Abstract: The present disclosure provides systems and methods for analyzing runs. In an aspect, the present disclosure provides a method for extracting semantic artifacts from manufacturing data. The method may comprise: (a) receiving manufacturing data corresponding to a manufacturing process, wherein the manufacturing data comprises at least one of observation data, context data, temporal metric data, and/or overall equipment effectiveness (OEE) components associated with the manufacturing process; (b) extracting one or more semantic artifacts from the manufacturing data; and (c) using the one or more semantic artifacts to generate a summary representation of the manufacturing process.

Abstract: The present disclosure provides systems and methods for inferring knowledge about manufacturing process metrics. In an aspect, the present disclosure provides a method for inferring knowledge about manufacturing process metrics. The method may comprise: (a) receiving one or more metrics associated with a manufacturing process; and (b) using a hierarchy of models to generate one or more inferences about the manufacturing process based on the one or more metrics, wherein the hierarchy of models comprises one or more individual models and one or more ensemble models configured to generate the one or more inferences based on a combination or an aggregation of outputs generated by the one or more individual models.

Abstract: Embodiments for automated feature engineering by one or more processors are described. One or more selected transformations may be applied to a set of features in a dataset to create a set of transform features using random feature transformation forest (RFTF) classifiers. A transform feature may be selected from the set of transform features having a highest discriminative power as compared to other features of the set of transform features. At each node in a decision tree, store the selected feature, a split value, and the one or more selected transformations for the transform feature.

Abstract: Embodiments for feature engineering by one or more processors are described. A plurality of transformations are applied to a set of features in each of a plurality of datasets. An output of each of the plurality of transformations is a score. For each of the sets of features, selecting those of the plurality of transformations for which said score is above a predetermined threshold. A signal representative of said selection is generated.

Abstract: Techniques for scenario planning are provided. In one example, a computer-implemented method can comprise analyzing, by a device operatively coupled to a processor, content using a topic model. The content can be associated with a defined source and is related to one or more current events. The computer-implemented method can also comprise determining, by the device, one or more portions of the analyzed content that are relevant to one or more key risk drivers using a risk driver model. The computer-implemented method can also comprise aggregating, by the device, the determined one or more portions into one or more emerging storylines based on values of one or more attributes of the topic model.

Abstract: Techniques for scenario planning are provided. In one example, a computer-implemented method can comprise analyzing, by a device operatively coupled to a processor, content using a topic model. The content can be associated with a defined source and is related to one or more current events. The computer-implemented method can also comprise determining, by the device, one or more portions of the analyzed content that are relevant to one or more key risk drivers using a risk driver model. The computer-implemented method can also comprise aggregating, by the device, the determined one or more portions into one or more emerging storylines based on values of one or more attributes of the topic model.

Abstract: 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.

Abstract: Embodiments described herein relate to a system for processing manufacturing data, comprising: an edge device; a cloud platform; a sensing device configured to collect manufacturing data and to provide the collected data to the edge device; wherein the edge device and the cloud platform are each configured to carry out a plurality of data processing functions on the manufacturing data; the system further comprising a resource manager configured to communicate with the edge device and with the cloud platform, wherein the resource manager is further configured to: determine whether each of the plurality of data processing functions is to be carried out at the edge device or at the cloud platform; if it is determined that the plurality of data processing functions is to be carried out at the edge device, instruct the edge device to carry out the plurality of data processing functions; if it is determined that the plurality of data processing functions is to be carried out at the cloud platform, instruct the clou

Abstract: Embodiments described herein relate to a system for processing manufacturing data, comprising: an edge device; a cloud platform; a sensing device configured to collect manufacturing data and to provide the collected data to the edge device; wherein the edge device and the cloud platform are each configured to carry out a plurality of data processing functions on the manufacturing data; the system further comprising a resource manager configured to communicate with the edge device and with the cloud platform, wherein the resource manager is further configured to: determine whether each of the plurality of data processing functions is to be carried out at the edge device or at the cloud platform; if it is determined that the plurality of data processing functions is to be carried out at the edge device, instruct the edge device to carry out the plurality of data processing functions; if it is determined that the plurality of data processing functions is to be carried out at the cloud platform, instruct the clou

Abstract: Embodiments for automated feature engineering by one or more processors are described. One or more selected transformations may be applied to a set of features in a dataset to create a set of transform features using random feature transformation forest (RFTF) classifiers. A transform feature may be selected from the set of transform features having a highest discriminative power as compared to other features of the set of transform features. At each node in a decision tree, store the selected feature, a split value, and the one or more selected transformations for the transform feature.

Abstract: A system, method, and computer program product for automatically selecting from a plurality of analytic algorithms a best performing analytic algorithm to apply to a dataset is provided. The automatically selecting from the plurality of analytic algorithms the best performing analytic algorithm to apply to the dataset enables a training a plurality of analytic algorithms on a plurality of subsets of the dataset. Then, a corresponding prediction accuracy trend is estimated across the subsets for each of the plurality of analytic algorithms to produce a plurality of accuracy trends. Next, the best performing analytic algorithm is selected and outputted from the plurality of analytic algorithms based on the corresponding prediction accuracy trend with a highest value from the plurality of accuracy trends.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system, method, and computer program product for automatically selecting from a plurality of analytic algorithms a best performing analytic algorithm to apply to a dataset is provided. The automatically selecting from the plurality of analytic algorithms the best performing analytic algorithm to apply to the dataset enables a training a plurality of analytic algorithms on a plurality of subsets of the dataset. Then, a corresponding prediction accuracy trend is estimated across the subsets for each of the plurality of analytic algorithms to produce a plurality of accuracy trends. Next, the best performing analytic algorithm is selected and outputted from the plurality of analytic algorithms based on the corresponding prediction accuracy trend with a highest value from the plurality of accuracy trends.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method for performing assisted knowledge discovery includes receiving a dataset. Each of a plurality of analytical techniques is applied to the received data set to generate a plurality of corresponding analytical results. A composite validation metric is applied to each of the plurality of analytical results. The composite validation metric is a single scoring/ranking function that is created from a plurality of different scoring/ranking functions. The plurality of analytical results is presented to a user arranged in accordance with the results of the applying the composite validation metric to each of the plurality of analytical results. A selection from the user from among the plurality of analytical results is recorded. The user's selection is used to modify the composite validation metric such that the analytical techniques responsible for generating the selected analytical result is scored/ranked more highly.

Abstract: Embodiments for feature engineering by one or more processors are described. A plurality of transformations are applied to a set of features in each of a plurality of datasets. An output of each of the plurality of transformations is a score. For each of the sets of features, selecting those of the plurality of transformations for which said score is above a predetermined threshold. A signal representative of said selection is generated.