Abstract: Methods, computer program products and/or systems are provided that perform the following operations: obtaining a performance matrix representing accuracies obtained by executing a plurality of pipelines on a plurality of training data sets, wherein a pipeline comprises a series of operations performed on a data set; selecting a defined number of top pipelines as potential pipelines for a testing data set based, at least in part, on a similarity between the testing data set and each of the plurality of training data sets represented in the performance matrix; storing results from executing each of the potential pipelines as a new data set; determining a pipeline accuracy for each of the potential pipelines when executed against the testing data set; and providing a recommended pipeline for use with the testing data set based, at least in part, on the pipeline accuracy for each potential pipeline.

Abstract: A method, system, and computer program product for a metalearner for automated machine learning are provided. The method receives a labeled data set. A set of data subsets is generated from the labeled data set. A set of unsupervised machine learning pipelines is generated. A training set is generated from the set of data subsets and the set of unsupervised machine learning pipelines. The method trains a metalearner for unsupervised tasks based on the training set.

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: Methods, computer program products and/or systems are provided that perform the following operations: obtaining a performance matrix representing accuracies obtained by executing a plurality of pipelines on a plurality of training data sets, wherein a pipeline comprises a series of operations performed on a data set; selecting a defined number of top pipelines as potential pipelines for a testing data set based, at least in part, on a similarity between the testing data set and each of the plurality of training data sets represented in the performance matrix; storing results from executing each of the potential pipelines as a new data set; determining a pipeline accuracy for each of the potential pipelines when executed against the testing data set; and providing a recommended pipeline for use with the testing data set based, at least in part, on the pipeline accuracy for each potential pipeline.

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 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 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 includes obtaining a graph representative of a given network, sampling the graph a given number of times to estimate a level of noisiness for one or more edges in the graph, and annotating the one or more edges of the graph with the respective level of noisiness.

Abstract: Embodiments of the invention include method, systems and computer program products for providing ease-of-drive driving directions. The computer-implemented method includes receiving, by a processor, a request for a route from a starting point to a destination point. The processor calculates one or more routes from the starting point to the destination point. The processor scores the one or more calculated routes according to ease-of-drive driving criteria. The processor presents at least one of the scored calculated routes that are below a predetermined threshold.

Abstract: Embodiments of the invention include method, systems and computer program products for providing ease-of-drive driving directions. The computer-implemented method includes receiving, by a processor, a request for a route from a starting point to a destination point. The processor calculates one or more routes from the starting point to the destination point. The processor scores the one or more calculated routes according to ease-of-drive driving criteria. The processor presents at least one of the scored calculated routes that are below a predetermined threshold.

Abstract: A method (and system) for supervised network clustering includes receiving and reading node labels from a plurality of nodes on a network, as executed by a processor on a computer having access to the network, the network defined as a group of entities interconnected by links. The node labels are used to define densities associated with the nodes. Node components are extracted from the network, based on using thresholds on densities. Smaller components having a size below a user-defined threshold are merged.

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: Mechanisms are provided for anonymizing data comprising a plurality of graph data sets. The mechanisms receive input data comprising a plurality of graph data sets. Each graph data set comprises data for generating a separate graph from graphs associated with other graph data sets. The mechanisms perform clustering on the graph data sets to generate a plurality of clusters. At least one cluster of the plurality of clusters comprises a plurality of graph data sets. Other clusters in the plurality of clusters comprise one or more graph data sets. The mechanisms also determine, for each cluster in the plurality of clusters, aggregate properties of the cluster. Moreover, the mechanisms generate, for each cluster in the plurality of clusters, pseudo-synthetic data representing the cluster, from the determined aggregate properties of the clusters.

Abstract: A system that labels an unlabeled message of a social stream. The system including a memory device storing instructions to execute a training model, the training model being trained based on labeled messages, and partitioned into a plurality of class partitions, each of which comprise statistical information and a class label, and a Central Processing Unit (CPU) that computes a confidence for each of the class partitions based on information of an unlabeled message and the statistical information of a respective class partition, and that labels the unlabeled message according to respective confidences of the class partitions.

Abstract: A method includes obtaining a graph stream, obtaining historical data of one or more nodes associated with the graph stream, extracting one or more features from the graph stream for one or more nodes, and creating one or more alarm levels for the one or more nodes based on the one or more extracted features and the historical data.

Abstract: Mechanisms are provided for anonymizing data comprising a plurality of graph data sets. The mechanisms receive input data comprising a plurality of graph data sets. Each graph data set comprises data for generating a separate graph from graphs associated with other graph data sets. The mechanisms perform clustering on the graph data sets to generate a plurality of clusters. At least one cluster of the plurality of clusters comprises a plurality of graph data sets. Other clusters in the plurality of clusters comprise one or more graph data sets. The mechanisms also determine, for each cluster in the plurality of clusters, aggregate properties of the cluster. Moreover, the mechanisms generate, for each cluster in the plurality of clusters, pseudo-synthetic data representing the cluster, from the determined aggregate properties of the clusters.

Abstract: Mechanisms are provided for anonymizing data comprising a plurality of graph data sets. The mechanisms receive input data comprising a plurality of graph data sets. Each graph data set comprises data for generating a separate graph from graphs associated with other graph data sets. The mechanisms perform clustering on the graph data sets to generate a plurality of clusters. At least one cluster of the plurality of clusters comprises a plurality of graph data sets. Other clusters in the plurality of clusters comprise one or more graph data sets. The mechanisms also determine, for each cluster in the plurality of clusters, aggregate properties of the cluster. Moreover, the mechanisms generate, for each cluster in the plurality of clusters, pseudo-synthetic data representing the cluster, from the determined aggregate properties of the clusters.

Abstract: A method includes obtaining a graph stream, obtaining historical data of one or more nodes associated with the graph stream, extracting one or more features from the graph stream for one or more nodes, and creating one or more alarm levels for the one or more nodes based on the one or more extracted features and the historical data.

Abstract: A method includes obtaining a graph representative of a given network, sampling the graph a given number of times to estimate a level of noisiness for one or more edges in the graph, and annotating the one or more edges of the graph with the respective level of noisiness.