Abstract: “Resource guarantee” refers to a unit of a resource that is guaranteed and therefore designated to a consumer. A multi-phased constraint programming (CP) approach is used to determine assignments of resource guarantees of a set of consumers to a set of hosts in a resource system. Phase I uses CP to segregate non-split consumers from split consumers. Phase II uses CP to assign each cotenant group of non-split consumers to a respective host. Phase III uses CP to assign resource guarantees of the split consumers across the hosts, wherein resource guarantees of a single split consumer may be splits across different hosts. Each phase involves execution of a CP solver based on a different CP data model. A CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

Abstract: “Resource guarantee” refers to a unit of a resource that is guaranteed and therefore designated to a consumer. A multi-phased constraint programming (CP) approach is used to determine assignments of resource guarantees of a set of consumers to a set of hosts in a resource system. Phase I uses CP to segregate non-split consumers from split consumers. Phase II uses CP to assign each cotenant group of non-split consumers to a respective host. Phase III uses CP to assign resource guarantees of the split consumers across the hosts, wherein resource guarantees of a single split consumer may be splits across different hosts. Each phase involves execution of a CP solver based on a different CP data model. A CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

Abstract: “Resource guarantee” refers to a unit of a resource that is guaranteed and therefore designated to a consumer. A multi-phased constraint programming (CP) approach is used to determine assignments of resource guarantees of a set of consumers to a set of hosts in a resource system. Phase I uses CP to segregate non-split consumers from split consumers. Phase II uses CP to assign each cotenant group of non-split consumers to a respective host. Phase III uses CP to assign resource guarantees of the split consumers across the hosts, wherein resource guarantees of a single split consumer may be splits across different hosts. Each phase involves execution of a CP solver based on a different CP data model. A CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

Abstract: Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

Abstract: Described is an improved approach to remove data outliers by filtering out data correlated to detrimental events within a system. One or more detrimental even conditions are defined to identify and handle abnormal transient states from collected data for a monitored system.

Abstract: Described is an approach for performing context-aware prognoses in machine learning systems. The approach harnesses streams of detailed data collected from a monitored target to create a context, in parallel to ongoing model operations, for the model outcomes. The context is then probed to identify the particular elements associated with the model findings.

Abstract: Described is an improved approach to implement selection of training data for machine learning, by presenting a designated set of specific data indicators where these data indicators correspond to metrics that end users are familiar with and are easily understood by ordinary users and DBAs within their knowledge domain. Selection of these indicators would correlate automatically to selection of a corresponding set of other metrics/signals that are less understandable to an ordinary user. Additional analysis of the selected data can then be performed to identify and correct any statistical problems with the selected training data.

Abstract: Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

Abstract: Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

Abstract: Described is an improved approach to remove data outliers by filtering out data correlated to detrimental events within a system. One or more detrimental even conditions are defined to identify and handle abnormal transient states from collected data for a monitored system.

Abstract: Described is an approach for performing context-aware prognoses in machine learning systems. The approach harnesses streams of detailed data collected from a monitored target to create a context, in parallel to ongoing model operations, for the model outcomes. The context is then probed to identify the particular elements associated with the model findings.

Abstract: Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

Abstract: Described is an improved approach to implement selection of training data for machine learning, by presenting a designated set of specific data indicators where these data indicators correspond to metrics that end users are familiar with and are easily understood by ordinary users and DBAs within their knowledge domain. Selection of these indicators would correlate automatically to selection of a corresponding set of other metrics/signals that are less understandable to an ordinary user. Additional analysis of the selected data can then be performed to identify and correct any statistical problems with the selected training data.

Abstract: A glass melt production apparatus, which comprises a melting vessel, a vacuum degassing apparatus, a first conducting pipe structure connecting the melting vessel and the vacuum degassing apparatus, and a second conducting pipe structure to introduce a glass melt to a forming means, provided downstream the vacuum degassing apparatus; the vacuum degassing apparatus having an uprising pipe through which the glass melt from the melting vessel ascends, a vacuum degassing vessel, and a downfalling pipe through which the glass melt from the vacuum degassing vessel descends; the flow path of the glass melt in the uprising pipe, the vacuum degassing vessel and the downfalling pipe being made of a refractory material; the first conducting pipe structure having an upstream pit to supply the glass melt to the uprising pipe; and the second conducting pipe structure having a downstream pit containing the glass melt from the downfalling pipe; the glass melt production apparatus further comprising a third conducting pipe st

Abstract: It is an object of the present invention to provide a vacuum degassing apparatus configured so as to be capable of carrying out more simply exchange operation for an extension pipe made of heat-resisting metal and immersed in molten glass. The present invention includes a vacuum housing, a vacuum vessel, an introduction pipe and a discharge pipe which are assembled by a plurality of bricks, and an extension pipe connected to at least one of the introduction pipe and the discharge pipe and made of heat-resisting metal. The extension pipe is constituted by a base end portion, a body portion formed so as to be continuous to the base end portion, and a sealing flange extending from an outer peripheral side of the body portion.

Abstract: A conduit structure for molten glass, in which molten glass is flown through a double-pipe structure, with little stress loading even for a long time operation. A supporting rib connecting an inner pipe and an outer pipe is employed in the double pipe structure having the inner pipe and outer pipe in the conduit structure for molten glass, wherein excessive stress concentration hardly occurs in an inner pipe-joint portion, an outer pipe-joint portion and the rib when the rib is applied with a stress load due to the weight of the inner pipe.

Abstract: It is an object of the present invention to provide a vacuum degassing apparatus configured so as to be capable of carrying out more simply exchange operation for an extension pipe made of heat-resisting metal and immersed in molten glass. The present invention includes a vacuum housing, a vacuum vessel, an introduction pipe and a discharge pipe which are assembled by a plurality of bricks, and an extension pipe connected to at least one of the introduction pipe and the discharge pipe and made of heat-resisting metal. The extension pipe is constituted by a base end portion, a body portion formed so as to be continuous to the base end portion, and a sealing flange extending from an outer peripheral side of the body portion.