Abstract:
Disclosed herein are methods and structures for networks of mobile computers which efficiently synchronizes table data across the mobile computers while exhibiting great tolerance for temporary disconnects.
Abstract:
Disclosed are methods and structures that facilitate the synchronization of mobile devices and apps with cloud storage systems. Our disclosure, Simba, provides a unified synchronization mechanism for object and table data in the context of mobile clients. Advantageously, Simba provides application developers a single, API where object data is logically embedded with the table data. On the mobile device, Simba uses a specialized data layout to efficiently store both table data and object data. SQL-like queries are used to store and retrieve all data via a table abstraction. Simba also provides efficient synchronization by splitting object data into chunks which can be synchronized independently. Therefore, if only a small part of an object changes, the full object need not be synced. Advantageously only the changed chunks need be synched.
Abstract:
A method supports data communication in a mobile application by specifying in the mobile application a program intent and one or more course or fine-grained properties of data objects in terms of tolerance to delay and loss; selecting a transfer policy for a set of data objects based on the application intent; receiving and coalescing intents of one or more applications for object data for the one or more applications; crafting an aggregate transfer policy, and communicating data from one or more applications as an aggregate based on the aggregate transfer policy to programmatically incorporate and benefit from tolerance to delay in the transfer of data.
Abstract:
Systems and methods for recognizing a face are disclosed and includes receiving images of faces; generating feature vectors of the images; generating clusters of feature vectors each with a centroids or a cluster representative; for a query to search for a face, generating corresponding feature vectors for the face and comparing the feature vector with the centroids of all clusters; for clusters above a similarity threshold, comparing cluster members with the corresponding feature vector; and indicating as matching candidates for cluster members with similarity above a threshold.
Abstract:
Systems and methods for recognizing a face are disclosed and includes receiving images of faces; generating feature vectors of the images; generating clusters of feature vectors each with a centroids or a cluster representative; for a query to search for a face, generating corresponding feature vectors for the face and comparing the feature vector with the centroids of all clusters; for clusters above a similarity threshold, comparing cluster members with the corresponding feature vector; and indicating as matching candidates for cluster members with similarity above a threshold.
Abstract:
A method supports data communication in a mobile application by specifying in the mobile application a program intent and one or more course or fine-grained properties of data objects in terms of tolerance to delay and loss; selecting a transfer policy for a set of data objects based on the application intent; receiving and coalescing intents of one or more applications for object data for the one or more applications; crafting an aggregate transfer policy, and communicating data from one or more applications as an aggregate based on the aggregate transfer policy to programmatically incorporate and benefit from tolerance to delay in the transfer of data.