Abstract: Methods and systems for secure data analysis include determining that analysis provider access rules and data provider access rules are compatible. Analysis software is received from an analysis provider and a dataset is received from a data provider. The analysis software is executed on the dataset to generate an analysis output, with access to data in the dataset being constrained by the analysis provider access rules and the data provider access rules. An output of the analysis is sent to the analysis provider.

Abstract: A computer-implemented method is presented for automatically generating alerting rules. The method includes identifying, via offline analytics, abnormal patterns and normal patterns from history logs based on machine learning, statistical analysis and deep learning, the history logs stored in a history log database, automatically generating the alerting rules based on the identified abnormal and normal patterns, and transmitting the alerting rules to an alerting engine for evaluation. The method further includes receiving a plurality of online log messages from a plurality of computing devices connected to a network, augmenting the plurality of online log messages, and extracting information from the plurality of augmented online log messages to be provided to the alerting engine, the alerting engine configured to approve and enforce the alerting rules automatically generated by the offline analytics processing.

Abstract: Methods and systems for deploying images to computing systems include predicting an environment for a plurality of processing nodes. Image deployment to the plurality of processing nodes is simulated to determine a subset of the plurality of processing nodes for deployment. One or more images is pre-loaded to the subset of the plurality of processing nodes in advance of a deployment time.

Abstract: Performing an operation comprising transforming an input dataset to a predefined format, extracting, from the transformed dataset, a plurality of features describing the transformed dataset, and generating, by a machine learning (ML) algorithm executing on a processor and based on an ML model, a plurality of rules for modifying the transformed dataset to conform with a first data model.

Abstract: Embodiments generally relate transforming data for a target schema. In some embodiments, a method includes receiving input data, where the input data includes a plurality of segments, and where the segments include a plurality of source fields containing target data. The method further includes characterizing the input data based at least in part on a plurality of predetermined metrics, where the predetermined metrics determine a structure of the input data. The method further includes mapping the target data in the source fields of the segments to a plurality of target fields of a target schema based at least in part on the characterizing. The method further includes populating the target fields of the target schema with the target data from the source fields based at least in part on the mapping.

Abstract: Embodiments of the present invention relate to a computer-implemented method, system and computer program product facilitating launching a middleware-based application. In some embodiments, a computer-implemented method includes: preparing, by a device operatively coupled to one or more processing units, an execution environment for middleware to be included in a container hosted on a machine; detecting, by the device, a request to schedule an application to be executed in the machine using the middleware; and in response to the request being detected, launching, by the device, the application within the container based on the prepared execution environment.

Abstract: A log augmentation system is provided for augmenting log management in a distributed micro-services environment. The log augmentation system includes a trigger device for triggering an augmentation process responsive to micro-service related events and a context of the micro-service related events. The log augmentation system further includes an augmentation rules database with rule management components for determining an augment direction, an augmentation target, and an augmentation correlation relating to the distributed micro-services. The log augmentation system also includes a coordinator for coordinating the augmentation process in the distributed micro-service using a rule-based decision approach. The log augmentation system additionally includes a labeler for applying a set of labels to augment log entries responsive to instructions from the coordinator derived from results of the rule-based decision approach used by the coordinator.

Abstract: Methods and systems for managing persistent volumes include mounting a distributed filesystem, located outside the processing node, to a local mount point on the processing node. Access to the local mount point is given to a container on the processing node.

Abstract: A service running on a server for developing software collaboratively. The service includes accessing at least one repository of code for software applications. A code tree structure is extracted from the repository which represents a plurality of preexisting pipeline requirements to be used with a tree kernel similarity algorithm. At least one development repository of code is accessed. A code tree structure is extracted from the development repository of code which represents a new pipeline requirement to be used with a tree kernel similarity algorithm. A tree kernel similarity algorithm is used that includes a specified similarity function to create feature map between the new pipeline requirements with the preexisting pipeline requirements. One or more features of the new pipe line requirements are clustered. Different requirements are extracted to different definitions based upon the features that have been clustered. A preexisting pipeline requirement is selected for a highest similarity.

Abstract: Methods and systems for managing persistent volumes include receiving a request from a container on a processing node to access a local mount point. A distributed filesystem located outside the processing node is mounted to a local mount point. Access to the local mount point is provided to the container.

Abstract: Computer technology facilitating optimization of a container image is provided. In one example, a computer-implemented method includes: analyzing a source container image to obtain a content of the source container image and building an initial target container image using a set of base image layers based on the content of the source container image. The computer-implemented method also includes determining content difference between the initial target container image and the source container image, and building a final target container image based on the initial target container image and the content difference.

Abstract: A computer-implemented method is presented for automatically generating alerting rules. The method includes identifying, via offline analytics, abnormal patterns and normal patterns from history logs based on machine learning, statistical analysis and deep learning, the history logs stored in a history log database, automatically generating the alerting rules based on the identified abnormal and normal patterns, and transmitting the alerting rules to an alerting engine for evaluation. The method further includes receiving a plurality of online log messages from a plurality of computing devices connected to a network, augmenting the plurality of online log messages, and extracting information from the plurality of augmented online log messages to be provided to the alerting engine, the alerting engine configured to approve and enforce the alerting rules automatically generated by the offline analytics processing.

Abstract: Embodiments of the present invention relate to a computer-implemented method, system and computer program product facilitating launching a middleware-based application. In some embodiments, a computer-implemented method includes: preparing, by a device operatively coupled to one or more processing units, an execution environment for middleware to be included in a container hosted on a machine; detecting, by the device, a request to schedule an application to be executed in the machine using the middleware; and in response to the request being detected, launching, by the device, the application within the container based on the prepared execution environment.

Abstract: A computer-implemented method, computer system, and computer program product that facilitates optimization of container image are disclosed. A computer-implemented method includes: analyzing, by the one or more processing units, a source container image to obtain a content of the source container image; building, by the one or more processing units, an initial target container image using a set of base image layers based on the content of the source container image; determining, by the one or more processing units, a content difference between the initial target container image and the source container image; and building, by the one or more processing units, a final target container image based on the initial target container image and the content difference.

Abstract: Methods and systems for managing persistent volumes include receiving a request from a container on a processing node to access a local mount point. A distributed filesystem located outside the processing node is mounted to a local mount point. Access to the local mount point is provided to the container.

Abstract: Methods and systems for deploying images to computing systems include predicting an environment for a plurality of processing nodes. Image deployment to the plurality of processing nodes is simulated to determine a subset of the plurality of processing nodes for deployment. One or more images is pre-loaded to the subset of the plurality of processing nodes in advance of a deployment time.

Abstract: Methods and systems for secure data analysis include determining that analysis provider access rules and data provider access rules are compatible. Analysis software is received from an analysis provider and a dataset is received from a data provider. The analysis software is executed on the dataset to generate an analysis output, with access to data in the dataset being constrained by the analysis provider access rules and the data provider access rules. An output of the analysis is sent to the analysis provider.

Abstract: A log augmentation system is provided for augmenting log management in a distributed micro-services environment. The log augmentation system includes a trigger device for triggering an augmentation process responsive to micro-service related events and a context of the micro-service related events. The log augmentation system further includes an augmentation rules database with rule management components for determining an augment direction, an augmentation target, and an augmentation correlation relating to the distributed micro-services. The log augmentation system also includes a coordinator for coordinating the augmentation process in the distributed micro-service using a rule-based decision approach. The log augmentation system additionally includes a labeler for applying a set of labels to augment log entries responsive to instructions from the coordinator derived from results of the rule-based decision approach used by the coordinator.

Abstract: A computer-implemented method for data leakage protection is disclosed. A monitoring template corresponding to the cloud application is selected based upon communication between a user and a cloud application and from a plurality of monitoring templates. A monitor is generated using the selected monitoring template. Identifying information of content shared between the user and the cloud application is obtained using the generated monitor. Data about the shared content for security analysis is obtained according to the identifying information of the shared content.

Abstract: Embodiments generally relate transforming data for a target schema. In some embodiments, a method includes receiving input data, where the input data includes a plurality of segments, and where the segments include a plurality of source fields containing target data. The method further includes characterizing the input data based at least in part on a plurality of predetermined metrics, where the predetermined metrics determine a structure of the input data. The method further includes mapping the target data in the source fields of the segments to a plurality of target fields of a target schema based at least in part on the characterizing. The method further includes populating the target fields of the target schema with the target data from the source fields based at least in part on the mapping.