39763 results about "Big data" patented technology

Hashes are short summaries or signatures of data files which can be used to identify the file. Hashing multimedia content (audio, video, images) is difficult because the hash of original content and processed (e.g. compressed) content may differ significantly. The disclosed method generates robust hashes for multimedia content, for example, audio clips. The audio clip is divided (12) into successive (preferably overlapping) frames. For each frame, the frequency spectrum is divided (15) into bands. A robust property of each band (e.g. energy) is computed (16) and represented (17) by a respective hash bit. An audio clip is thus represented by a concatenation of binary hash words, one for each frame. To identify a possibly compressed audio signal, a block of hash words derived therefrom is matched by a computer (20) with a large database (21). Such matching strategies are also disclosed. In an advantageous embodiment, the extraction process also provides information (19) as to which of the hash bits are the least reliable. Flipping these bits considerably improves the speed and performance of the matching process.

A wireless wearable high data throughput (big data) brain machine interface apparatus is presented. An implanted recording and transmitting module collects neural data from a plurality of implanted electrodes and wirelessly transmits this over a short distance to a wearable (not implanted) receiving and forwarding module, which communicates the data over a wired communication to a mobile post processing device. The post processing device can send this neural data to an external display or computer enabled device for viewing and/or manipulation. High data throughput is supported by aggregating multiple groups of electrodes by multiple n-channel recording elements, which are multiplexed and then modulated into high frequency wireless communications to the wearable module. Embodiments include use of multiple radiators (multiple polarizations and/or spatially distributed), with beam alignment adjustment.

The invention discloses a threat early warning and monitoring system and method based on big data analysis and a deployment architecture. The system comprises a data acquisition system module, which is used for carrying out real-time data acquisition on original network traffic; a data storage system module, which is used for carrying out data merging and data cleaning on the data collected by the data acquisition system module, and then, carrying out storage management; a real-time threat intelligent analysis system module, which is used for carrying out deep analysis and mining on security data through data mining, text analysis, traffic analysis, full-text search engine and real-time processing, and identifying unknown security threats in real time by combining an intrusion detection module, a network abnormal behavior module and a device abnormal behavior module; and a situation awareness display system module, which is used for carrying out comprehensive display on security threat situations stereoscopically in real time through a data visualization tool library. The threat early warning and monitoring system and method based on big data analysis and the deployment architecture are used for network security threat situation awareness and deep analysis under a plurality of service scenarios, and realize comprehensive abilities from attack early warning, attack identification to analysis and evidence obtaining.

Techniques for managing big data include retrieval using per-subject dictionaries having multiple levels of sub-classification hierarchy within the subject. Entries may include subject-determining-power (SDP) scores that provide an indication of the descriptive power of the entry term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different SDP scores in each of the dictionaries. A retrieval request for one or more documents containing search terms descriptive of the one or more documents can be processed by identifying a set of candidate documents tagged with subjects, i.e., identifiers of per-subject dictionaries having entries corresponding to a search term, then using affinity values to adjust the aggregate score for the terms in the dictionaries. Documents are then selected for best match to the subject based on the adjusted scores. Alternatively, the adjustment may be performed after selecting the documents by re-ordering them according to adjusted scores.

The invention relates to a method and a system for storing mass data of the Internet of Things (IoT). The system comprises a plurality of data receiving nodes, a master node server and a database cluster. The method comprises the following steps of: (1) carrying out preprocessing on IoT data, and putting the preprocessed data in the database cluster consisting of a master node, slave nodes and the data receiving nodes; (2) creating sample records, which take sample elements as storage units, on the master node according to the static and dynamic information of the data in the database cluster; (3) after the sample records are encapsulated, sending the encapsulated sample records to all the slave nodes by the master node so as to be subjected to fragmentation processing and/or separated storage; and (4) after the slave nodes complete storage, uploading results to the master node, and updating the data in the database cluster by the master node, thereby completing storage. According to the method and the system, the cluster is extended by fully utilizing the existing storage technologies so as to store the mass data, the IoT data are divided into lightweight data and multimedia data, and particularly, a fragmentation strategy is adopted aiming at a large-scale amount of data, so that the time expenditure caused by the extension of a storage space is avoided.

Embodiments of the invention provide systems and methods for managing and processing large amounts of complex and high-velocity data by capturing and extracting high-value data from low value data using big data and related technologies. Illustrative database systems described herein may collect and process data while extracting or generating high-value data. The high-value data may be handled by databases providing functions such as multi-temporality, provenance, flashback, and registered queries. In some examples, computing models and system may be implemented to combine knowledge and process management aspects with the near real-time data processing frameworks in a data-driven situation aware computing system.

A smart memory computing system that uses smart memory for massive data storage as well as for massive parallel execution is disclosed. The data stored in the smart memory can be accessed just like the conventional main memory, but the smart memory also has many execution units to process data in situ. The smart memory computing system offers improved performance and reduced costs for those programs having massive data-level parallelism. This smart memory computing system is able to take advantage of data-level parallelism to improve execution speed by, for example, use of inventive aspects such as algorithm mapping, compiler techniques, architecture features, and specialized instruction sets.

he invention discloses a medical information extraction system and method based on depth learning and distributed semantic features. The system is composed of a pretreatment module, a linguistic-model-based word vector training module, a massive medical knowledge base reinforced learning module, and a depth-artificial-neural-network-based medical term entity identification module. With a depth learning method, generation of the probability of a linguistic model is used as an optimization objective; and a primary word vector is trained by using medical text big data; on the basis of the massive medical knowledge base, a second depth artificial neural network is trained, and the massive knowledge base is combined to the feature leaning process of depth learning based on depth reinforced learning, so that distributed semantic features for the medical field are obtained; and then Chinese medical term entity identification is carried out by using the depth learning method based on the optimized statement-level maximum likelihood probability. Therefore, the word vector is generated by using lots of unmarked linguistic data, so that the tedious feature selection and optimization adjustment process during medical natural language process can be avoided.

The present invention is a complete solution for mission critical facilities to create a safe environment and energy efficient CRAC system. The invented system collects mission critical facility volume environmental and energy data in real time, and uses big data analytics to provide adaptive group control to CRACs. The data is visualized in 2D and 3D graphs, dashboard, history curves and reports locally and through the cloud. The present invention also provides user management to allow authorized users to view and control the racks/servers owned or rented by these users. The present invention is comprised of four companion patents: 1) A Battery-powered Wireless Long Life Temperature and Humidity Sensor Module, 2) An Intelligent Multi-channel Wireless Data Acquisition Gateway, 3) Mission critical facility Three Dimension Temperature Contour Generator, and 4) An Adaptive Automatic CRAC Master Control Method.

The Field Programmable Instrument Controller (FPIC) is a stand-alone low to high performance, clocked or unclocked multi-processor that operates as a microcontroller with versatile interface and operating options. The FPIC can also be used as a concurrent processor for a microcontroller or other processor. A tightly coupled Multiple Chip Module design incorporates non-volatile memories, a large field programmable gate array (FPGA), field programmable high precision analog to digital converters, field programmable digital to analog signal generators, and multiple ports of external mass data storage and control processors. The FPIC has an inherently open architecture with in-situ reprogrammability and state preservation capability for discontinuous operations. It is designed to operate in multiple roles, including but not limited to, a high speed parallel digital signal processing; co-processor for precision control feedback during analog or hybrid computing; high speed monitoring for condition based maintenance; and distributed real time process control. The FPIC is characterized by low power with small size and weight.

Mass data (the contents of arrays, large data structures, linked data structures and similar data structures stored in memory) are common targets for attack. The invention presents a method and system of protecting mass data by mapping virtual addresses onto randomly or pseudo-randomly selected actual addresses. This mapping distributes data values throughout the memory so an attacker cannot locate the data he is seeking, or identify patterns which might allow him to obtain information about his target (such as how the software operates, encryption keys, biometric data or passwords stored therein, or algorithms it uses). Additional layers of protection are described, as well as efficient techniques for generating the necessary transforms to perform the invention.

An intelligent instrumented air cleaner comprises a multi-functional sensor, a central processor, a cloud server, a wireless communication module, a purification device, an alarm device, a motor driving mechanism and a humidifier. According to the intelligent instrumented air cleaner, updating and exchange of memory data are realized through the connection between the wireless communication module and a cloud server of the Internet of Things, the air cleaner is controlled through the connection between a mobile device and the cloud server so that a remote controller can be replaced, remote network control is also realized, the cloud server also has an analysis function and a processing result advice providing function and sends processed data to users for reference, the users can set the working state of the air cleaner by themselves and define working strategies by themselves, the air cleaner saves the strategies automatically and performs actions according to the strategies, and self-selection of self-learning setting optimization is conducted on the data in a memory through big data analysis and processing. By the adoption of the intelligent instrumented air cleaner, an intelligent instrumented household net and an intelligent instrumented office net are realized in the true sense, air is purified, safe and reliable living and working environments are provided, useless consumption of electric energy of a traditional air cleaner is avoided, energy consumption is reduced, using pleasure of a user is improved, the parameters of the environment where the user is located are provided for the user in time, and reliable parameters and solutions are provided for environment change handling.

The invention discloses a network safety situation awareness early-warning method and system based big data, and the method comprises the steps: collecting intelligence information from the Internet, non-governmental organizations, governmental agencies and interiors of companies; obtaining log and network flow generated by safety equipment, network equipment, a host and other safety protection systems, carrying out the real-time preprocessing of the collected original data, carrying out the standardization of the data after preprocessing, and converting the data into safety data with the unified standard; carrying out scene modeling according to different attack behaviors, carrying out correlation analysis through combining with the intelligence information and the safety data, and generating early-warning information; carrying out the check processing of the early-warning information, and carrying out the visualized display of the early-warning information after check. The method can achieve the complete sensing of a safety situation, the early warning of safety threats and the capability of timely handling and responding of a safety event, and improves the overall safety protection capability of a power system.

The invention discloses a cloud computing and big data-based parking method and system. The method comprises the steps: a cloud server receives parking lot information query instruction of an intelligent terminal, wherein the parking lot information query instruction comprises destination data information of the intelligent terminal; according to the received parking lot information query instruction, information data collected through a plurality of parking lot terminals is searched for corresponding parking lot information, and query results are sent to the intelligent terminal. According to the cloud computing and big data-based parking method and system, a car owner can quickly and comprehensively learn about the information of charge of a parking lot nearby the destination, real-time available parking spaces and the like; by comprehensively analyzing factors, such as weather, road conditions, major events which affect the traffic nearby the destination, and parking lot selection of car owners who frequently park nearby, the optimal parking lot and path are provided for the user, thus, the car owner can quickly make a wise selection even in a strange place or environment, and the parking efficiency can be improved.

Massive amounts of multimedia data are stored in databases supporting web pages and servers, including text, graphics, video and audio. Searching and finding matching multimedia images can be time and computationally intensive. A method for storing and retrieving image data includes computing a descriptor, such an a Fourier-Mellin Transform (FMT), corresponding to a multidimensional space indicative of each of the stored images and organizing each of the descriptors according to a set similarity metric. The set similarity metric is based on Locality-Sensitive Hashing (LSH), and orders descriptors near to other descriptors in the database. The set similarity metric employs set theory which allows distance between descriptors to be computed consistent with LSH. A target image for which a match is sought is then received, and a descriptor indicative of the target image is computed. The database is referenced, or mapped, to determine close matches in the database. Mapping includes selecting a candidate match descriptor from among the descriptors in the database and employing a distance metric derived from the similarity metric to determine if the candidate match descriptor is a match to the target descriptor.

The invention discloses a multi-task layered image retrieval method based on a depth self-coding convolution neural network. The method is characterized by mainly comprising a multi-task end-to-end convolution neural network for deep learning and training recognition, a rapid visual segmentation detection and positioning method of a region-of-interest secondary screening module based on an RPN network, a coarse search of a full-graph sparse hash code, an area sensing semantic feature and matrix h accurate comparison and search based on the maximum response, and a region-of-interest selectivitycomparison algorithm. According to the method, the end-to-end training can be achieved, the interest region with higher quality can be automatically selected, the automation degree and the intelligent level of search by images can be effectively improved, and the image retrieval requirements of the big data age can be met by using little storage space at a high search speed.

The invention discloses a method and a system for improving the large data volume query performance and belongs to the technical field of large data volume query. The method comprises A, loading data in a disk database into distributed caches in a cache ID-entity data key value pair mode, and storing the cache ID and key information of the entity data in a cache ID table of a memory database simultaneously; B, querying the cache ID table according to a query request when the query request sent by a client is obtained to selecting an ID set meeting the query request; C, obtaining the entity data from corresponding distributed caches according to the cache ID set and returning the entity data to the client. By means o the system and the method, loads of the disk database can be effectively reduced, and the big data query performance is improved.