International Business Machines Corporation

Timothy Rea Dinger

Yuan-Chi Chang

Long H Vu

Venkata Nagaraju Pavuluri

Lingtao Cao

Deepak Srinivas Turaga

Nagui Halim

Daby Mousse Sow

Dudley A Chance

Alain E Biem

Gang Luo

David P Lapotin

William Joseph Gallagher

David B Goland

Dharmashankar Subramanian

Thomas K Worthington

Evan Ezra Davidson

Debarun Bhattacharjya

Arthur Davidson

Walter V Vilkelis

A mechanism is provided for implementing a model update mechanism to update new models in real time while avoiding data loss and system downtime. Responsive to receiving a request to update a scorer model currently being executed by an existing worker thread in the data processing system, the model update mechanism initializing a new worker thread. The model update mechanism loads an updated scorer model into the new worker thread and initializes a state transfer from the existing worker thread to the new worker thread. The model update mechanism executes the updated scorer model such that the updated scorer model scores the input data. The model update mechanism then outputs a prediction based on the updated scorer model processing of the input data.

Data loss machine learning model update

Mechanism are provided to select a machine learning model from an analytics model library based on ingested data. One or more pieces of clarified data are fused to provide time-correlated data tuples of data streams. One or more features are extracted from the time-correlated data tuples and scored based on a set of predetermined rules thereby generating discriminative scoring of trigger data. Utilizing the discriminative scoring of the trigger data, trigger data of a current analytics model being utilized by the data processing and one or more new analytics models from the analytics model library are scored. Responsive to the scoring of the trigger data indicating a selection of a different analytics model from the analytics model library, the current analytics model is replaced with a selected analytics model from the analytics model library such that the data processing system executes the selected analytics model.

Context driven model selection

A method, computer program product, and/or computer system improves a future efficiency of a specific system. One or more processors receive multiple historical data snapshots that describe past operational states of a specific system. The processor(s) identify a time series pattern for the time series of data in the multiple historical snapshots and calculate their variability. The processor(s) then determine that the variability in a first sub-set of the time series pattern is larger than a predefined value, and determine that future values of the first set of the time series pattern are a set of non-forecastable future values. The processor(s) also determine that the variability in a second sub-set of the time series pattern for the data is smaller than the predefined value, and utilizes this second sub-set to modify the specific system at a current time.

Modifying a particular physical system according to future operational states

A mechanism is provided to identify wall-clock time reference dependency in one or more software components of a data analytics solution. The data analytics solution is decomposed into a set of software components. A first software component of the set of software components is deployed to a first computer server and the remaining software components are deployed to a second computer server. A system clock time on the first computer server is changed to differ from the system clock of the second computer server. Based on executing a test on the data analytics solution, a determination is made of whether the first software component, is wall-clock time independent. Responsive to the test of the of the software component failing indicating that the wall-clock time of the software component is dependent of the system clock time difference, the software component is recorded as wall-clock time dependent and an administrator is notified.

Testing and modifying calendar and event sensitive timer series data analytics

A method of analyzing physiological data streams. According to the method, physiological data is received into a computerized machine. The physiological data comprises numerical data and medical symptoms of a patient. Features are extracted from the physiological data based on development of the physiological data over a period of time. The features are converted into a textual representation using natural language generation. Input terms for an information retrieval system operating on the computerized machine are automatically generated based on the features. The input terms are input to the information retrieval system. A corpus of data is automatically searched to retrieve results to the input terms using the information retrieval system.

Hypothesis-driven, real-time analysis of physiological data streams using textual representations

Improved conductors and superconducting magnets are described utilizing superconducting materials exhibiting critical field anisotropy. This anisotropy is one in which the ability of the superconductor to stay in a superconducting state depends on the orientation of a magnetic field applied to the superconductor with respect to the direction of current flow in the superconductor. This anisotropy is utilized in the design of conductors and magnet windings comprising the superconductive material and specifically is directed to magnet windings in which the direction of high critical current through the superconductor is parallel to the magnetic field produced by current in these windings in order to obtain high critical fields. Particularly favorable examples of a superconducting material are the so-called high T superconductors in which the primary supercurrent flow is confined to 2 dimensional Cu&mdash;O planes. By arranging the superconductive windings so that the Cu&mdash;O planes are substantially parallel to the magnetic field produced by current in these windings, the windings will be able to withstand high fields without being driven normal. This maximizes the magnetic field amplitudes that can be produced by the magnet.

High current conductors and high field magnets using anisotropic superconductors

A semiconductor package is described for supporting and interconnecting semiconductor chips, each chip having contact lands on a contact surface, the package also including a substrate with a contact surface. An interposer module is disposed between at least one chip's contact surface and the substrate's contact surface. The interposer module has first and second opposed surfaces and a first plurality of contact locations positioned on its first surface which mate with a chip's contact land. A second plurality of contact locations on the interposer modules second surface are positioned to mate with contact lands on the substrate. A set of conductive vias are positioned within the interposer module and connect the first plurality of contact locations with a first subset of the second plurality of contact locations. A distributed capacitance layer is positioned within the interposer and is adjacent to its first surface. Adjacent to the second surface are X and Y lines which can be used to make engineering change interconnections.

Semiconductor chip interposer module with engineering change wiring and

An integrated circuit carrier comprising a modular substrate having an upper surface, a multitude of electrically conducting device terminals on the upper surface of the substrate, a multitude of electrically conducting engineering change pads also on the upper surface of the substrate, and an engineering change network to form a unique electrical connection between each of an arbitrary subset of the device terminals and each of an arbitrary subset of the engineering change pads. The engineering change network includes a multitude of connecting pads, and a multitude of first, second, and third conductive leads or wires, and each of the connecting pads includes first and second spaced apart sections.

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Croton-on-Hudson, NY, United States of America

Timothy Rea Dinger