Abstract: Embodiments for implementing enhanced ensemble model diversity and learning by a processor. One or more data sets may be created by combining one or more clusters of data points of a minority class with selected data points of a majority class. One or more ensemble models may be created from the one or more data sets using a supervised machine learning operation. An occurrence of an event may be predicted using the one or more ensemble models.

Abstract: Embodiments for implementing enhanced ensemble model diversity and learning by a processor. One or more data sets may be created by combining one or more clusters of data points of a minority class with selected data points of a majority class. One or more ensemble models may be created from the one or more data sets using a supervised machine learning operation. An occurrence of an event may be predicted using the one or more ensemble models.

Abstract: An automated outlier detection system implements an unsupervised set of processes to determine feature subspaces from a dataset; determine candidate exploratory actions, where each candidate exploratory action is a specific combination of a feature subspace and a parameterized instance of an outlier detection algorithm; and identify a set of optimal exploratory actions to recommend for execution on the dataset from among the candidate exploratory actions. Outlier scores obtained as a result of execution of the set of optimal exploratory actions are processed to obtain one or more outlier views such that each outlier view represents a consistent characterization of outliers by each optimal exploratory action corresponding to that outlier view.

Abstract: A network security apparatus includes a packet detector detecting transmission of data packets between a plurality of hosts and a plurality of domains and defining a plurality of links therefrom. A model builder circuit receives the plurality of links from the packet detector, receives ground truth information labeling one or more of the plurality of hosts or one or more of the plurality of domains as benign or malicious, generates predictive models from the received links and ground truth information, and stores generated predictive models in a predictive model database. An anomaly detector circuit retrieves the generated predictive models from the predictive model database and uses the predictive models to label each of the plurality of hosts and plurality of domains, that have not previously been labeled by the ground truth information, as benign or malicious.

Abstract: A computer program product is provided for estimating algorithm run times given parameters of the algorithm, specifications of an architecture on which the algorithm will execute and dimensions of a data set which will be input into the algorithm. The computer program product includes instructions to cause a processing circuit to create training data sets, generate run time data of executions of instances of the algorithm on the architecture for each training data set, identify model-usable features, generate a map of the model-usable features to an expression of the run time data and iteratively tuning the model-usable features toward improving map accuracy until a target map accuracy is achieved, develop a predictive model based on iteratively tuned versions of the model-usable features and estimate a run time of an execution of the algorithm on a new data set and on a new architecture using the predictive model.

Abstract: An automated outlier detection system implements an unsupervised set of processes to determine feature subspaces from a dataset; determine candidate exploratory actions, where each candidate exploratory action is a specific combination of a feature subspace and a parameterized instance of an outlier detection algorithm; and identify a set of optimal exploratory actions to recommend for execution on the dataset from among the candidate exploratory actions. Outlier scores obtained as a result of execution of the set of optimal exploratory actions are processed to obtain one or more outlier views such that each outlier view represents a consistent characterization of outliers by each optimal exploratory action corresponding to that outlier view.

Abstract: An automated outlier detection system implements an unsupervised set of processes to determine feature subspaces from a dataset; determine candidate exploratory actions, where each candidate exploratory action is a specific combination of a feature subspace and a parameterized instance of an outlier detection algorithm; and identify a set of optimal exploratory actions to recommend for execution on the dataset from among the candidate exploratory actions. Outlier scores obtained as a result of execution of the set of optimal exploratory actions are processed to obtain one or more outlier views such that each outlier view represents a consistent characterization of outliers by each optimal exploratory action corresponding to that outlier view.

Abstract: A network security apparatus includes a packet detector detecting transmission of data packets between a plurality of hosts and a plurality of domains and defining a plurality of links therefrom. A model builder circuit receives the plurality of links from the packet detector, receives ground truth information labeling one or more of the plurality of hosts or one or more of the plurality of domains as benign or malicious, generates predictive models from the received links and ground truth information, and stores generated predictive models in a predictive model database. An anomaly detector circuit retrieves the generated predictive models from the predictive model database and uses the predictive models to label each of the plurality of hosts and plurality of domains, that have not previously been labeled by the ground truth information, as benign or malicious.

Abstract: A computer program product is provided for estimating algorithm run times given parameters of the algorithm, specifications of an architecture on which the algorithm will execute and dimensions of a data set which will be input into the algorithm. The computer program product includes instructions to cause a processing circuit to create training data sets, generate run time data of executions of instances of the algorithm on the architecture for each training data set, identify model-usable features, generate a map of the model-usable features to an expression of the run time data and iteratively tuning the model-usable features toward improving map accuracy until a target map accuracy is achieved, develop a predictive model based on iteratively tuned versions of the model-usable features and estimate a run time of an execution of the algorithm on a new data set and on a new architecture using the predictive model.

Abstract: An automated outlier detection system implements an unsupervised set of processes to determine feature subspaces from a dataset; determine candidate exploratory actions, where each candidate exploratory action is a specific combination of a feature subspace and a parameterized instance of an outlier detection algorithm; and identify a set of optimal exploratory actions to recommend for execution on the dataset from among the candidate exploratory actions. Outlier scores obtained as a result of execution of the set of optimal exploratory actions are processed to obtain one or more outlier views such that each outlier view represents a consistent characterization of outliers by each optimal exploratory action corresponding to that outlier view.

Abstract: An automated outlier detection system implements an unsupervised set of processes to determine feature subspaces from a dataset; determine candidate exploratory actions, where each candidate exploratory action is a specific combination of a feature subspace and a parameterized instance of an outlier detection algorithm; and identify a set of optimal exploratory actions to recommend for execution on the dataset from among the candidate exploratory actions. Outlier scores obtained as a result of execution of the set of optimal exploratory actions are processed to obtain one or more outlier views such that each outlier view represents a consistent characterization of outliers by each optimal exploratory action corresponding to that outlier view.

Abstract: A horizontal anomaly detection method includes receiving at plurality of objects described in a plurality of information sources, wherein each individual information source captures a plurality of similarity relationships between the objects, combining the information sources to determine a similarity matrix whose entries represent quantitative scores of similarity between pairs of the objects, and identifying at least one horizontal anomaly of the objects within the similarity matrix, wherein the horizontal anomalies are anomalous relationships across the plurality of information sources.

Abstract: A method for automated data exploration including selecting a plurality of analytic flows from an analytic flow pattern, executing a task, wherein the task is tracked by the plurality of analytic flows, receiving feedback for each of the plurality of analytic flows, determining a performance score for each of the plurality of analytic flows, and adjusting the flow according to the performance score.

Abstract: A horizontal anomaly detection method includes receiving at plurality of objects described in a plurality of information sources, wherein each individual information source captures a plurality of similarity relationships between the objects, combining the information sources to determine a similarity matrix whose entries represent quantitative scores of similarity between pairs of the objects, and identifying at least one horizontal anomaly of the objects within the similarity matrix, wherein the horizontal anomalies are anomalous relationships across the plurality of information sources.

Abstract: A method, system and computer program product for matching an entry of a list to data. The method includes the steps of: generating source code which matches at least a part of said list with said data; compiling said source code into binary code; and determining a match between said list and said data using said compiled binary code wherein at least one of the steps is carried out using a computer device.

Abstract: A horizontal anomaly detection method includes receiving at plurality of objects described in a plurality of information sources, wherein each individual information source captures a plurality of similarity relationships between the objects, combining the information sources to determine a similarity matrix whose entries represent quantitative scores of similarity between pairs of the objects, and identifying at least one horizontal anomaly of the objects within the similarity matrix, wherein the horizontal anomalies are anomalous relationships across the plurality of information sources.

Abstract: A horizontal anomaly detection method includes receiving at plurality of objects described in a plurality of information sources, wherein each individual information source captures a plurality of similarity relationships between the objects, combining the information sources to determine a similarity matrix whose entries represent quantitative scores of similarity between pairs of the objects, and identifying at least one horizontal anomaly of the objects within the similarity matrix, wherein the horizontal anomalies are anomalous relationships across the plurality of information sources.

Abstract: A method, system and computer program product for matching an entry of a list to data. The method includes the steps of: generating source code which matches at least a part of said list with said data; compiling said source code into binary code; and determining a match between said list and said data using said compiled binary code wherein at least one of the steps is carried out using a computer device.

Abstract: A system, method and computer program product for classification of an analog electrical signal using statistical models of training data. A technique is described to quantize the analog electrical signal in a manner which maximizes the compression of the signal while simultaneously minimizing the diminution in the ability to classify the compressed signal. These goals are achieved by utilizing a quantizer designed to minimize the loss in a power of the log-likelihood ratio. A further technique is described to enhance the quantization process by optimally allocating a number of bits for each dimension of the quantized feature vector subject to a maximum number of bits available across all dimensions.

Abstract: A method (and system) of transferring data of multimedia objects from servers to clients using memory in video-on-demand clusters, includes dividing multimedia objects into segments and independently serving and caching the segments on a plurality of servers, retrieving the segments from the servers, directing the client to the server that is responsible for serving the at least one of the segments using a redirection algorithm, and predicting a likely sequence of segments that will be requested by the client and prefetching data from a next segment in the likely sequence of segments and loading the next segment into memory within one of the plurality of servers.