Abstract: Methods and systems for data management are described, particularly for processing global queries. Each global query includes a user-defined query constraint value, such as laxity or query response time limit. The query receiving node maintains a copy of the previously updated data from all of its children node. The query receiving node first searches for the requested query data in its local data storage to minimize children node query. If any portion of the requested data in the local data storage fails to meet the query constraint value, then the child node from which the data came from is tasked with recursively executing the global query.

Abstract: Systems and methods for filtering time-varying data for filtering and extracting a predicted physiological signal. A method including: segmenting the time-varying data into temporal windows; using a trained filter machine learning model, predicting an error for each prediction of the physiological signal for each window of time-varying data, the filter machine learning model trained using physiological signal predictions based on training time-varying data and known values of the physiological signal for the training time-varying data; discarding each window of time-varying data when the predicted error for such window is greater than a threshold; and where the window of time-varying data is not discarded, outputting at least one of the window of time-varying data and the predicted error for each prediction of the physiological signal.

Abstract: Methods and systems for data management are described, particularly for processing global queries. Each global query includes a user-defined query constraint value, such as laxity or query response time limit. The query receiving node maintains a copy of the previously updated data from all of its children node. The query receiving node first searches for the requested query data in its local data storage to minimize children node query. If any portion of the requested data in the local data storage fails to meet the query constraint value, then the child node from which the data came from is tasked with recursively executing the global query.

Abstract: The present disclosure provides systems, methods, and computer-readable media for managing graphics processing unit (GPU) allocation for a virtual machine (VM). A first GPU driver, associated with a first GPU, is offloaded from an operating system (OS) of the VM. Then, the first GPU is deallocated from the VM. A second GPU is allocated to the VM, and a second GPU driver, associated with the second GPU, is loaded in the OS of the VM. To restore a GPU context from the first GPU within the second GPU, a GPU command log from the first GPU is replayed to the second GPU.

Abstract: A method includes creating a session token including a session identifier and a current node, capturing a set of queries in a cache from a client device at the current node associated with the session identification, receiving a request from the client for data access at a second node wherein the request includes the session token, transferring a subset of data records from the current node to the second node, wherein the subset of data records is selected based on the session identifier and the set of queries; and granting access to the client to the second node.

Abstract: Methods and apparatus are disclosed to provision virtual machine resources. An example method includes labeling a copy of memory associated with an established virtual machine with an execution status based on an architecture type associated with the copy, and constraining a fetch operation in response to a page fault to a labeled portion of the copy that matches an architecture type of a received processor instruction.

Abstract: Network presence of a computing device in a cloud computing network is maintained while power consumption of the computing device is reduced. When the computing device is determined to enter an idle state, at least some of the operations of the computing device running in a virtual machine environment are migrated to a server within the cloud computing network while maintaining connectivity of the computing device to the cloud computing network. When the computing device is determined to be in the idle state, the computing device is put into a sleep mode to reduce power consumption of the computing device. When the computing device is determined to be in an active state, the computing device is woken, and the migrated operations are returned from the server to the computing device. This reduces power consumption of the computing device while maintaining the network presence of the computing device in the cloud computing network.

Abstract: Methods and apparatus are disclosed to provision virtual machine resources. An example method includes labeling a copy of memory associated with an established virtual machine with an execution status based on an architecture type associated with the copy, and constraining a fetch operation in response to a page fault to a labeled portion of the copy that matches an architecture type of a received processor instruction.

Abstract: Network presence of a computing device in a cloud computing network is maintained while power consumption of the computing device is reduced. When the computing device is determined to enter an idle state, at least some of the operations of the computing device running in a virtual machine environment are migrated to a server within the cloud computing network while maintaining connectivity of the computing device to the cloud computing network. When the computing device is determined to be in the idle state, the computing device is put into a sleep mode to reduce power consumption of the computing device. When the computing device is determined to be in an active state, the computing device is woken, and the migrated operations are returned from the server to the computing device. This reduces power consumption of the computing device while maintaining the network presence of the computing device in the cloud computing network.

Abstract: A component-based adaptation system is provided in which the operation of an application or the data being used by the application is adapted according to an application-specific or a user-specific policy. Following a request for a document by an application, the requested document is retrieved and converted into an application-independent format. The data of the document is then supplied to the application according to a user-specific or application-specific policy. The application of the policy may result in a lower fidelity version or a subset of the data of the requested document being supplied to the application. The policy may also govern the updating of the data supplied to the application. The data supplied to the application may be updated following the occurrence of a tracked event in the application or according to a background policy governing the supply of updated data without reference to the user's operation of the application.

Abstract: A component-based adaptation system is provided in which the operation of an application or the data being used by the application is adapted according to an application-specific or a user-specific policy. Following a request for a document by an application, the requested document is retrieved and converted into an application-independent format. The data of the document is then supplied to the application according to a user-specific or application-specific policy. The application of the policy may result in a lower fidelity version or a subset of the data of the requested document being supplied to the application. The policy may also govern the updating of the data supplied to the application. The data supplied to the application may be updated following the occurrence of a tracked event in the application or according to a background policy governing the supply of updated data without reference to the user's operation of the application.