1373 results about "Large scale data" patented technology

The present disclosure relates generally to a data enrichment service that extracts, repairs, and enriches datasets, resulting in more precise entity resolution and correlation for purposes of subsequent indexing and clustering. As the data enrichment service can include a visual recommendation engine and language for performing large-scale data preparation, repair, and enrichment of heterogeneous datasets. This enables the user to select and see how the recommended enrichments (e.g., transformations and repairs) will affect the user's data and make adjustments as needed. The data enrichment service can receive feedback from users through a user interface and can filter recommendations based on the user feedback.

A computer program embodied in a computer readable medium in a mobile station MS includes instructions to display a first identifier, such as a song title, associated with a first media (music or video) file and a second identifier identified with a second media file. The first identifier indicates that the first media file is stored in the MS and the second identifier indicates that the file is not stored within the MS. Five such identifiers are disclosed, with functionality to up / download the media files one or more at a time from a PC or a networked server. Playlists and albums may be similarly indicated in their identifiers. The user is enabled to create and edit playlists on the MS without regard to what media files are stored on the MS. The MS may store file identifiers for the database maintained on the PC, a large-scale database maintained at a media service server, or a portion thereof. A mobile station is also described.

In a communication network comprising nodes and links between the nodes, a controller node disseminates link state information. A nodal routing table exists at each node comprising routes between pairs of nodes. The nodal routing table is either populated by the given node based on network information received from the controlling node or populated at the controlling node and received by the given node. Each node receives heartbeat signals from its neighbouring nodes. An unexpected delay between heartbeat signals may be perceived as a failure of a link. The perceived failure of that link is reported by the perceiving node to the controlling node. Upon receiving link failure information from a node, the controlling node may determine a subset of nodes in the network influenced by the link failure and indicate the link failure to the determined subset of influenced nodes.

A method and system for creating hypercomplex representations of data includes, in one exemplary embodiment, at least one set of training data with associated labels or desired response values, transforming the data and labels into hypercomplex values, methods for defining hypercomplex graphs of functions, training algorithms to minimize the cost of an error function over the parameters in the graph, and methods for reading hierarchical data representations from the resulting graph. Another exemplary embodiment learns hierarchical representations from unlabeled data. The method and system, in another exemplary embodiment, may be employed for biometric identity verification by combining multimodal data collected using many sensors, including, data, for example, such as anatomical characteristics, behavioral characteristics, demographic indicators, artificial characteristics. In other exemplary embodiments, the system and method may learn hypercomplex function approximations in one environment and transfer the learning to other target environments. Other exemplary applications of the hypercomplex deep learning framework include: image segmentation; image quality evaluation; image steganalysis; face recognition; event embedding in natural language processing; machine translation between languages; object recognition; medical applications such as breast cancer mass classification; multispectral imaging; audio processing; color image filtering; and clothing identification.

A computer implemented system for, and a computer implemented method of calculating indicators to reflect real-life interactions between people, among those are connectivity, social proximity, best paths and trust level. The system comprises a server connected via a communication link to users associated with communication and web based environments and to the web based environments and communication platforms themselves. The server is arranged to receive data relating to users, their profiles, connections and related data in the communication and web based environments as well as large scale data from these environments. The server comprises an application arranged to convert the data into a standard numeric format quantifying the connectivity, the social proximity, the trust level and other indicators to reflect real-life interactions between people. The computer implemented method collects information about the users and their connectivity, and analyses and maps the information as a virtual network spanning a plurality of the web based environments and communication platforms.

The invention provides a construction method of a GPU and CPU combined processor, which comprises the following steps: a CPU and a GPU are coupled to construct a combined processor, wherein the CPU is responsible for the general-purpose processing tasks of an operation system, system software, general-purpose application program, and the like which have complex instruction scheduling, circulation, branches and logical judgment; and the GPU is responsible for the highly-parallel calculating processing of large-scale data without logical relation; and the CPU and the GPU jointly finish the same large-scale parallel calculating application. In the GPU and CPU combined processor, a plurality of cores of CPU are communicated with each other by a memory bus and carry out calculation, and the cores of the GPU exchange data and are calculated by a uniform shared memory (a video memory on the GPU); the GPU and the CPU are connected by a high-speed serial bus and exchange calculating data by the memory of the CPU and the shared memory of the GPU; and the construction method can fully utilize the parallel processing capacity of the cores of the GPU and realize the rapid parallel calculation of enormous data volume.

The invention belongs to the technical field of multimedia information searching, and particularly relates to a commodity image category forecasting method based on an online shopping platform. The commodity image category forecasting method mainly involves six modules and comprises corresponding algorithms, namely training image obtaining, image characteristic extracting, irrelevant image filtering, image characteristic training, multilevel image classifying and relevant image selecting. According to the commodity image category forecasting method, based on real data obtained from the online shopping platform, commodity category information in images can be automatically analyzed through large-scale data training, shopping guide can be provided for a user, and therefore online shopping procedures can be simplified for the user, user experience is enhanced, and the commodity image category forecasting method has broad application value in the field of image searching.

The invention discloses a massive data continuous analysis system suitable for stream processing, which comprises a metadata management module, a query plan generation module, a data import task generation module, an increment processing module, an MR (MapReduce) message processing module and a database connection module, wherein the metadata management module is used for managing meta-information of data tables and databases; the query plan generation module is used for receiving a query request and generating an optimal query plan; the data import task generation module is used for receiving a data import request and generating a data import MR operation set; the increment processing module is used for incrementally committing data import and query operations to a Hadoop system in parallel; the MR message processing module is used for receiving a result of a Map or Reduce function of the Hadoop system and outputting the result to a Reduce end or the next operation; and the database connection module is used as an interface between the Hadoop system and the databases. According to the invention, the Hadoop system is used for organically organizing the databases in nodes and simultaneously executing data import and data query and a pipeline technology is used for improving the MR execution flow, so that the data query is executed in a continuous stream mode and the time of analyzing massive data is greatly shortened.

The invention relates to an image super-resolution method based on overcomplete dictionary learning and sparse representation. The method comprises the following steps of: extracting two overcomplete dictionaries (a low-resolution image block dictionary and a high-resolution image block dictionary) in a large-scale dataset and utilizing the two overcomplete dictionaries to realize super-resolution reconstruction of image sparse representation. Simultaneously, in order to further improve the super-resolution effect of color images, the invention also proposes UV chromaticity super-resolution reconstruction based on super-resolution luminance information. The image super-resolution method has wide application prospect in the fields of video monitoring, medical imaging, remote sensing image and the like.

A method to manage the secure download of data (102) to a very large number of network connected client devices (104, 110, 112, 114, 116, 118, 1120, 122) distributed within large geographic areas. The method provides adaptive data throttling and makes optimal use of network data bandwidth. The method can be advantageously applied for managing simultaneous data downloading to millions of network connected remote devices (104, 110, 112, 114, 116, 118, 1120, 122) via private networks, public networks and the Internet (124). A close-loop download regulation algorithm achieves the highest download capacity for the files without exceeding the maximum network capacity and without degrading the transactional and operational traffic performance between the server and the connected client devices.