Abstract: A method and system are described. The method includes determining, in a development phase of a software service, whether the software service complies with a first policy in response to a request. The method also monitors, in at least one of a testing phase or a production phase of the software service, whether operation of the software service complies with a second policy. Based on the determining and the monitoring, an indication of a service vulnerability is generated in response to the software service failing to comply with the first policy in the development phase or failing to comply with the second policy in the at least one of the testing phase or the production phase.

Abstract: Systems, methods, and non-transitory computer-readable media can be configured to provide machine learning data to an edge computing device based on information associated with the edge computing device. A change to the information associated with the edge computing device is determined. One or more machine learning operations can be managed on the edge computing device based at least in part on the change to the information associated with the edge computing device.

Abstract: One embodiment is directed to training a machine-learning model using sample data by partitioning the machine-learning model into sub-portions and training the sub-portions in different nodes. Another embodiment is directed to training machine-learning models using features determined based on different data layers. Another embodiment is directed to determining a validity of a request for accessing data based on the processing results of policy modules. Another embodiment is directed to a policy engine including a policy knowledge module and a policy intelligence module. Another embodiment is directed to a smart data warehouse using natural language processing and nested heterogeneous graphs to visualize results.

Abstract: In one embodiment, a computing system may receive query information associated with a machine-learning model. The system may access a knowledge graph that defines relationships between a number of machine-learning models and a number of features of the machine-learning models. The system may determine, based on the knowledge graph and the query information, one or more correlation metrics indicating correlations between the machine-learning model and one or more features of the features in the knowledge graph. The system may determine one or more recommended features for the machine-learning model based on the one or more correlation metrics and the one or more features.

Abstract: An apparatus and method are provided for review-based machine learning. Included are a non-transitory memory storing instructions and one or more processors in communication with the non-transitory memory. The one or more processors execute the instructions to receive first data, generate a plurality of first features based on the first data, and identify a first set of labels for the first data. A first model is trained using the first features and the first set of labels. The first model is reviewed to generate a second model, by receiving a second set of labels for the first data, and reusing the first features with the second set of labels in connection with training the second model.

Abstract: An apparatus and method are provided for a managed knowledge network platform (KNP). Model dissimilarity values for model pairs are obtained, each model pair including a first model of a plurality of models in a KNP and a different model in the plurality of models. Path lengths between a first model node of a plurality of model nodes in the KNP and each one of other model nodes are computed, where the first model node represents the first model and the first model node is connected to a first user node of a plurality of user nodes representing users of the KNP. At least one of the different models is selected based on the model dissimilarity values and the path lengths. A recommendation that includes the at least one model is generated for a first user represented by the first user node.

Abstract: Disclosed herein is a system and method for generating an enhanced index for documents contained in a knowledge base. Documents or configuration snapshots are tokenized and then passed through a number of filters. The filters modify the token stream to generate an enhanced token stream for the document by removing words from the stream and adding domain level knowledge to the tokens in the stream. The token stream is then added to the index for the document so that searches can be completed against the knowledge base when searches are generated from configuration snapshots or from inputted search queries.

Abstract: A performance-based storage service level agreement (SLA) can be established that specifies one or more storage performance parameters. A storage allocation process can include receiving a request for a storage SLA that specifies one or more storage performance parameters, determining, for a virtual machine (VM) and based at least in part on the one or more storage performance parameters in the storage SLA: (i) a storage location among a set of candidate storage locations, and (ii) an amount of storage to allocate. The amount of storage can then be allocated at the storage location for the VM to use in making storage requests. Runtime enforcement of the storage SLA can utilize a scheduling mechanism that buffers individual storage requests into different queues that are used for meeting one or more storage performance requirements specified in storage SLA.

Abstract: A system, computer readable medium, and method are provided for a resource management in a cloud architecture. The method includes the steps of collecting a first time stamped data (TSD), and a second TSD, and generating a prediction model based on the first TSD and the second TSD. The method further includes collecting a third TSD, and predicting a fourth TSD based on the prediction model and the third TSD. With more data are obtained via the prediction, the resource management is more efficient and accurate.

Abstract: A platform for obtaining digital items provides consumers access to digital item from multiple sources such as other consumers, libraries, rental services, and stores. The platform may be implemented as a cloud-based system accessible over the Internet. The platform transforms digital items into different formats when needed for compatibility with a computing device of the end consumer and transfers digital rights management (DRM) restrictions across different DRM schema. Consumers may store personal information in association with a digital item for later re-association with other copies of the same digital item. Consumers may also comment on a digital item and the platform may make those comments available to other consumers that have accessed the same digital item. The platform may also mediate instant messaging between consumers that are both associated with the same digital item. Computing devices of the consumers may receive widgets from the platform that provide additional functionality.

Abstract: A single sign-on system accepts master credentials from a user device and/or application, and automatically signs on to supported services using account credentials corresponding to those services. If the user has not created an account used by a particular device or application, the system can automatically interact with the account service to create the account. Similarly, if the device or application that relies on the account has not already been registered with the account, the system automatically interacts with the account to register the device or account.

Abstract: A system, computer readable medium, and method are provided for a resource management in a cloud architecture. The method includes the steps of collecting a first time stamped data (TSD), and a second TSD, and generating a prediction model based on the first TSD and the second TSD. The method further includes collecting a third TSD, and predicting a fourth TSD based on the prediction model and the third TSD. With more data are obtained via the prediction, the resource management is more efficient and accurate.

Abstract: User devices such as electronic book readers may be configured to provide a particular device experience. This device experience may comprise settings of a user interface, states of one or more applications or modules executing on the device, timing of events, and so forth. Settings, state information, timing, and so forth describing the device experience may be stored as an experience template. This experience template may be distributed to other user devices, allowing those other devices to experience the same or similar experiences.

Abstract: An apparatus and method are provided for a managed knowledge network platform (KNP). Model dissimilarity values for model pairs are obtained, each model pair including a first model of a plurality of models in a KNP and a different model in the plurality of models. Path lengths between a first model node of a plurality of model nodes in the KNP and each one of other model nodes are computed, where the first model node represents the first model and the first model node is connected to a first user node of a plurality of user nodes representing users of the KNP. At least one of the different models is selected based on the model dissimilarity values and the path lengths. A recommendation that includes the at least one model is generated for a first user represented by the first user node.

Abstract: An apparatus and method are provided for review-based machine learning. Included are a non-transitory memory storing instructions and one or more processors in communication with the non-transitory memory. The one or more processors execute the instructions to receive first data, generate a plurality of first features based on the first data, and identify a first set of labels for the first data. A first model is trained using the first features and the first set of labels. The first model is reviewed to generate a second model, by receiving a second set of labels for the first data, and reusing the first features with the second set of labels in connection with training the second model.

Abstract: A mobile device, computer readable medium, and method are provided for allocating resources within a cloud. The method includes the steps of collecting profile data from a plurality of resource agents and allocating a number of resource units to each resource agent in the plurality of resource agents based on the collected profile data. The allocating may be performed via a resource manager in communication with the plurality of resource agents.

Abstract: A mobile device, computer readable medium, and method are provided for allocating resources within a cloud. The method includes the steps of receiving metrics data associated with one or more tasks, training one or more models based on the metrics data to predict scores for tasks executed with a particular number of resource units, receiving a request that specifies a first task for processing a dataset, determining an optimal number of resource units to allocate to the first task based on predicted scores output by a first model, and allocating the optimal number of resource units to a resource agent in the cloud to manage the execution of the first task. The metrics data, which is collected by a plurality of cognitive agents, is received by a cognitive engine service in communication with the plurality of cognitive agents deployed in the cloud.

Abstract: A method implemented in a cloud-based data system includes a central controller receiving time-stamped reports from a plurality of agents including a server status and a server resource usage, calculating a number of active servers and a sum of resource usage on each server per interval based on each time-stamped report, generating a prediction model based on data results generated from calculating the number of active servers and the sum of resource usage per interval, predicting a number of servers needed in the cloud-based system based on the prediction model, generating a forecasting model to forecast an amount of resource usage at a future date, based on time series data associated with calculating the sum of resource usage over multiple intervals, and using the prediction model to predict whether a different number of servers is needed at the future date based on the forecasted amount of resource usage.

Abstract: A performance-based storage service level agreement (SLA) can be established that specifies one or more storage performance parameters. A storage allocation process can include receiving a request for a storage SLA that specifies one or more storage performance parameters, determining, for a virtual machine (VM) and based at least in part on the one or more storage performance parameters in the storage SLA: (i) a storage location among a set of candidate storage locations, and (ii) an amount of storage to allocate. The amount of storage can then be allocated at the storage location for the VM to use in making storage requests. Runtime enforcement of the storage SLA can utilize a scheduling mechanism that buffers individual storage requests into different queues that are used for meeting one or more storage performance requirements specified in storage SLA.

Abstract: Disclosed herein is a system and method for searching or processing queries for searching for documents contained in a domain specific knowledge base. The system takes a query and generates from the query a modified version of the query by passing the query through one or more filters in a query processor. The query processor adds or removes terms from the query. The query processor can add or recognize that two words that appear to be separate words actually identify a specific software entity or can determine that a number appearing in a query is not just a number but refers to a specific version or a number relevant to the specific problem.