Abstract: Business goals may be achieved using adaptive rewarding for the personalization of contents. In response to receiving user information, personalized contents for the user can be recommended using a reinforcement learning algorithm. In response to presenting the personalized content to the user, an action by the user selecting a particular content may be received. A reward value can be calculated for the action based on a reward function. The reward function can be based, at least in part, upon the action, the selected content, and/or the user. The reward function can be based upon one or more business goals, such as user engagement, monetization, and/or security. The calculated reward value can be provided to the reinforcement learning algorithm, which can be adapted based upon the reward value for future selection of personalized contents.

Abstract: A multivariate clustering-based anomaly detector can generate an event for consumption by an APM manager that indicates detection of an anomaly based on multivariate clustering analysis after topology-based feature selection. The anomaly detector accumulates time-series data across a series of time instants to form a multivariate time-series data slice or multivariate data slice. The anomaly detector then performs multivariate clustering analysis with the multivariate data slice. The anomaly detector determines whether a multivariate data slice is within a cluster of multivariate data slices. If the multivariate data slice is within the cluster and the cluster is a known anomaly cluster, then the anomaly detector generates an anomaly detection event indicating detection of the known anomaly. The anomaly detector can also determine that a multivariate data slice is within an unknown cluster and generate an event indicating detection of an unknown anomaly.

Abstract: Business goals may be achieved using adaptive rewarding for the personalization of contents. In response to receiving user information, personalized contents for the user can be recommended using a reinforcement learning algorithm. In response to presenting the personalized content to the user, an action by the user selecting a particular content may be received. A reward value can be calculated for the action based on a reward function. The reward function can be based, at least in part, upon the action, the selected content, and/or the user. The reward function can be based upon one or more business goals, such as user engagement, monetization, and/or security. The calculated reward value can be provided to the reinforcement learning algorithm, which can be adapted based upon the reward value for future selection of personalized contents.

Abstract: Instead of attempting to scan all metric measurements of a distributed application, an anomaly detector intelligently selects instances of metrics from the universe of metric instances available for the distributed application to detect anomalies. Intelligent feature selection allows the anomaly detector to efficiently and reliably detect anomalies for a distributed application. The intelligent selection is guided by execution paths of transactions of the distributed application, and the execution paths are determined from a topology of the distributed application. The anomaly detector scans the incoming time-series data of the selected metric instances by transaction type and determines whether the scanned measurements across the selected metric instances form a pattern correlated with anomalous behavior.

Abstract: Systems and methods for application development include predicting a probable set of risks (e.g., security risks, financial risks, legal risks etc.) and risk mitigations for software development or deployment risk management. The system records user activity with respect to assigning risks and risk mitigations to application components. The system utilizes user inputs and characteristics of the modelled application as well as the user inputs and characteristics associated with past development and deployment of similar applications in order to predict a probable set of risks and/or risk mitigation actions.

Abstract: A multivariate path-based anomaly detection and prediction service (“anomaly detector”) can generate a prediction event for consumption by the APM manager that indicates a likelihood of an anomaly occurring based on path analysis of multivariate values after topology-based feature selection. To predict that a set of metrics will travel to a cluster that represents anomalous application behavior, the anomaly detector analyzes a set of multivariate date slices that are not within a cluster to determine whether dimensionally reduced representations of the set of multivariate data slices fit a path as described by a function.

Abstract: A system uses event correlation to identify components belonging to a same service or service domain. The system correlates events by generating covariance matrices or by performing sequence mining with temporal databases in order to discover event patterns that occur sequentially in a fixed time window. Components corresponding to the correlated events are identified as being part of a same service domain and can be indicated in a service domain data structure, such as a topology. The system utilizes the identified service domains during root cause analysis. The system can determine an anomalous event occurring a lowest layer component in a service domain as a root cause or can determine an anomalous event which occurs first in an identified event sequence of a service domain as a root cause. After identifying the root cause event, the system suppresses notifications of events occurring at other components in the service domain.

Abstract: A system uses event correlation to identify components belonging to a same service or service domain. The system correlates events by generating covariance matrices or by performing sequence mining with temporal databases in order to discover event patterns that occur sequentially in a fixed time window. Components corresponding to the correlated events are identified as being part of a same service domain and can be indicated in a service domain data structure, such as a topology. The system utilizes the identified service domains during root cause analysis. The system can determine an anomalous event occurring a lowest layer component in a service domain as a root cause or can determine an anomalous event which occurs first in an identified event sequence of a service domain as a root cause. After identifying the root cause event, the system suppresses notifications of events occurring at other components in the service domain.

Abstract: To adapt anomaly detection to changing canonical behavior and reduce the chances of feeding in feature value combinations that appear to be outliers but correspond to canonical behavior, multi-variate non-parametric density estimation is employed. An adaptive canonical behavior filter builds a sample dataset from observed time-series values of memory related metrics and then performs kernel density estimation on the sample dataset. With the resulting probability density function, the adaptive canonical behavior filter filters out subsequently observed time-series values of the memory related metrics that fall within a canonical behavior range that is specified/configured.

Abstract: A lightweight, non-intrusive memory anomaly detector has been designed that focuses on time sub-windows in the time-series data for selected memory related metrics that can efficiently be collected by probes or agents without being intrusive with the virtual machines (VMs) being monitored. In addition, the memory anomaly detector extracts features from those sub-windows of correlated features to present a smaller input vector to two classifiers: a fuzzy rule-based classifier and an artificial neural network. This allows the memory anomaly detector to be “lightweight” because it is less computationally expensive to run a smaller artificial neural network. The fuzzy rule-based classifier applies fuzzy rules to the input vector and provides classification labels, which are used to train an artificial neural network (ANN). After being trained, the trained ANN is refined with supervised feedback and presents its output of classification probabilities for application performance analysis.

Abstract: A multivariate clustering-based anomaly detector can generate an event for consumption by an APM manager that indicates detection of an anomaly based on multivariate clustering analysis after topology-based feature selection. The anomaly detector accumulates time-series data across a series of time instants to form a multivariate time-series data slice or multivariate data slice. The anomaly detector then performs multivariate clustering analysis with the multivariate data slice. The anomaly detector determines whether a multivariate data slice is within a cluster of multivariate data slices. If the multivariate data slice is within the cluster and the cluster is a known anomaly cluster, then the anomaly detector generates an anomaly detection event indicating detection of the known anomaly. The anomaly detector can also determine that a multivariate data slice is within an unknown cluster and generate an event indicating detection of an unknown anomaly.

Abstract: A multivariate path-based anomaly detection and prediction service (“anomaly detector”) can generate a prediction event for consumption by the APM manager that indicates a likelihood of an anomaly occurring based on path analysis of multivariate values after topology-based feature selection. To predict that a set of metrics will travel to a cluster that represents anomalous application behavior, the anomaly detector analyzes a set of multivariate date slices that are not within a cluster to determine whether dimensionally reduced representations of the set of multivariate data slices fit a path as described by a function.

Abstract: Instead of attempting to scan all metric measurements of a distributed application, an anomaly detector intelligently selects instances of metrics from the universe of metric instances available for the distributed application to detect anomalies. Intelligent feature selection allows the anomaly detector to efficiently and reliably detect anomalies for a distributed application. The intelligent selection is guided by execution paths of transactions of the distributed application, and the execution paths are determined from a topology of the distributed application. The anomaly detector scans the incoming time-series data of the selected metric instances by transaction type and determines whether the scanned measurements across the selected metric instances form a pattern correlated with anomalous behavior.

Abstract: A non-intrusive system which monitors a data center by detecting electromagnetic waves alleviates a resource burden on components and is inexpensive to deploy and scale within a data center. The system detects waves using omnidirectional antennas positioned throughout the data center, thus alleviating the need to physically attach directional antennas to components. The system performs a learning phase wherein representations of detected waves are mapped to occurrence of events within the data center. Once the learning phase is complete, operation of existing network monitoring tools, such as agents and probes, may cease, and the system may begin monitoring for events based on the detected waves. The system may also analyze wave data prior to the occurrence of events to identify event prediction indicators, e.g. distinctive wave values or patterns, which may be used to predict the occurrence of an event.

Abstract: A method for controlling and visualizing an internationalization process may include detecting a plurality of modules within source code, determining a respective status for each of the plurality of modules, formatting for display at a graphical user interface the respective status for each of the plurality of modules, parsing the plurality of modules to determine whether any particular module has one of a plurality of predetermined internationalization issues, and determining that a particular module has one of the plurality of predetermined internationalization issues. In response to determining that the particular module has one of the plurality of predetermined internationalization issues, the method may include updating the respective status of the particular module to a first status.

Abstract: A non-intrusive system which monitors a data center by detecting electromagnetic waves alleviates a resource burden on components and is inexpensive to deploy and scale within a data center. The system detects waves using omnidirectional antennas positioned throughout the data center, thus alleviating the need to physically attach directional antennas to components. The system performs a learning phase wherein representations of detected waves are mapped to occurrence of events within the data center. Once the learning phase is complete, operation of existing network monitoring tools, such as agents and probes, may cease, and the system may begin monitoring for events based on the detected waves. The system may also analyze wave data prior to the occurrence of events to identify event prediction indicators, e.g. distinctive wave values or patterns, which may be used to predict the occurrence of an event.

Abstract: Techniques are disclosed herein for implementing risk-adaptive system development. A model generator generates a displayed system model comprising displayed system components. A cycle generator determines a risk management cycle comprising a multiple risk management phases. The cycle generator generates a displayed risk management interface which includes multiple displayed risk management phases that are selected and sequenced within the displayed risk management interface based, at least in part, on the determined risk management cycle. The cycle generator further generates component objects that each correspond to a respective one within a set of the system components that have been selected for risk analysis. A display device displays each of the component objects within a respective one of the displayed risk management phases.

Abstract: A method includes performing by a processor of a server: receiving a requirement description of a service for a software application, generating a migration graph that comprises vertices representing candidate service providers for the service, respectively, identifying migration capability information between the vertices of the migration graph by connecting a portion of the vertices with edges, generating a centrality metric for each of the vertices of the migration graph based on the migration capability information and a number of edges terminating at the respective vertex, grading the candidate service providers based on one of the migration capability information and the centrality metric, and deploying the service using one of the candidate service providers responsive to grading the candidate service providers receiving an architecture description for a software application that identifies a plurality of generic services, receiving a requirement description for the software application that comprises

Abstract: A method includes performing operations as follows on a processor: receiving a big data dataset comprising new active data, receiving a request to predict a level of performance with respect to a performance parameter of a data processing system in analyzing the new active data, selecting a machine learning algorithm from a plurality of machine learning algorithms based on the performance parameter to obtain a selected machine learning algorithm, selecting a group of historical metadata from a plurality of groups of historical metadata of datasets that have previously been analyzed using the data processing system to provide a selected group of historical metadata, applying the selected machine learning algorithm to the selected group of historical metadata to generate a model of the selected group of historical metadata, obtaining metadata of the new active data, applying the model to the metadata of the new active data to generate a prediction of the level of performance with respect to the performance para

Abstract: A method includes performing operations as follows on a processor: receiving a description of a plurality of products, each product in the plurality of products having a local geographic feature that is configurable in one of a plurality of geographic configurations, receiving a description of priorities associated with the plurality of products, generating a risk based on the priorities, generating a risk mitigating treatment based on the risk, generating, for each of the plurality of products, a plurality of product localization scores corresponding to the plurality of geographic configurations, respectively, each of the plurality of product localization scores indicating a relative amount of the risk mitigated by the risk mitigating treatment with the local geographic feature having the respective geographic configuration, generating a plurality of global localization scores based on the plurality of product localization scores, each of the plurality of global localization scores corresponding to the plura