International Business Machines Corporation

Globalfoundries Inc.

Venkata Nagaraju Pavuluri

Long H Vu

Deepak Srinivas Turaga

Yuan-Chi Chang

Srinivasan Parthasarathy

Timothy Rea Dinger

Lingtao Cao

Nagui Halim

Daby Mousse Sow

Udayan Khurana

Sameep Mehta

Arvind Agarwal

Dharmashankar Subramanian

Saket Sathe

John Z Colt, Jr

Debarun Bhattacharjya

Rajmohan Chandrahasan

Ankush Gupta

Rodrigue Ngueyep Tzoumpe

Techniques for automatically determining different data types found in databases are disclosed. In one example, a computer implemented method comprises receiving a portion of identifying information for one or more components of a database, and generating one or more descriptions for the one or more components based at least in part on the portion of the identifying information for the one or more components. The one or more descriptions are inputted to one or more machine learning models, and, using the one or more machine learning models, one or more data types associated with the one or more components are predicted. The prediction is based at least in part on the one or more descriptions.

Automated classification of data types for databases

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

Split an input dataset into training and test datasets; the former includes a plurality of data examples, each represented as a feature vector, and having an associated true label. Split the training dataset into a plurality of training data subsets; for each, train a corresponding machine learning model to obtain a plurality of such models, and apply same to the test dataset to obtain a plurality of predicted labels and prediction scores. For each of the plurality of examples, compute an agreement metric based on a corresponding one of the associated true labels; corresponding ones of the predicted labels; and corresponding ones of the prediction scores. Based on the computed metric, select, for at least some of the true label values, appropriate ones of the data examples to be added to a regression set. Add the appropriate ones of the data examples from the test dataset to the regression set.

Automated data and label creation for supervised machine learning regression testing

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

An encoding system for encoding an event time series, the system including an inter-arrival time computing device configured to transform inter-arrival times between a plurality of input events into discrete time symbols and map the input events and the discrete time symbols using a dictionary to output a time gram representing a temporal dimension between a sequences of events.

Encoding for time grams

Embodiments relate to analyzing dataset. A method of analyzing data is provided. The method obtains a description of a dataset. The method automatically generates a plurality of analysis options from the description of the dataset. The method generates a plurality of queries based on the analysis options. The method deploys the queries on the dataset to build a plurality of statistical models from the dataset.

Automatic enumeration of data analysis options and rapid analysis of statistical models

A method, system, and computer program product derive data schema for application to a data set. One or more processors generate a directed acyclic weighted graph that encodes data types and semantic types used by a data set. One or more processors assign estimated frequencies for each component of the directed acyclic weighted graph, where the estimated frequencies predict a likelihood of a particular data schema element being used by any data set. One or more processors traverse through paths in the directed acyclic weighted graph with a predetermined portion of the data set to determine a data schema that correctly defines data from the data set and identifies any errors in the data set, and then apply the data schema to the data set to generate clean data that is properly formatted.

Schema discovery through statistical transduction

A computer-implemented method, system, and/or computer program product controls manufacturing devices in a manufacturing environment. One or more processors receive sensor readings, which detect conditions that are unique to different areas within the manufacturing environment, in order to generate models of operations for each area in the manufacturing environment. One or more processors generate an ensemble model by extracting information from the models to describe a relationship between the conditions. One or more processors generate a device control signal, based on the ensemble model, that adjusts operations in the different areas in order to ameliorate the detected conditions.

Use of multivariate models to control manufacturing operations

An encoding system for encoding an event time series, the system including an inter-arrival time computing device configured to compute an inter-arrival time between a plurality of input events and computes a sequence of events, a transformation device configured to transform the inter-arrival time between the plurality of input events into discrete time symbols, and a mapping device configured to map the input events and the discrete time symbols using a dictionary to output a time gram.

Growing community of inventors

New Rochelle, NY, United States of America

Venkata Nagaraju Pavuluri