Abstract: A computing system for querying multiple data sources and method therefor is provided. The computing system may comprise one or more nodes in communication with at least one data source of the multiple data sources to access data therefrom. The computing system may further comprise a second node in communication with the one or more nodes. The second node may be configured to receive a query instance and process the query instance to generate one or more relational query instances. The one or more relational query instances may be distributed among the nodes to extract data from the at least one data source in communication therewith corresponding to the respective one or more relational query instances. The second node may be further configured to receive extracted data from each of the one or more nodes queried. The second node may be further configured to aggregate the extracted data.

Abstract: Performing predictive analysis on running batch jobs is provided. A series of batch end time predictive models is retrieved according to a sequence of milestone jobs in a batch of jobs. Retrieved batch end time predictive models are assembled into an aggregate batch end time predictive model to increase accuracy and stability of an end time prediction of the batch of jobs. The aggregate batch end time predictive model is utilized to predict an end time of the batch of jobs during running of the batch of jobs to form a predicted end time of the batch of jobs.

Abstract: A method for identifying influential effects that contribute most to a status change of a target index for goal seeking analysis. The method includes generating a candidate list of significant changed predictors between the normal and abnormal status time periods in collected data, and building a plurality of regression models from the collected data. The method determines a first value (trend value or Pearson correlation value) for each of the significant changed predictors based on whether at least one of the significant changed predictors have a significant change trend using the regression models. The method obtains a second predictor importance value for each of the significant changed predictors from a single model built on all the collected data. The method generates a final predictor value for each of the significant changed predictors by combining the first value with the second predictor importance value for each of the significant changed predictors.

Abstract: A computer implemented method manages an ensemble model system to classify records. A number of processor units cluster records into groups of records based on classification predictions generated by base models in the ensemble model system for the records. The number of processor units determines sets of weights for the base models that increase a probability that the base models in the ensemble model system correctly predict the groups of records. Each set of weights in the sets of weights is associated with a group of records in the groups of records.

Abstract: One or more computer processors create a respective coincidence matrix for each component model of a deployed ensemble model. The one or more computer processors generate evaluation measures for each component model comprised in the deployed ensemble model. The one or more computer processors responsive to the respective coincidence matrix failing to exceed a record threshold, update the respective coincidence matrix with the generated evaluation measures. The one or more computer processors responsive to the respective updated coincidence matrix failing to exceed a disable cut point, flag the respective component model. The one or more computer processors responsive to the coincidence matrix associated with the flagged component model not exceeding an elimination cut point, remove the respective component model from the deployed ensemble model.

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: A method, computer system, and a computer program product are provided for post-modeling feature evaluation. In one embodiment, at least at least one post model visual output and associated data is obtained that at least includes an individual conditional expectation (ICE) plot and a partial dependence (PDP) plot. Using the associated data and the plots, a Feature Importance (PI) plot is provided. A plurality of features is then determined for each PI, PDP and ICE plots to calculate at least one Interesting Value for each plot. An overall score is also calculated for each plurality of features based on the associated Interesting Values for each PDP, ICE and PI plots. At least one top feature is selected based on said scores. A final plot is then generated at least reflecting the top feature. The final plot combines the PI, PDP and ICE plots together.

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 computing system for querying multiple data sources and a method therefor is provided. The computing system may comprise one or more nodes in communication with at least one data source of the multiple data sources to access data therefrom. The computing system may further comprise a second node in communication with the one or more nodes. The second node may be configured to receive a query instance and process the query instance to generate one or more relational query instances. The one or more relational query instances may be distributed among the one or more nodes to extract data from the at least one data source in communication therewith corresponding to the respective one or more relational query instances. The second node may be further configured to receive extracted data from each of the one or more nodes queried. The second node may be further configured to aggregate the extracted data.

Abstract: An approach is provided in which the approach builds a combination model that includes a normal status model and an abnormal status model. The normal status model is built from a set of time-sequenced normal status records and the abnormal status model is built from a set of time-sequenced abnormal status records. The approach computes a set of time-sequenced coefficient combination values of the normal status model and the abnormal status model based on applying a set of fitting coefficient characteristics to the normal status model and the abnormal status model. The approach performs goal seek analysis on a system using the combination model and the set of time-sequenced coefficient combination values.

Abstract: A routing information management method adapted for a layer-3 switch is proposed. For each ARP entry in an ARP table, a processor assigns to the ARP entry an aging reference parameter that is identical to a quantity of those routing entries in an IP routing table whose next hop IP addresses are identical to the IP address of the ARP entry, and removes those ARP entries whose aging reference parameters are zero. For each routing entry in the IP routing table, when the next hop IP address of the routing entry is absent from the ARP table, the processor makes the routing entry serve as an inactive routing entry, proactively sends an ARP request for the inactive routing entry, and updates the ARP table based on a piece of ARP information received externally.

Abstract: A computer-implemented method includes receiving an initial request from a client, splitting the initial request into a plurality of split requests based on a business logic, acquiring mapping information associated with the plurality of split requests, determining, based, at least in part, on the mapping information, respective split requests included in the plurality of split requests that are candidates for merger into a merged request, merging the respective split requests that are determined to be candidates for merger into one or more merged requests, and sending the one or more merged requests to one or more nodes capable of processing the one or more merged requests.

Abstract: Detecting anomalous data by applying a plurality of models to a data set to yield detection results including anomalous data, applying evaluation methods to the detection results for each of the plurality of models, determining a combined score for the detection results according to the evaluation methods, determining a combined score threshold, and defining a set of detected anomalies according to the combined score and the combined score threshold.

Abstract: An approach is provided in which the approach generates a parallel chart based on multiple records that includes a set of variable values corresponding to a set of variables. To generate the parallel chart, the approach arranges the set of variables on the parallel chart in a variable order based on at least one variable arrangement rule. The approach arranges an initial variable value order for each one of the set of variables, and computes a lucidity score based on the variable order and the initial variable value order of each of the set of variables. The lucidity score is a measurement of the clarity of the parallel chart. The approach adjusts the variable value order of at least one of the set of variables to increase the lucidity score and optimizes the clarity of the parallel chart based on the adjusted variable value order.

Abstract: Automated development and training of deep forest models for analyzing data by growing a random forest of decision trees using data, determining Out-of-bag (OOB) predictions for the forest, appending the OOB predictions to the data set, and growing an additional forest using the data set including the appended OOB predictions, and combining the output of the additional forest, then utilizing the model to classify data outside the training data set.

Abstract: A processing method that is performed by one or more processor is provided. The processing method includes determining a target video frame in a currently captured video; determining an object area in the target video frame based on a box selection model; determining a category of a target object in the object area based on a classification model used to classify an object in the object area; obtaining augmented reality scene information associated with the category of the target object; and performing augmented reality processing on the object area in the target video frame and the augmented reality scene information, to obtain the augmented reality scene.

Abstract: Disclosed are an intelligent bionic human body part model detection device and a method for manufacturing same. The device comprises: a bionic human body part model (1); and multiple optical fiber grating sensing units (5) which are integrated on an optical fibre and arranged at multiple pre-determined positions of the bionic human body part model (1). The device can improve the accuracy of the detection of pressure applied to the intelligent bionic human body part model.

Abstract: A cleaning device includes a host, a drag head assembly and a water spray assembly. The host includes a housing, a control device housed in the housing and a handle member fixedly connected to the housing. The housing contains a water inlet pipe for connecting to a water source. The drag head assembly is fixed on the housing and electrically connected to the control device. The control device is used to control the drag head assembly for cleaning operations. The drag head assembly includes a cleaning brush head. The water spray assembly is fixed in the drag head assembly and connected to the end of the water inlet pipe away from the water source. The water spray assembly has evenly set spray holes. The direction of the spray holes is towards the cleaning brush head.

Abstract: Embodiments of the present disclosure relate to a method, system, and computer program product for predictive models. According to the method, a processor may provide a first list including at least one input variable of a predictive model and a second list including a plurality of variables of the predictive model. For each of input variables in the second list, the processor may determine contribution of the input variable to prediction of the predictive model with respect to the at least one input variable in the first list. The processor may update the first list by moving an input variable in the second list into the first list based on the determined contribution of the plurality of input variables. The processor may render one or more of input variables in the updated first list based on an order of the input variables in the updated first list.

Abstract: Provided are a computer-implemented method, a system, and a computer program product. The method comprises extracting features from a plurality of base models in an ensemble model. The plurality of base models are configured to provide respective prediction results. The ensemble model is configured to provide an overall prediction result from the prediction results of the plurality of base models. The features are associated with time performance of the base models. The method further comprises clustering the plurality of base models into a plurality of clusters based on the extracted features. The method further comprises assigning the plurality of base models to a plurality of parallel computation units based on the plurality of clusters.