Abstract: An approach to time-series data point anomaly detection is presented. Data point anomalies in time-series data can cause a cascade of incorrect predictions in a time-series data prediction model. Presented herein is an approach to decompose a time-series training data set into elementary components, such as seasonal, trend and residual. The approach determines one or more confidence intervals for elementary components of data points including level shift, variance, and outlier. From these confidence intervals, new data points are analyzed and identified as anomaly data points. The approach also prevents anomaly data points from being incorporated into a time series data prediction model, reducing prediction error in the prediction model.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) identifies a query with low performance. The program code generates a small data environment for use in optimizing the query. The program code identifies table(s) and field(s) related to the query with low performance. The program code samples a portion of each table of the one or more tables based on, for each table, parameters of the one or more fields in each table, where the portion sampled comprises records from each table with common data traits to a whole of each table. The program code generates a small data environment comprising the portion of each table. The program code performance tests an optimized version of the query by executing it on the small data environment.

Abstract: A process includes receiving a set of static characteristics, a set of dynamic characteristics, and a health objective of a target object. A set of reference object data is retrieved and includes multiple reference objects of the same type as the target object. The reference object data includes the same data as the target object data. The dynamic data of the target object and the reference objects is parameterized over time. Each a set of dynamic characteristics for each reference object is split into a plurality of reference profiles. A set of similar reference profiles having a higher similarity metric than a remainder of the reference profiles is identified. A goal difference between the health objective of the target object and an end state of the similar reference profiles is determined. The reference profile having the smallest goal difference is identified and a corresponding health regimen is implemented.

Abstract: Embodiments of the present disclosure relate to articles, coated articles, and methods of coating such articles with a corrosion resistant coating. The corrosion resistant coating can comprise hafnium aluminum oxide. The corrosion resistant coating may be deposited by a non-line of sight deposition, such as atomic layer deposition. Articles that may be coated may include chamber components, such as gas lines.

Abstract: An assembling equipment for performing asynchronous process on a first workpiece and a second workpiece. The assembling equipment includes a base and a conveying mechanism on the base. The conveying mechanism transports a first positioning assembly carrying a first workpiece and a second positioning assembly carrying a second workpiece to a first station synchronously, and a first attaching mechanism attaches a first component to the first workpiece at the first station. Then the conveying mechanism transports the first positioning assembly and the second positioning assembly to a second station synchronously, a second attaching mechanism attaches a second component to the second workpiece, and a first processing mechanism processes first workpiece that has been attached with the first component according to a preset requirement at the same time. The assembling equipment improves efficiency of production.

Abstract: A computer-implemented method for anomaly detection for a time series data is provided. Aspects include receiving a time series data including a plurality of sequential data points, calculating an expected next value for the time series data based on the plurality of sequential data points, and receiving an actual next value corresponding to the time series data. Aspects also include calculating an anomaly strength estimate based on the expected next value and the actual next value, identifying one of a plurality of anomaly detection pipelines based on the anomaly strength estimate and a portrait associated with each of the plurality of anomaly detection pipelines, and obtaining an anomaly prediction by inputting the time series data and the actual next value into the one of the plurality of anomaly detection pipelines.

Abstract: A joint assembly, a swing device and a robot are provided. The joint assembly includes a movable part defining a movable groove and a rotating part. The rotating part includes a rotating body being a spherical shape. The rotating body is rotatably received in the movable groove to adapt to the movable part. The rotating part defines an air hole with an air inlet and an air outlet. The air inlet is configured to receive introduced air, and the air outlet is configured to expel the air to the movable groove.

Abstract: A method for improving a query performance of a query of a database application using rewrite includes introducing a database query into a database application to obtain a query result, analyzing the database query to identify index columns and a predicate column; identifying associated columns by determining if any associations exist between the index columns and the predicate column, calculating a confidence score value for each of the associated columns responsive to the associated columns and the predicate column, generating a list of associated columns with the confidence score values, generating a rewritten query by rewriting the database query based on the list of associated columns and the confidence score values and validating the rewritten query.

Abstract: A computer-implemented method for treating post-modeling data includes computing, sequentially for each category of a feature, a category importance (CI) value. The CI value is based on a model accuracy change when records of a category being examined are reassigned to a remaining set of categories of the feature according to a cumulative distribution of records among the remaining set of categories of the feature, wherein the remaining set of categories include all categories of the feature, except for the category being examined. A post-modeling category is performed to merge of each category having the CI value less than a CI value threshold.

Abstract: A computer-implemented method for model selection is provided. The computer-implemented method includes building models for predicting characteristics of pipeline models, receiving user specifications for pipeline model performance, defining a metric for weighing the characteristics of the pipeline models, using the metric to iteratively reduce a number of the pipeline models capable of meeting the user specifications to a reduced number of the pipeline models, determining which one pipeline model of the reduced number of the pipeline models exhibits a best capability of meeting the user specifications and deploying the one pipeline model.

Abstract: An approach to time-series data point anomaly detection may be presented. Data point anomalies in time-series data can cause a cascade of incorrect predictions in a time-series data prediction model. Presented herein may be an approach to decompose a time-series training data set into elementary components, such as seasonal, trend and residual. The approach may determine one or more confidence intervals for elementary components of data points including level shift, variance, and outlier. From these confidence intervals, new data points can be analyzed and identified as anomaly data points. The approach may also prevent anomaly data points from being incorporated into a time series data prediction model, reducing prediction error in the prediction model.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to determining time series model stability and robustness in refreshment. The computer-implemented system can comprise a memory that can store computer executable components. The computer-implemented system can further comprise a processor that can execute the computer executable components stored in the memory, wherein the computer executable components can comprise a computation component that can employ weighted model evaluation to compute stability of time series pipelines over respective holdout datasets and a determination component that can select, based on the computed pipeline stabilities, a most stable time series pipeline.

Abstract: In an approach for post-modeling data visualization and analysis, a processor presents a first visualization of a training dataset in a first plot. Responsive to receiving a selection of a data group of the training dataset to analyze, a processor identifies three or fewer key model features of the data group of the training dataset. A processor ascertains a representative record of each key model feature of the three or fewer key model features using a Local Interpretable Model-Agnostic Explanation technique. A processor presents a second visualization of the three or fewer key model features and the representative record of each key model feature in a second plot.

Abstract: Determining whether synthetic data is sufficient for utilization in connection with one or more machine learning models. The computing device accesses a protected batch of data associated with a machine learning model. The computing device accesses a simulated batch of data, the simulated batch of data based upon but anonymizing the protected batch of data. The computing device accesses one or more comparisons of one or more variables in the protected batch of data and the simulated batch of data to obtain a similarity value. The computing device performs a machine learning function utilizing at least in-part the simulated batch of data if the similarity value exceeds a similarity threshold.

Abstract: A receiving structure providing real-time information as to the materials constituting its contents includes a box body, a first side plate, and a second side plate. The box body, the first side plate, and the second side plate forming a receiving space, and the receiving space receives materials. A discharge port is formed between the second side plate and the first side plate, the discharge port is in communication with the receiving space, and the discharge port is configured to take out the material. A material checking unit is provided on the first side plate, the material checking unit detects and enables real time identification of the materials in the receiving structure.

Abstract: An embodiment configures a plurality of parameters, the parameters being usable to generate artificial data from original data, the configuring adjusting a level of privacy in the artificial data. An embodiment fits a distribution type to a variable of the original data. An embodiment adjusts, using a desired level of privacy and the distribution type, a level of noise, wherein the level of noise corresponds to the desired level of privacy. An embodiment generates, using the distribution type and the level of noise, the artificial data, the artificial data achieving the desired level of privacy by including noise data corresponding to the level of noise.

Abstract: A computer-implemented method for anomaly detection for a time series data is provided. Aspects include receiving a time series data including a plurality of sequential data points, calculating an expected next value for the time series data based on the plurality of sequential data points, and receiving an actual next value corresponding to the time series data. Aspects also include calculating an anomaly strength estimate based on the expected next value and the actual next value, identifying one of a plurality of anomaly detection pipelines based on the anomaly strength estimate and a portrait associated with each of the plurality of anomaly detection pipelines, and obtaining an anomaly prediction by inputting the time series data and the actual next value into the one of the plurality of anomaly detection pipelines.

Abstract: Embodiments analyze a query pattern of an incoming query on a database, perform a semantic analysis of the query pattern of the incoming query, generate a re-write query that has an improved query performance in comparison to a query performance of the incoming query based on the analyzed query pattern and the semantic analysis; build a query model using machine learning based on at least one of the query pattern and the semantic analysis; and apply the re-write query by performing the re-write query on the database to provide the improved query performance.

Abstract: A computer-implemented method for generating an artificial data set is provided. Aspects include obtaining an input data set, calculating an association between the plurality of categorical variables of the input data set, and creating, based on the association, a plurality of clusters of categorical variables. Aspects also include identifying a key variable for each of the plurality of clusters of categorical variables, creating a key cluster for each of the plurality of clusters, and creating a cluster contingency table for each of the clusters. Aspects further include generating, based on the cluster contingency table for each of the plurality of clusters and for the key cluster, a data set for each of the plurality of clusters and the key cluster and generating the artificial data set based on a combination of the data set for each of the plurality of clusters and the key cluster.

Abstract: In an approach for optimizing abnormal point detection, a processor receives a set of data, wherein the set of data is partially labeled time series data; determines a data block size for the set of data; splits the set of data into data blocks based on the data block size; computes trait measurements for traits for each data block; assigns a tag to each data block, wherein the tag is selected from the group consisting of a normal tag, an abnormality tag, and an unknown tag; uses the respective data blocks with either the normal tag or the abnormality tag as training data; updates the training data with artificial abnormalities; trains a detection model with the updated training data; and utilizes the trained detection model to predict whether the respective data blocks with the unknown tag have an abnormality or no abnormality.