nonvolatile memory to which the data should be written based at least on one or more wear metrics corresponding to the location. The one or more wear metrics are based on measurements of the location. The measurements estimate physical wear of the location. The techniques further include writing the data to the determined location in the nonvolatile memory. The techniques may be performed by methods, apparatus (e.g., a memory controller), and computer program products.

Abstract: An embodiment is a method for establishing a correspondence between a first logical address and a first physical address on solid-state storage devices located on a solid-state storage board. The solid-state storage devices include a plurality of physical memory locations identified by physical addresses, and the establishing is by a software module located on a main board that is separate from the solid-state storage board. The correspondence between the first logical address and the first physical address is stored in in a location on a solid-state memory device that is accessible by an address translator module located on the solid-state storage board. The solid-state memory device is located on the solid-state storage board. The first logical address is translated to the first physical address by the address translator module based on the previously established correspondence between the first logical address and the first physical address.

Abstract: Solid-state storage management for a system that includes a main board and a solid-state storage board separate from the main board is provided. The solid-state storage board includes a solid-state memory device and solid-state storage devices. The system is configured to perform a method that includes a correspondence being established, by a software module located on the main board, between a first logical address and a first physical address on the solid-state storage devices. The correspondence between the first logical address and the first physical address is stored in a location on the solid-state memory device. The method also includes translating the first logical address into the first physical address. The translating is performed by an address translator module located on the solid-state storage board and is based on the previously established correspondence between the first logical address and the first physical address.

Abstract: A method, system and computer program product are disclosed for compressing encrypted data, wherein the data is encrypted by using a block encryption algorithm in a chained mode of operation, and the encrypted data is comprised of a set of N encrypted blocks, C1 . . . CN. In one embodiment, the method comprises leaving block CN uncompressed, and compressing all of the blocks C1 . . . CN in a defined sequence using a Slepian-Wolf code. In an embodiment, the data is encrypted using an encryption key K, and the compressing includes compressing all of the blocks C1 . . . CN without using the encryption key. In one embodiment, the compressing includes outputting the blocks C1 . . . CN as a set of compressed blocks CmprC1 . . . CmprCN-1, and the method further comprises decrypting CN to generate a reconstructed block {tilde over (X)}n, and decrypting and decompressing the set of compressed blocks using {tilde over (X)}n.

Abstract: A method, system and computer program product are disclosed for compressing encrypted data, wherein the data is encrypted by using a block encryption algorithm in a chained mode of operation, and the encrypted data is comprised of a set of N encrypted blocks, C1 . . . CN. In one embodiment, the method comprises leaving block CN uncompressed, and compressing all of the blocks C1 . . . CN in a defined sequence using a Slepian-Wolf code. In an embodiment, the data is encrypted using an encryption key K, and the compressing includes compressing all of the blocks C1 . . . CN without using the encryption key. In one embodiment, the compressing includes outputting the blocks C1 . . . CN as a set of compressed blocks CmprC1 . . . CmprCN-1, and the method further comprises decrypting CN to generate a reconstructed block {tilde over (X)}n, and decrypting and decompressing the set of compressed blocks using {tilde over (X)}n.

Abstract: A method for analyzing a virtual discussion is provided. The method may include identifying, with a processing device, a first concept relevant to a first subdiscussion associated with an online discussion. The method may also include identifying a second concept relevant to the first subdiscussion, and determining a relation between the first concept and the second concept.

Abstract: 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.

Abstract: According to embodiments, methods, systems, and computer program products are provided for generating material compositions including receiving, by a processor, a plurality of material inputs that collectively form one or more known material compositions, assigning a vector space value to each of the plurality of material inputs, wherein each vector space value is unique for each material input, receiving a first constraint and a second constraint, and generating a transformed material composition based on the vector space value, the first constraint, and the second constraint.

Abstract: According to embodiments, methods, systems, and computer program products are provided for generating material compositions including receiving, by a processor, a plurality of material inputs that collectively form one or more known material compositions, assigning a vector space value to each of the plurality of material inputs, wherein each vector space value is unique for each material input, receiving a first constraint and a second constraint, and generating a transformed material composition based on the vector space value, the first constraint, and the second constraint.

Abstract: According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.

Abstract: According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.

Abstract: A system and method dynamically provisions resources in a virtual environment. A current resource requirement is determined based on a current workload demand using one or more computer systems providing resources and access to the resources. The method and system includes comparing the current resource requirement with a current resource allocation using an engine communicating with resources. The engine is configured to allocate the resources, and the engine determines the resource requirement responsive to communications with a plurality of library instances. The library exposes a single-node interface for use by a user-application. The current resource allocation is modified based on the comparison of the current resource requirement with the current resource allocation, and in response to the current resource requirement, using the engine.

Abstract: A method, system and computer program product are disclosed for compressing encrypted data, wherein the data is encrypted by using a block encryption algorithm in a chained mode of operation, and the encrypted data is comprised of a set of N encrypted blocks, C1 . . . CN. In one embodiment, the method comprises leaving block CN uncompressed, and compressing all of the blocks C1 . . . CN in a defined sequence using a Slepian-Wolf code. In an embodiment, the data is encrypted using an encryption key K, and the compressing includes compressing all of the blocks C1 . . . CN without using the encryption key. In one embodiment, the compressing includes outputting the blocks C1 . . . CN as a set of compressed blocks CmprC1 . . . CmprCN-1, and the method further comprises decrypting CN to generate a reconstructed block {tilde over (X)}n, and decrypting and decompressing the set of compressed blocks using {tilde over (X)}n.

Abstract: Embodiments relate to estimating closeness of topics based on graph analytics. A graph that includes a plurality of nodes and edges is accessed. Each node in the graph represents a topic and each edge represents a known association between two topics. A statistical traversal experiment is performed on the graph. A strength of relations between any two topics represented by nodes in the graph is inferred based on statistics extracted from the statistical traversal experiment.

Abstract: Enterprise learning system may receive as input learning goals for a learner. Learning goals may also be specified by job-roles. The system may output course-sequence recommendations, sequence-associations to learning goals and learning-goal recommendations. A semantic analysis may automatically relate learning-goals to learning assets and job-roles. Computational semantic relation-finding may leverage available knowledge-bases. A sequence-recommendation recommends course or learning asset sequences to the learner based on historical usage, the learner's current and/or desired job-roles and learning goals.

Abstract: Embodiments relate to estimating closeness of topics based on graph analytics. A graph that includes a plurality of nodes and edges is accessed. Each node in the graph represents a topic and each edge represents a known association between two topics. A statistical traversal experiment is performed on the graph. A strength of relations between any two topics represented by nodes in the graph is inferred based on statistics extracted from the statistical traversal experiment.

Abstract: A computing device identifies one or more contextual variables within one or more social media messages. The computing device determines a contextual influence value based on the one or more social media messages. The computing device determines an appetite level. The computing device determines an unadjusted expected value of pleasantness based on the determined contextual influence value and the determined appetite level.

Abstract: A method, system and computer program product are disclosed for compressing encrypted data, wherein the data is encrypted by using a block encryption algorithm in a chained mode of operation, and the encrypted data is comprised of a set of N encrypted blocks, C1 . . . CN. In one embodiment, the method comprises leaving block CN uncompressed, and compressing all of the blocks C1 . . . CN in a defined sequence using a Slepian-Wolf code. In an embodiment, the data is encrypted using an encryption key K, and the compressing includes compressing all of the blocks C1 . . . CN without using the encryption key. In one embodiment, the compressing includes outputting the blocks C1 . . . CN as a set of compressed blocks CmprC1 . . . CmprCN-1, and the method further comprises decrypting CN to generate a reconstructed block {tilde over (X)}n, and decrypting and decompressing the set of compressed blocks using {tilde over (X)}n.

Abstract: A method that comprises receiving a plurality of inputs including data a plurality of multiple business repositories, generating from the plurality of inputs a corpus graph as a statistical relational network, inferring a plurality of new relations between informational elements of the statistical relational network, and generating a plurality of summaries of the plurality of new relations. Further, each summary describes the informational elements of the statistical relational network associated with a corresponding risk-relation.