International Business Machines Corporation

Lingtao Cao

Yuan-Chi Chang

Long H Vu

Venkata Nagaraju Pavuluri

Timothy Rea Dinger

Charu Chandra Aggarwal

Deepak Srinivas Turaga

Sunhwan Lee

Saket Sathe

Sarah E Knoop

Tan Hung M Ng

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 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

As received, a data value of an expected input set of received data values is missing from user input. A subset of known data with data values similar to a subset of the received data values is determined. A data sample average for the missing data value is determined from data values within the subset of the known data. An initial estimate of the missing data value is initialized using the data sample average. Boundary data clusters near the initial estimate of the missing data value are identified within the subset of the known data. A data harvesting region encapsulated according to the boundary clusters is defined. Data support clusters within at least one subset of the known data inside the data harvesting region are selected. The initial estimate of the missing data value is updated based upon data of the boundary clusters and the data support clusters.

Missing value imputation using adaptive ordering and clustering analysis

Embodiments for accurate temporal event predictive modeling by a processor. An average reverse event delay may be determined from one or more event delays in a time-series window. A time-series event may be predicted by applying the average reverse event delay in conjunction with one or more weighted factors in a predictive model.

Growing community of inventors

Hayward, CA, United States of America

Lingtao Cao